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Using Load Reduction Instruction (LRI)to boost motivation and engagementAndrew J. MartinSchool of Education, University of New South Wales, Australia

35th Vernon-Wall Lecture

ISSN: 0263-5895ISBN: 978-1-85433-744-3

First published in Great Britain 2016 by the British Psychological Society.

Copyright © The British Psychological Society. All rights reserved.

ISSN: 0263-5895 ISBN: 978-1-85433-744-3

Produced by the Psychology of Education Section of the British Psychological Society.

AcknowledgmentsThanks are extended to John Sweller, Slava Kalyuga, Paul Ginns, PaulEvans, and Rebecca Collie for their comments as this review developed.

Further informationRequests about this investigation can be made to Andrew J. Martin, Schoolof Education, University of New South Wales, NSW 2052, AUSTRALIA.Email: [email protected]

To cite this monograph Martin, A.J. (2016). Using Load Reduction Instruction (LRI) to boost motivationand engagement. Leicester, UK: British Psychological Society.

Executive Summary

Executive Summary The escalation in academic demands through school underscores the need to approach instruction in ways that appropriately manages the burden on learners where possible and feasible. Cognitive psychology has been informative in identifying instructional approaches that are directly aimed at managing the cognitive load on students to better help them learn and achieve. Load reduction instruction (LRI) is an umbrella term referring to instructional approaches that seek to reduce and/or manage cognitive load in order to optimize students’ learning and achievement. LRI encompasses explicit and direct instruction. At the appropriate point in learning, LRI also involves less structured approaches such as guided discovery-, problem-, and inquiry-based learning. A major tenet of LRI is that students are at first novices with respect to academic skill and subject matter and that a structured and somewhat directional approach to instruction that reduces cognitive load is important for learning and achievement in the early stages of learning. Then, as core skill, knowledge, fluency and automaticity develop, LRI emphasizes the centrality of guided discovery-, problem-, and inquiry-based learning. LRI is based on five principles at key points in the learning process:

(1) Reducing the difficulty of a task during initial learning; (2) Instructional support and scaffolding through the task; (3) Ample structured practice;

(4) Appropriate provision of instructional feedback; and (5) Independent practice, supported autonomy, and guided discovery learning.

Although numerous frameworks have recognized the roles of explicit or discovery approaches, LRI is distinct in that its emphasis is on reducing or managing the cognitive burden on students as they learn and that this can comprise both explicit and discovery approaches. LRI is thus termed, framed, and developed deliberately to indicate why we engage its various instructional elements - namely, to deliver instruction and instructional support so as to appropriately reduce or manage the cognitive burden on the learner. Essentially, LRI helps build the content of long-term memory and develops a level of fluency and automaticity that frees up working memory to apply to a given task or problem. Importantly, fluency and automaticity also have implications for students’ motivation and engagement. However, relatively little attention has been given to the role of LRI in students’ academic motivation and engagement. The present review thus considers the relationship between motivation, engagement, and LRI. The Motivation and Engagement Wheel is the framework used to explore LRI and its motivation and engagement links. The Motivation and Engagement Wheel comprises four overarching dimensions of motivation and engagement, each comprising specific motivation and engagement factors:

• Positive Motivation: self-efficacy, valuing, mastery orientation;

35th Vernon-Wall Lecture

Executive Summary

• Positive Engagement: planning and monitoring behavior, task management, persistence;

• Negative Motivation: anxiety, failure avoidance (fear of failure), uncertain (low) control;

• Negative Engagement: self-handicapping, disengagement.

The review examines each of these motivation and engagement factors and explores the extent to which specific approaches and strategies under LRI can address them. In so doing, the review complements the large body of work into LRI and its achievement effects with closer consideration of its potential yields for students’ motivation and engagement. In addressing these issues, the review is organized into five parts. Part 1. Load Reduction Instruction: (i) definition and description of LRI, (ii) a review of human cognitive architecture as relevant to LRI, (iii) consideration of fluency and automaticity, (iv) a summary of LRI effects on achievement, (v) consideration of LRI for diverse learners and subject areas, and (vi) identification of specific load reduction instructional elements. Part 2. Motivation and Engagement: (vii) definition and description of motivation and engagement and (viii) a motivation and engagement framework for considering LRI. Part 3. Load Reduction Instruction, Motivation, and Engagement: (ix) LRI approaches for specific motivation and engagement dimensions. Part 4. Load Reduction Instruction and the Broader Process of Learning: (x) the role of guided discovery learning and (xi) understanding the optimal learning sequence. Part 5. Looking Forward: (xii) Opportunities for future research in LRI, explicit instruction, motivation, and engagement.

Taken together, it is important to recognize the motivating and engaging properties of clear, structured and well guided instruction, and the implications this has for students’ learning and achievement outcomes. Load Reduction Instruction (LRI) is proposed herein as an effective pedagogical means of supporting students’ motivation, engagement, learning, and achievement at school - and beyond.

35th Vernon-Wall Lecture

2 35th Vernon-Wall Lecture

THE VERNON-WALL LECTURES havebeen highlights of the annual conferencesof the Psychology of Education Section of

the British Psychological Society (BPS) formore than 30 years. The current Sectioncommittee believes that in future the member-ship would welcome a foreword to eachpublished lecture with information about theorigin of the series and the lives of the two menit honours. I am happy to have been invited tohelp since I was personally involved in estab-lishing the series, both men were knownpersonally to me, and both preceded me in theUniversity of London Chair in EducationalPsychology, held at the Institute of Education. To account for the origin I go back to

1980 when the Section was simply named theEducation Section of the BPS. As itscommittee chair I was present at a BPSCouncil meeting when it was reported thatthe BPS held royalties from publications byPhilip Vernon and William Wall and wasseeking advice on how best to deploy thefunds. Given the nature of their contribu-tions to psychology, and the regard in whichthey were held, I suggested the EducationSection be consulted, with the result thatCouncil agreed to fund an annual lecture tobe named after the two men and to be givenat the Section annual conference and there-after published. But what, may be asked, wasthe nature of the work being appreciated inthis recognition? It was certainly not thesame for both.When Philip Vernon began his degree

studies at Cambridge in 1927 he was alreadyacquainted with psychology (his father was awell-known industrial psychologist) and wasinterested in work on mental development.His Master’s and doctoral studies there sethim on a life course as a noted research figurein the fields of human personality, skill and

intelligence. His work on personality and itsmeasurement was influenced from the startby the then ongoing work of Gordon Allportin America, whilst that on intelligence devel-oped in relation to that of Cyril Burt andCharles Spearman in London, work whichfocused on attempts to measure human intel-ligence and skills and to explore their struc-ture through statistical analysis (notablyfactor analysis) of such data. By 1938 Vernonhad held a variety of academic positions onboth sides of the Atlantic before beingappointed to the Chair in Psychology atGlasgow University, a position he held until1947 and from which he advised the Admi-ralty and the War Office on the training andselection of recruits during World War Two.In 1949 he was appointed to the Chair inEducational Psychology at London, a positionfrom which he published major works on thenature and assessment of personality andintelligence. There can hardly have been apsychology graduate in the country who didnot know of his work and appreciate thedistinction he made between theoreticalintelligence (ability influenced by heredityand environment) and measured intelligenceas descriptive of tested performance onvarious criteria. Having established an inter-national reputation in the field of educa-tional psychology he retired from London in1968 to a Chair in Educational Psychology inCalgary where he continued to work andpublish until 1978. In 1979 he published amajor work entitled Intelligence – Heredity andEnvironment drawing together major issues inthe highly controversial field to which he hadmade such an outstanding contribution. In addition to his university appoint-

ments Philip Vernon was active in the work ofthe BPS. He edited the British Journal ofEducational Psychology and was welcomed at

ForewordHazel Francis

conferences where his presence was muchappreciated, not least for his quiet opennessand friendly helpfulness. I last heard fromhim in 1985 when we enjoyed an exchange ofletters and he made very apt comments onthe problems of measurement of complexeducational skills. William Wall, generally known as Bill,

pursued educational problems of a differentsort. Like Vernon he was born before WorldWar One and began his career in the depres-sion years of the 1930s, but (ever an impres-sive amateur artist) he first trained as anarchitect. Lack of employment prospects ledhim to take a degree in English and tobecome a secondary school teacher. WorldWar Two service brought him into contactwith illiterate recruits whose plight so movedhim that he felt committed to working toimprove educational provision for adoles-cents. To arm himself for this cause hepursued a psychology degree at UniversityCollege, London, followed by a doctoratefrom the University of Birmingham where hehad taken a teaching post. After a briefperiod on the staff of the university his partic-ular motivation and his excellent Frenchtook him to a post in Paris with UNESCO towork on child development and education.Here he met the desperate need for

educational development in the post-warworld. He made numerous internationalcontacts and advised on projects in severalcountries including the UK, where the BPSinvolved him in assisting local authorities todevelop educational psychology services. In1956 his appreciation of the need for psycho-logical research in educational developmentlay behind his appointment to the post of

Director of the National Foundation forEducational Research where he facilitated anumber of projects including the NationalChild Development Study. The internationalinterest prevailed, however, and in 1968 hemoved to the London University Institute ofEducation where, as Dean, he was able towork with staff involved in internationaldevelopment and in the education andtraining of staff and students from overseascentres. He maintained such work, particu-larly with reference to adolescent develop-ment and education, when he was appointedto the Chair in Educational Psychology in1972 and later, from 1978, when he retired towork for the Bernard van Leer Foundation. I came to know Bill personally when I

moved to the London Chair. He was helpfuland kind and, true to character, he had meworking on a publication for van Leer andUNESCO before I had laid the foundations inLondon for continuing my own work. Bill wasnot so much a research psychologist himselfas a man with a mission to use research toencourage and develop services, particularlyfor educationally needy adolescents. As might be imagined, the extensive

publications of the two men reflect theirdifferent, but hugely important, contribu-tions to psychology and education. I believethe Section made a very good decision tohonour the value of psychological enquiry inthe context of educational needs when itdrew these two men together in establishingthe Vernon-Wall Lectures.

Hazel FrancisProfessor Emerita in Educational Psychology,University of London.

35th Vernon-Wall Lecture 3

Foreword

INTRODUCTIONSchool is academically demanding andbecomes more so as students move fromelementary school to middle school to highschool. Across these stages of schooling (andyear levels within them), there is an escalationin homework, frequency and difficulty ofassessment, content to be covered, subject difficulty, and competing deadlines(Anderman, 2013; Anderman & Mueller, 2010;Graham & Hill, 2003; Hanewald, 2013;Kvaslund, 2000; Martin, 2015; Martin, Way,Bobis & Anderson, 2015; Zeedyk, Gallacher,

Henderson, Hope, Husband & Lindsay, 2003).This progressive escalation in challenge placesincreased cognitive demands on students. At the same time, there are well-docu-

mented declines in motivation and engage-ment as students move from elementary toand through high school. For example,Eccles and colleagues (Eccles & Midgley,1989; Eccles, Midgley, Wigfield, Buchanan,Reuman, Flanagan & Mac Iver, 1993; Eccles& Roesser, 2009; Wang & Eccles, 2012; seealso Booth & Gerard, 2014; Gillen-O’Neel &Fuligni, 2013) have identified significant

4 35th Vernon-Wall Lecture

Can educators reduce students’ cognitiveload and boost motivation andengagement?Integrating explicit instruction anddiscovery learning through LoadReduction Instruction (LRI)Andrew J. Martin

Load Reduction Instruction (LRI) is an umbrella term referring to instructional approaches that seek to reducecognitive load in order to optimise students’ learning and achievement. LRI typically encompasses explicit anddirect instruction, and under particular conditions can also encompass less structured approaches such asguided discovery-, problem-, and inquiry-based learning. Theory and research support the role of LRI instudents’ academic learning and achievement. Relatively less attention has been given to the role of LRI instudents’ academic motivation and engagement. This review examines key dimensions of motivation andengagement and explores the extent to which specific approaches and strategies under LRI may promote them.A major tenet of the review is that students are at first novices with respect to academic skill and subject matterand that a structured and somewhat directional approach to instruction that reduces cognitive load is impor-tant for achievement, motivation, and engagement in the early stages of learning. LRI helps build the contentof long-term memory and develops a level of fluency and automaticity that frees up working memory to applyto a given task or problem. As discussed, this fluency and automaticity has implications for students’ moti-vation and engagement. Importantly, as core skill, knowledge and automaticity further develop, LRI empha-sises the centrality of guided discovery-, problem-, and inquiry-based learning. Introduced at the appropriatepoint in the learning process, these scaffolded exploratory approaches can also be a means to manage cogni-tive load, generate autonomous learning, and provide a further basis for students’ motivation and engage-ment. The review concludes by showing how these instructional practices that unambiguously emphasise therole of the teacher are in fact predominantly student-centered and student-salient. Taken together, it is consid-ered important to recognise the motivating and engaging properties of clear, structured and well guidedinstruction, and the implications this has for students’ learning and achievement outcomes.

35th Vernon-Wall Lecture 5

Using Load Reduction Instruction (LRI) to boost motivation and engagement

declines in academic expectancy and valuingbetween elementary and high school. Oncein high school, Martin (2007, 2009) hasshown that both motivation and engagementdecline as students move from early highschool to middle high school and that thisfollows from higher levels of motivation andengagement in elementary school. Ecclesand Midgley (1989) proposed that motiva-tion and engagement decline across the tran-sition from elementary to middle/highschool because the developmental needs ofadolescents do not fit with the change ofcontext and demands in high school – andnor do instructional approaches adequatelymeet the needs of the developing learner. The escalation in demands through

school brings into consideration the need toapproach instruction in ways that appropri-ately manage the burden on learners wherepossible and feasible. Cognitive psychologyhas been informative in identifying instruc-tional approaches that are directly geared tomanaging the cognitive load on students tobetter help them learn and achieve. Thisarticle considers numerous instructionalapproaches that explicitly or implicitlyappropriately manage the cognitive burdenon students as they learn. ‘Load Reduction Instruction’ (LRI) is

introduced here as an umbrella term thatencompasses instructional models such asdirect instruction and explicit instruction –as well as some less structured approaches to instruction (e.g. guided discoverylearning) – that seek to optimally manage thecognitive burden on students in order toenhance their learning and achievement.To date, the bulk of research into LRI

approaches has focused on their effects forlearning and achievement. As discussed inthis review, findings support the role of LRIin generating learning and achievementgains. Although learning and achievementare desirable ends in themselves, there areother factors that are considered desirableacademic ends. Motivation and engagementare two such factors salient on the psycho-

educational landscape. Indeed, from a cogni-tive psychological perspective, motivationand engagement are recognised as importantfactors in more complex learning (e.g. VanMerrienboer & Sweller, 2005) and factorsthat can increase the cognitive resourcesdevoted to a task (e.g. Paas, Renkl & Sweller,2003).The present review considers the rela-

tionship between motivation, engagement,and LRI. It examines key dimensions of moti-vation and engagement and explores theextent to which specific approaches andstrategies under LRI can address them. In sodoing, it seeks to complement the large bodyof work into LRI and its achievement effectswith closer consideration of its potentialyields for students’ motivation and engage-ment. Figure 1 presents an overview of thethemes and processes addressed herein.In addressing these issues, the review is

organised into five parts.

Part 1. Load Reduction Instruction: (i) defi-nition and description of LRI, (ii) a review ofhuman cognitive architecture as relevant toLRI, (iii) consideration of fluency and auto-maticity, (iv) a summary of LRI effects onachievement, (v) consideration of LRI fordiverse learners and subject areas, and (vi)identification of specific Load ReductionInstructional elements. Part 2. Motivation and Engagement: (vii)definition and description of motivation andengagement and (viii) a motivation andengagement framework for considering LRI. Part 3. Load Reduction Instruction, Motiva-tion, and Engagement: (ix) LRI approachesfor specific motivation and engagementdimensions. Part 4. Load Reduction Instruction and theBroader Process of Learning: (x) the role ofguided discovery learning and (xi) under-standing the optimal learning sequence.Part 5. Looking Forward: (xii) Opportunitiesfor future research in LRI, explicit instruc-tion, motivation, and engagement.

6 35th Vernon-Wall Lecture

Andrew J. Martin

Load Reduction Instruction (LRI)Instruction that appropriately reduces or

manages the cognitive load on the student in the learning process.

Key elements(1) Reducing the difficulty of a task during initial learning(2) Instructional support and scaffolding through the task(3) Ample structured practice(4) Appropriate provision of instructional feedback(5) Independent practice and guided autonomy.

Major instructional approachesExplicit instruction and guided discovery learning.

Specific instructional strategiesPre-training; Modelling important processes; Showcasing; Segmenting; Preliminary

(and spaced) reviews; Reducing split-attention; Integrating; Information integrationsequencing; Harnessing different modalities; Avoiding redundancy; Increasing

coherence; Signalling; Organising information thematically; Allowing appropriateinstructional time; Checking for understanding; Worked examples; Providing

templates; Prompting; Personalising; Deliberate practice; Mental practice; Guided practice; Feedback; Feedforward; Independent practice; Guided discovery learning.

Academic outcomesLearning, achievement, motivation, engagement.

Figure 1: Organising themes and processes for this review.

PART 1: LOAD REDUCTIONINSTRUCTIONLoad Reduction Instruction (and GuidedDiscovery Learning)Load Reduction Instruction (LRI) is definedhere as a mode of teacher-led instructionthat involves the following at key points inthe learning process: (1) Reducing the difficulty of a task during

initial learning(2) Instructional support and scaffolding

through the task(3) Ample structured practice(4) Appropriate provision of instructional

feedback, and (5) Independent practice and guided

autonomy (e.g. Adams & Engelmann,1996; Cromley & Byrnes, 2012; Fisher &Frey, 2008; Magliaro, Lockee & Burton,2005; Martin, 2013, 2015; Rosenshine,1986, 2008, 2009; Stein, Carnine & Dixon,1998; Wood, Bruner & Ross, 1976).

A major tenet of LRI is that learners are atfirst novices with respect to academic skilland subject matter, that a structured andsystemic approach to instruction is importantin the early stages of learning, and that thereis an appropriate time for guided discoveryand exploratory approaches as novicesbecome more developed in their learning(Liem & Martin, 2013; see also Kalyuga,Ayres, Chandler & Sweller, 2003). Indeed, guided discovery learning can be

another means by which to manage cognitiveload for the student in the learning process.Accordingly, attention will also be given tothe role of guided discovery learning as apart of LRI. As discussed below, followingsufficient explicit input, guided practice anddemonstration of independent learning,there is an important place for guideddiscovery learning, including with regards tomotivation and engagement (Liem & Martin,2013; Martin, 2013). Once learners progressbeyond novice status and have sufficientlyautomated core skills and knowledge, theyare ready to engage in meaningful discoveryand exploratory learning that have motiva-tional properties beyond the motivational

yields experienced through LRI. LRI thusrecognises that explicit and constructivistlearning and teaching are inextricably inter-twined such that the effectiveness of one isreliant on the effectiveness of the other. Although other frameworks have recog-

nised the roles of both explicit and discovery approaches (e.g. ‘balanced instruc-tion’, ‘gradual release of responsibility’,‘enhanced discovery learning’, ‘differenti-ated instruction’ etc; e.g. Alfieri, Brooks,Aldrich & Tenenbaum, 2011; Fisher & Frey,2008; Marzano, 2011; Maynes, Julien-Schultz& Dunn, 2010; Pearson & Gallagher, 1983;Pressley & Allington, 2014; Tomlinson,2001), LRI is distinct in that its emphasis ison reducing or managing the cognitiveburden on students as they learn. LRI is thustermed, framed, and developed deliberatelyto indicate why we engage its various instruc-tional elements – namely, to deliver instruc-tion and instructional support so as toappropriately reduce and manage the cogni-tive burden on the learner.

The cognitive architecture of the humanmind: Working and long-term memoryWhen developing instructional approachesfor students, it is important to understandthe cognitive parameters relevant tolearning. The architecture of the humanmind – and its memory systems – is one of thecore foundations underpinning the rationalefor LRI approaches. This has implications forthe development and delivery of LRI – as wellas the ordering and balancing of explicitinstruction and guided discovery learning. Working and long-term memory are

primary mechanisms for learning (Kirschner,Sweller & Clark, 2006; Sweller, 2012; Winne &Nesbit, 2010). Working memory refers to theconscious component of cognition respon-sible for receiving and processing informa-tion, performing tasks, solving problems,etc. – particularly new information, new tasks,and novel problems. Learning is believed tooccur when information is successfully movedfrom working memory and stored in long-term memory (Kirschner et al., 2006; Sweller,

35th Vernon-Wall Lecture 7

Using Load Reduction Instruction (LRI) to boost motivation and engagement

2012; Winne & Nesbit, 2010). Figure 2 showsthe process, with stimuli received by thesensory register (e.g. sound, sight, touch etc.)sent to working memory, information inworking memory is encoded and sent to long-term memory, and information in long-termmemory is retrieved to working memory to beapplied as necessary.If working memory is overly burdened or

overloaded then there is a heightened riskthat instructional content is not understood,information is misinterpreted or confused,information is not effectively encoded in long-term memory, and learning is markedlyslowed down (Rosenshine, 1986, 2009; Tobias,1982). Given this, there is a need to deliverinstruction, present instructional material,and organise learning tasks that do not overlyor unnecessarily burden students’ workingmemory (Kirschner et al., 2006). It is also the case that working memory is

limited. Indeed, because a major function ofworking memory in the classroom is toprocess novel, unfamiliar information thatcomes from others (via listening, observing, orreading), working memory limits are highlyrelevant at many points of the learningprocess. This presents a substantial challengeto teachers as effective instruction relies onthem navigating this limited conscious aspectof the cognitive structure (working memory)when teaching new material and presentingnovel subject matter (Sweller, Ayres &Kalyuga, 2011; Winne & Nesbit, 2010). It hasbeen speculated that information stored inworking memory has a capacity of about seven

elements (or even as low as four elements plusor minus one element). Further, this can belost within about 30 seconds unless rehearsed(Baddeley, 1994). Clearly, a vast body ofinstructional material comprises informationthat exceeds seven (or so) elements orrequires the student to be able to retainextended or complex concepts in consciousworking memory for more than 30 seconds.This reality has led to research and theory intoinstructional approaches that aim to accom-modate the boundary conditions inherent inlearners’ working memory systems.Fortunately, long-term memory does not

have the same limitations as working memory.Long-term memory has vast capacity. Thus, ifinformation can be effectively and accuratelystored in long-term memory and if workingmemory can efficiently access this long-termmemory, successful learning can take place.Given this, there is a clear necessity to deliverinstruction and develop instructional materialthat optimally assists the processing of informa-tion to long-term memory from workingmemory, the processing of information fromlong-term memory to working memory, and aworking memory that is freed from unnecessaryburden or load (Martin, 2015; Paas et al., 2003;Sweller, 2003, 2004; Winne & Nesbit, 2010). From a cognitive load perspective, learning

thus very much relies on building long-termmemory and effectively managing workingmemory to facilitate this (Kirschner et al.,2006; Sweller, 2012; Winne & Nesbit, 2010).According to Kirschner and colleagues: ‘Anyinstructional theory that ignores the limits of

8 35th Vernon-Wall Lecture

Andrew J. Martin

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Figure 2: Process of sensory, working, and long-term memory.

working memory when dealing with novelinformation or ignores the disappearance ofthose limits when dealing with familiar infor-mation is unlikely to be effective’ (2006, p.77). Indeed, cognitive load theorists suggest

three goals for designing learning: reduceextraneous cognitive load, manage essentialcognitive processing, and foster generativeprocessing (Mayer, 2004; Mayer & Moreno,2010; Moreno & Mayer, 2010). In all cases, it isrecognised that cognitive capacity is limitedand so it is important to reduce load onlearners in order to facilitate the learningprocess. Notably, when dealing with familiar,organised information held in long-termmemory, there are no known capacity or dura-tion limits on working memory. Thus,students are transformed when information istransferred to long-term memory and thisexplains why education is transformative(Sweller, 2012).

Fluency and automaticityAccording to Rosenshine (1986, 2009),fluency and automaticity are vital means ofreducing the burden on working memory.This occurs when information is effectivelystored in long-term memory and is accessedby working memory fluently and seeminglyautomatically. This frees up workingmemory that can then be used to processnew information to long-term memory, toapply one’s learning, or for higher orderthinking and guided discovery learning(Rosenshine, 1986, 2009). That is, as long-term memory builds and automaticitydevelops, the learner is ready for greaterdiscovery, exploration, and inquiryapproaches to instruction. The pedagogicalapproach traversing this process is hereinreferred to as LRI.Indeed, it is claimed that it is this auto-

maticity that demarcates novice learnersfrom expert learners. Expert learners deriveand build their skill by drawing on the exten-sive information stored in long-term memoryand quickly selecting and applying it to solvenew problems (Kirschner et al., 2006).Accordingly, the aim of education is to

increase the information held in long-termmemory and this is achieved through instruc-tion that optimises the capacity of workingmemory and long-term memory to processnew information efficiently.Automaticity also demarcates the student

who struggles academically from the studentwho does not (Martin, 2015). There aresome students for whom working memory(or related executive functions) is impaired.These students are more likely to be cogni-tively overloaded than students without suchimpairments. Especially for these academi-cally at-risk students, it is important that teachers implement instructionalapproaches that reduce the burden onworking memory. Accommodating the boundary condi-

tions of human cognitive architecture as rele-vant to learning thus relies on the teacher tostructure learning material and learningactivities in a way that reduces ambiguity,enhances clarity, builds in sequencing, andharnesses scaffolds. In so doing, the teachermanages the learning and instructionprocess in a way that optimises learner andlearning efficiency. Notably, recent develop-ments in cognitive psychology that have beenapplied to educational processes provideguidance on how material can be organisedand presented to learners to free up workingmemory, optimise long-term memory, andenhance the processing of information fromlong-term to working memory – and in sodoing, realise the aims of instructionintended to reduce the cognitive burden onstudents (Winne & Nesbit, 2010). However,as discussed below, as fluency and auto-maticity develop, the cognitive load inherentin instruction may be upwardly adjusted (e.g.via independent and guided discoverylearning) to match the developing expertiseof the learner.

Load Reduction Instruction andevidence: Learning and achievementIn numerous empirical studies, meta-analysesand reviews, the achievement-related meritsof LRI approaches are evident (Cromley &

35th Vernon-Wall Lecture 9

Using Load Reduction Instruction (LRI) to boost motivation and engagement

Byrnes, 2009; Lee & Anderson, 2013; Liem &Martin, 2013; Mayer, 2004). Across numeroussubject domains and skill sets to be learned,LRI is positively associated with learningand/or achievement (e.g. see Cooper &Sweller, 1987; Klahr & Nigam, 2004; Matlen& Klahr, 2010; Strand-Cary & Klahr, 2008;Sweller & Cooper, 1985).In early work, Adams and Engelmann

(1996) examined the effectiveness of majoreducational approaches (including thosealigned with LRI) on numerous educationaloutcomes. Findings showed that explicitinstruction, for example, yielded consistentlypositive effects on basic skills (e.g. wordrecognition, spelling, math computation)and cognitive skills (e.g. reading comprehen-sion, math problem solving). Positive effectswere also observed for motivational factorsand affective outcomes (e.g. self-concept,attributions to success). In a meta-analysis by Haas (2005), the

most effective method of teaching algebrawas deemed to be explicit instruction. Itseffectiveness was attributed to the focus onappropriate pacing and both guided andindependent practice. Borman andcolleagues (2003) conducted a meta-analysisof numerous school reform programs. Theyfound that explicit instruction evinced thestrongest systematic evidence of effective-ness. In a meta-analysis across 304 explicit(direct) instruction studies, Hattie (2009)ranked explicit instruction 26th out of 138effects on achievement, placing it ‘amongthe most successful outcomes’ (p.205).Meta-analysis by Alfieri and colleagues

(2011) showed that the specific techniquesemphasised under LRI-oriented frameworksmoderated the effects on achievement. Forexample, worked examples yielded thestrongest results, followed by feedback, directteaching, and explanations. When reviewingthe range of meta-analyses conducted overthe past two decades, Liem and Martin(2013) concluded that LRI approaches thatallow teachers to be ‘activators’ of studentlearning (Hattie, 2009) are well placed toalleviate cognitive demands and assist

working memory and long-term memory toeffectively process instructional material (seeAlfieri et al., 2011; Kirschner et al., 2006).

An examination of evidence across arange of studentsIn assessing the feasibility of any instructionalapproach, it is important to examine its effec-tiveness across different types of learners. Forexample, if LRI is to be implemented in theclassroom, it is important to show that itseffects are positive across the range of studentsthat typically reside in that classroom. Indeed,this range comprises (inter alia) students ofhigh, average and low ability, students withspecific learning disabilities (e.g. dyslexia) orexecutive function deficits (e.g. ADHD), andstudents at-risk on the basis of such factors associo-economic status (Martin, 2015).

Low and high performersAdams and Engelmann (1996) argue that lowand high performers are not qualitativelydifferent. There are relatively few mistakesamong low performers that high performersare not at risk of making. Instead, variationseems to be in the degree and amount of aparticular instructional approach that isappropriate for low and high performers:‘Work with students of different abilitiesreveals that higher performers require lessrepetition, fewer examples, and often lessreinforcement than lower performers. Lowerperformers may have concept and skill defi-ciencies that the higher performers of thesame age do not have, and these deficienciesrequire time to remedy’ (Adams & Engel-mann, 1996, p.28; see also Tarver, 1998; Tarver& Jung, 1995; Vitale & Romance, 1992). Accordingly, if a complex skill (e.g.

reading) is able to be taught to lowerperformers, the main difference from highperformers is that it tends to be easier andfaster to teach to higher performers (Adams& Engelmann, 1996): ‘Given that both thehigher performer and the lower performerdo not know a particular skill, however, andgiven that both start about the same level ofnaiveté, both would have to learn the same

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information, operations, or processes’(p.29). Hence the main instructional varia-tion would be the pace of the presentation ofinformation, the relative weight given to thecore steps in explicit instruction, and thespeed at which they would be moved ontoguided discovery and independent learning.

Academically at-risk studentsStudents who are academically at-risk will beparticularly challenged – and potentiallydisadvantaged – with the escalation ofcurriculum and the cognitive load this placeson them (Martin, 2015). In the ‘regular’classroom, for example, cognitive demandswill be especially salient for students withexecutive function disorders (i.e. disorderssuch as impairments to working memory,planning, organisation) such as attention-deficit/hyperactivity disorder (ADHD) aswell as for students with specific learningdisabilities such as dyslexia, dyscalculia andthe like. Further, because these types of disor-ders are co-morbid with other disorders, it isnot uncommon that at-risk students willexperience more than one cognitive diffi-culty (Cantwell & Baker, 1991; Tabassam &Grainger, 2002). For example, estimatessuggest that approximately one-third ofstudents with specific learning disabilitiesalso have ADHD (Hallahan, 1989; Robins,1992). Carmichael and colleagues (1997)found ADHD in around half of students diag-nosed with a specific learning disability.McKinney and colleagues (1993) found co-occurrence over 60 per cent. If the effects ofLRI (or any form of instruction for thatmatter) are not positive for such at-riskstudents, then there is a danger that an unan-ticipated consequence of its implementationis to create and/or widen achievement gaps. In relation to at-risk students, it has been

claimed that they can have difficulty under-standing or identifying many of the subtletiesof instructional material and the ‘hidden struc-ture’ of learning (Ewing, 2011). By making allelements of learning explicit, less is hidden.Consistent with this, LRI-oriented practiceshave been found to be effective for special

education students (Forness, 2001; Forness,Kavale, Blum & Lloyd, 1997). In a meta-analysis by Swanson and Sachse-Lee (2000),substantial variance in academic outcomes forstudents with learning disabilities was relatedto instructional strategies involving drill-repeti-tion-practice-review procedures as well asappropriate segmentation of material. Theyconcluded, ‘regardless of the practical ortheoretical orientation of a study, treatmentsthat included the aforementioned instructioncomponents yielded high effect sizes’ (p.129;see also McMullen & Madelaine, 2014; Rupley,Blair & Nichols, 2009). Similarly, in a review of meta-analyses of

students in special education services byForness (2001), only four meta-analyses metthe criterion for large achievement-relatedeffect sizes, one of which related toexplicit/direct instruction. In other meta-analyses of students with learning disabilitiesand LRI-oriented practice, Swanson et al.(1996) and Swanson and Hoskyn (1998)found large effect sizes for achievement, as didHattie (2009) for special education students.With regards to ‘core’ skills such as

literacy and numeracy, LRI has been effec-tive for academically at-risk students. In thearea of literacy for students with learningdisabilities, Jitendra and colleagues (2004)found significant gains maintained overtime and Mastropieri and colleagues (1996)found large effect sizes for reading compre-hension. Similar positive results were foundfor at-risk students and reading achieve-ment (Carlson & Francis, 2003; see alsoKamps, Abbott, Greenwood, Wills,Veerkamp & Kaufman, 2008). For mathe-matics achievement and learning disabledstudents, Kroesbergen and Van Luit (2003)and Gersten and colleagues (2009) derivedmoderate to large effect sizes for loadreduction techniques. In reviewing suchfindings, Purdie and Ellis (2005) concludedthe results: ‘clearly demonstrate thatteaching approaches based on directinstruction and strategy instruction producepositive effects for students with learningdifficulties’ (p.21; see also Farkota, 2003).

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Low socio-economic statusSocioeconomic status is another dimensionthrough which students can be placed atacademic risk (Sirin, 2005). In terms ofdiverse socio-demographic groups, LRIapproaches have shown efficacy for studentslow in socio-economic status and those whoare geographically marginalised. Forexample, Stockard (2010) showed thatexplicit instruction is effective in helping lowsocio-economic students overcome the lateelementary school slump typical of manystudents not exposed to enriched contexts.Stockard (2011) also showed that readingamong rural students is significantlyenhanced through LRI approaches, withtheir reading scores above national averagefollowing explicit instruction.

An examination of evidence across arange of subject areasThe effectiveness of LRI must also bedemonstrated across a range of subjectstaught in the typical educational context,including the so-called ‘concrete’ subjectssuch as mathematics and the less ‘struc-tured’ subjects such as English. To the extentthat LRI can be demonstrated to be effectiveacross this range of subjects, its educationalvalidity can be further established. Withrespect to LRI-oriented practice, explicitinstruction has been found to be effectivefor learning and achievement in subjectssuch as reading as well as in subjects such asmathematics (Hattie, 2009). In a review ofmathematics programs, Przychodzin-Havisand colleagues (2004) found resultsfavoured explicit instruction in the majorityof studies reviewed. In a subsequent publica-tion (2005) on reading, they also identifiedthe effectiveness of explicit instructionacross most studies. In a review of readingmastery, findings favoured LRI approaches(Schieffer, Marchand-Martella, Martella,Simonsen & Waldron-Soler, 2002).

Load Reduction Instructional elementsThus far, the review has focused broadly onLRI as an approach to pedagogy, the cogni-

tive rationale for its effectiveness, andsupportive evidence for diverse learners andsubject areas. As with any instructionalapproach, it is the component elements ofLRI that drive its specific and concrete oper-ationalisation. These core elements are whataddress the limits of working memory, opti-mise storage in long-term memory, andenhance processing between the two.Across many decades of research, span-

ning cognitive and educational psychology,there emerges some commonality in the keyor especially effective elements of instructionthat can reduce the burden on students’working memory. As noted above, theseelements are intended to facilitate theprocessing of information as relevant to thefunctions of working and long-term memory –and thereby reduce cognitive load. As relevantto the present review, they are also a means bywhich to assess the role of LRI in fosteringstudent motivation and engagement. It will be recalled that LRI involves the

following at some point in the learning andachievement process: (1) Reducing the difficulty of a task during

initial learning(2) Instructional support and scaffolding

through the task(3) Ample structured practice(4) Appropriate provision of instructional

feedback, and (5) Independent practice and guided

autonomy. These represent a useful organising frame-work for considering key elements of LRI.Here, these elements are briefly introduced.In a section to follow, they are described indetail, including how they may assistacademic motivation and engagement.

(1) Reducing the difficulty of a task duringinitial learningPre-training � Teacher provides early instruction onthe core elements of a task (e.g.identifying name, definition, location,function of topics or components) toassist subsequent learning

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Modelling important processes� Teacher demonstrates how tocomplete a task; can also involve‘think-aloud’ strategies as the teacherconducts a task

Showcasing� Teacher shares examples of goodpractices and good work to provideclarity on what constitutes good workand how to do it

Segmenting� Teacher breaks a task into ‘bite-size’components (or ‘chunks’) andencourages students to see thecompletion of each component as asuccess

Preliminary (and spaced) reviews� Teacher and students review priorlearning at the outset of a new task orlesson; teacher reviews at regular(spaced) intervals (e.g. review priorweek’s learning at the start of each week)

(2) Instructional support and scaffoldingthrough the taskReducing split-attention� Two or more stimuli are integratedwhere feasible to reduce splittingstudents’ attention across disparatestimuli (e.g. integrate the equation forfinding an angle into the angle itselfon a given diagram)

Integrating� Teacher integrates the focus of alearning task with a meaningfulproblem (e.g. integrate instruction onpunctuation into a student’s own essay)

Information integration sequencing� Teacher integrates two successivepieces of instructional material intothe one instructional element (e.g.integrate the narration of howlightening is formed with ananimation of that process)

Harnessing different modalities� Teacher presents different pieces ofinformation (or stimuli) in a differentmodality (e.g. present an image with anarrative in order to reduce the burdenon visual and auditory processers)

Avoiding redundancy and increasingcoherence� Where possible, teacher presentsinformation once (avoidingredundancy) and organises materialso that extraneous or overly elaboratematerial that may be tangential toessential learning is reduced orremoved (increasing coherence)

Signalling� Teacher provides cues to help thelearner locate and focus on theessential material in a lesson oractivity (e.g. teacher asks students towatch out for a particular event orcharacter in a plot)

Organising information thematically� Teacher identifies a major/maintheme in a task or learning activityand explicitly connects instruction tothis theme

Allowing appropriate instructional time� Teacher schedules tasks and lessons toensure sufficient instructional timeoccurs in a task, in a lesson, and acrossthe day

Checking for understanding� Teacher employs checking strategiessuch as frequently posing questionsand asking students to summarisemajor points or repeat explanations

Worked examples � New material is presented to learnerswith completed samples of work thatshow how a particular problem can besolved or task is to be completed

Providing Templates � Materials are provided to learners thatare formatted or structured to helpthe learner stay on track or that listthe important features to include oraddress in a task

Prompting� Learners are strategically prompted topersist with and complete lessstructured tasks such as those foundin comprehension and writing tasks(e.g. students are asked to identify the‘what’, ‘who’, ‘why’, and ‘when’ in a

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stimulus passage; this helps themextract specific information orarticulate an answer or response)

Personalising� Teacher adjusts wording and/oradministration of a task to involve thelearner in a more personalised andindividually-relevant way (e.g. Useinstructions such as ‘Your goal in thistask is to …’ rather than ‘The goal forthis task is to …’)

(3) Ample structured practiceDeliberate practice� Teacher ensures rehearsal that isrelevant to a specific skill, usually alsoinvolving feedback, and conducted bythe student on his/her own

Mental practice� Learners imagine or mentallyrehearse a concept or procedure (e.g.the student studies an example, thenturns away and rehearses the examplein his/her mind)

Guided practice� Learners are systematically guidedthrough the steps of learning orproblem solving (e.g. promptingresponses through a task or providingpart of a solution for a student tocomplete)

(4) Appropriate provision of instructionalfeedbackFeedback � Concrete and specific information isprovided on the correctness of ananswer or the quality of application

Feedforward� Concrete and specific information isprovided on how the answer or qualityof the application can be improved infuture schoolwork

(5) Independent practice and guidedautonomyIndependent practice� When skills and knowledge becomeautomated and fluent, the learner isencouraged to attempt similarproblem tasks independently

Guided discovery learning� When the learner has engaged insuccessful independent practice,he/she is encouraged to undertakenew tasks, move in new directions, orapply learning to ‘real-world’problems that further enrichlearning.

(For research and reviews supporting identi-fication of these elements, see for example:Adams & Engelmann, 1996; Atkinson, Derry,Renkl & Wortham , 2000; Cromley & Byrnes,2012; DeRuvo, 2009; Farkota, 2003; Ginns,Martin & Marsh, 2013; Hattie, 2009, 2012;Hunter, 1984; Lee & Anderson, 2013; Liem &Martin, 2013; Martin, 2013, 2015; Marzano,2003, 2011; Mayer & Moreno, 2010;Nandagopal & Ericsson, 2012; Nuthall, 1999;Purdie & Ellis, 2005; Renkle, 2014; Renkl &Atkinson, 2010; Rosenshine, 1986, 1995,2009; Schute, 2008; Sweller, 2012; van Gog,Ericsson, Rikers & Paas, 2005; Van Merriën-boer & Sweller , 2005; Wiliam, 2011).The present review draws on each of these

key elements of LRI-oriented approaches andconsiders how each one may impact students’motivation and engagement. In doing so, theaim is to extend the large body of work intoLRI that has focused on its achievementeffects to also consider it in terms of its moti-vation and engagement yields. To the extentthat plausible connections can be made, LRImay be considered an instructional approachthat not only has learning and achievementbenefits, but also benefits for students’academic motivation and engagement.

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PART 2: MOTIVATION ANDENGAGEMENTIt is evident that LRI approaches haveachievement-related merit for a wide rangeof students, including for academically at-risk students. It is also evident that LRI canbe effective for achievement across diversecurricular domains. Relatively less attentionhas been directed to LRI and academicmotivation and engagement. The limitedbody of research has suggested positiveconnections between the two (e.g. forresearch and reviews see Adams & Engel-mann, 1996; Bessellieu, Kozloff & Rice,2001; Farkota, 2003; Reeves, 2010; Tarver,1998; Van Keer & Verhaeghe, 2005).However, when motivation and engagementare addressed in LRI research, they tend tobe considered as part of a range of outcomevariables (i.e. not the focus of the researchstudy), and typically they are positioned assomewhat secondary to achievement. It isalso the case that motivation and engage-ment research has incorporated LRIperspectives. However, this work tends torepresent LRI as part of a range of peda-gogical approaches (e.g. Bost & Riccomini,2006; Cromley & Byrnes, 2012; Guthrie &Davis, 2003; Wigfield, Guthrie, Perencevich,Taboada, Klauda, McRae & Barbosa, 2008;Wigfield, Guthrie, Tonks & Perencevich,2004); that is, LRI is not often the mainfocus in motivation and engagementresearch. There is thus a need to purpose-fully focus on motivation and engagementfactors and formally assess the extent towhich LRI approaches might address them.

Multidimensional motivation andengagementMotivation and engagement are definedhere as students’ inclination, interest,energy, drive, and effort to learn, work effec-tively, and achieve to potential (Liem &Martin, 2012; Martin 2007, 2009; Pintrich,2000, 2003; Reschly & Christenson, 2012;Schunk & Miller 2002; Schunk, Pintrich &Meece, 2008). Concerns have been raised

that the diversity of motivation and engage-ment theories and factors has left educa-tional psychology overly fragmented.Accordingly, there have been calls for morecohesive and integrative approaches to moti-vation and engagement theorising andresearch (e.g. Bong, 1996; Murphy &Alexander 2000; Pintrich 2003; Reeve, 2015;Reschly & Christenson, 2012). One recent integrative effort has led to

the development of a multidimensionalmodel of motivation and engagement,referred to as the Motivation and Engage-ment Wheel (Martin, 2007, 2009) – shownin Figure 3. Although the Wheel is the focusin this review, there are other examples ofmultidimensional motivation and engage-ment frameworks and instrumentation suchas that reflected in the Patterns of AdaptiveLearning Survey (PALS) by Midgley andcolleagues (1997), the Motivated Strategiesfor Learning Questionnaire by Pintrich,Smith, Garcia, and McKeachie (1991), theStudent Engagement Instrument (SEI) byAppleton, Christenson, Kim, and Reschly(2006), and the Inventory of School Motiva-tion (ISM) by McInerney, Yeung, and McIn-erney (2000).

The Motivation and Engagement WheelThere are three primary concepts underpin-ning the Wheel. The first is that motivationand engagement factors can be demarcatedinto ‘internal’ (or intrapsychic) and‘external’ (or behavioural) factors. Thesecond is that these factors can be demar-cated into adaptive and maladaptive dimen-sions. The third is that there are seminalmotivation theories important to represent.With regards to the ‘internal’ and

‘external’ dimensions of motivation andengagement, recent reviews of motivationand engagement have identified this as acommon theme through the literature (seeMartin, 2012b; Martin, Ginns & Papworth,2016 for reviews). Reeve (2012) has notedthat motivation comprises ‘private, unob-servable, psychological, neural, and biolog-

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ical’ factors whereas engagement comprises‘publicly observable behaviour’ (p.151).Cleary and Zimmerman (2012) identifiedengagement as comprising observable(behavioural) and internal (cognitive andaffective) factors. Ainley (2012) posits moti-vation as an inner psychological factor andengagement as a factor reflecting involve-ment in a task or activity. Anderman andPatrick (2012) demarcate engagement intoits emotional, cognitive and behaviouralterms (also see Fredicks et al., 2004 for adetailed review). Schunk and Mullen (2012)describe motivation as an internal force thatenergises engagement. Voelkl (2012) identi-fies affective and behavioural factors in theliterature and reports motivation as aligningwith the former and engagement aligningwith the latter. Taken together, these authorssuggest in one way or another that motiva-tion and engagement can be demarcated

into ‘internal’ and ‘external’ (or observable)forms. Accordingly, the Wheel is demarcatedinto motivation (primarily cognitive; but alsoemotional) that represents the ‘internal’ andengagement (behavioural) that representsthe ‘external’. In relation to adaptive and maladaptive

dimensions of motivation and engagement,it is the case that, for the most part, a gooddeal of motivation and engagement researchand theory emphasises positive constructsand positive constructions. However, it hasbeen suggested that a dual approach to moti-vating and engaging students is required:enhance adaptive motivation and engage-ment and reduce maladaptive motivationand engagement (Martin, 2012b; Martin,Anderson, Bobis, Way & Vellar, 2012).Accordingly, Martin and colleagues (2012)recommended that the study of motivationand engagement requires attention to both

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Self-efficacy

Mastery orientation

Valuing Persistence

Planning

Task management

Anxiety

Failure avoidance

Uncertaincontrol

Self-handicapping

Disengagement

MaladaptiveEngagement

AdaptiveMotivation

AdaptiveEngagement

MaladaptiveMotivation

Figure 3: The Motivation and Engagement Wheel. Reproduced with permission from A.J. Martin and Lifelong Achievement Group (www.lifelongachievement.com).

adaptive and maladaptive dimensions. Theirresearch operationalised persistence atschool in terms of the joint forces of‘switching on’ (engagement) and ‘switchingoff’ (disengagement). They found thatalthough the two are significantly correlated(negatively), they also each accounted forunique variance in the academic process.Accordingly, the Wheel comprises both adap-tive and maladaptive dimensions of motiva-tion and engagement. With regards to seminal psycho-educa-

tional theory, Pintrich (2003) identified keysubstantive issues critical to address as moti-vational science develops. He emphasisedthe importance of considering and concep-tualising motivation in terms of seminal theo-rising relevant to: self-efficacy (and relatedexpectancies), valuing, goal orientation, self-determination, self-regulation, attributions,control, need achievement, and self-worth.In line with this, there are numerous theoriesand conceptual frameworks describing andexplaining academic motivation and engage-ment, including self-efficacy and agencyperspectives (Bandura, 1997, 2001) thatsuggest inclusion of a self-efficacy factor;expectancy-value theory (Wigfield & Eccles,2000) suggesting a valuing factor; goal theory(Elliot, 2005) suggesting approach (masteryorientation) and avoidance (failure avoid-ance) goal factors; self-determination theory(Deci & Ryan, 2012; Ryan & Deci, 2000)suggesting core psychological needs such ascompetence (self-efficacy); self-regulatorytheories (Zimmerman, 2002) suggestingplanning, task management, and persistencefactors; attribution and related control theo-ries (Connell, 1985; Weiner, 2010)suggesting a control (or, conversely, uncer-tain control) factor; and need achievementand self-worth motivation theories(Covington, 1992, 1998, 2000) suggestinganxiety, self-handicapping and disengage-ment factors. Accordingly, as Figure 3 demonstrates, the

Wheel is organised into higher-order andlower-order factors. The higher order factors

reflect the internal/external andadaptive/maladaptive tenets of motivationand engagement. The lower order factorsreflect multidimensional psycho-educationaltheorizing suggested by Pintrich (2003). Thefour higher order factors are adaptive cogni-tion, sometimes referred to as adaptive moti-vation (lower-order factors: self-efficacy,valuing, mastery orientation); adaptive behav-iour, sometimes referred to as adaptiveengagement (lower-order factors: planningand monitoring behaviour, task management,persistence); maladaptive cognition, some-times referred to as maladaptive motivation(lower-order factors: anxiety, failure avoid-ance, uncertain control); and maladaptivebehaviour, sometimes referred to as maladap-tive engagement (lower-order factors: self-handicapping, disengagement). Each ofthese factors is briefly defined (followingMartin, 2007, 2009, 2010) as follows:

Adaptive motivation:� Self-efficacy is students’ belief andconfidence in their ability tounderstand or to do well inschoolwork, to meet challenges theyface, and to perform to the best oftheir ability.

� Valuing is how much students believewhat they learn at school is useful,relevant, meaningful, and important.

� Mastery orientation refers to students’interest in and focus on learning,developing new skills, improving,understanding, and doing a good jobfor its own sake and not just forrewards or the marks they will get fortheir efforts.

Adaptive engagement:� Persistence refers to how much studentskeep trying to work out an answer orto understand a problem, even if thatproblem is difficult or challenging.

� Planning (and monitoring) refers to howmuch students plan assignments,homework and study and, how muchthey actively keep track of theirprogress as they do this work.

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� Task management refers to howstudents use their study or homeworktime, organise a study or homeworktimetable, choose and arrange wherethey study or do homework, andincreasingly, how they manage theirdigital world (e.g. self-regulation andimpulse control with regards tomobile technology while doingschoolwork).

Maladaptive motivation:� Anxiety has two parts: feeling nervousand worrying. Feeling nervous is theuneasy or sick feeling students get whenthey think about or do theirschoolwork, assignments, or tests.Worrying refers to fearful thoughtsabout schoolwork, assignments, or tests.

� Uncertain control reflects students’uncertain or low sense of control,typically when they are unsure how todo well or how to avoid doing poorly.

� Failure avoidance refers to a motivationto do one’s schoolwork in order toavoid doing poorly, to avoid beingseen to do poorly, or to avoid thenegative consequences of poorperformance.

Maladaptive engagement:� Self-handicapping refers to behavioursthat reduce students’ prospects ofsuccess at school (e.g. waste time,procrastinate, do little or no study,misbehave in class) in order toestablish an alibi or excuse in case theydo not perform well.

� Disengagement refers to thoughts andfeelings of giving up, trying less eachweek, detachment from school andschoolwork, feelings of helplessness,and little or no involvement in class orschool activities.

The Motivation and Engagement ScaleThe conceptually-oriented Motivation andEngagement Wheel (Martin, 2007, 2009) isaccompanied by multidimensional measure-ment instrumentation – the Motivation andEngagement Scale (MES; Martin, 2016) – thatis used to assess each of the eleven factors.There are four items per factor, yielding 44items for the MES, each rated on a 1 (StronglyDisagree) to 7 (Strongly Agree) scale. TheMES (and select subscales within it) hasdemonstrated sound factor structure, highfactor loadings, reliable factors, invariance as afunction of age and gender, and externalvalidity with other educational and personalityfactors and processes (e.g. Bodkin-Andrews,Denson & Bansel, 2013; Bugler, McGeown &St Clair-Thompson, 2015; Edgar, 2015; Ginns,Martin, Liem & Papworth, 2014; Liem &Martin, 2012; Martin, 2007, 2009; Martin,Anderson, Bobis, Way & Vellar, 2012; Martin,Papworth, Ginns & Liem, 2014; Martin, Yu,Papworth, Ginns & Collie, 2015; Nagab-hushan, 2012; Plenty & Heubeck, 2011, 2013;Tinker & Elphinstone, 2014; Wurf & Croft-Piggin, 2015; Yeung, Barker, Tracy & Mooney,2013). The MES has also been validated inother countries such as China, the US,Canada, Jamaica, and the UK (Martin & Hau,2010; Martin, Martin, & Evans, 2016; Martin,Yu & Hau, 2014; Yin & Wang, 2015).

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PART 3: LOAD REDUCTIONINSTRUCTION, MOTIVATION, AND ENGAGEMENTHaving identified key facets of multidimen-sional motivation and engagement (via theWheel) and the key elements of LRI (via theLRI framework 1. Reducing task difficulty …5. Independent practice), it is possible toconduct a nuanced analysis of how the two areconnected. The approach adopted here is toconsider each of the 11 parts of the Motivationand Engagement Wheel and identify which ofthe key LRI elements are likely to promotethem. Essentially, then, the aim is to identifyhow the motivation and engagementelements in the first column (Column A) ofTable 1 are associated with the LRI elementsin the second column (Column B) of Table 1.Importantly, in considering the links

between motivation, engagement and LRI,it is emphasised that these links are indica-tive and suggestive, not prescriptive ordefinitive. Importantly also, in addition tothe links proposed here, there are otherplausible links between LRI elements anddifferent parts of the Wheel (e.g. guideddiscovery learning might also be connectedto mastery orientation). The point of thisreview is to identify channels of alignedrelevance between key elements of LRI andmajor motivation and engagement factors.As noted later in this review, empirical work is needed to ascertain which specificLRI strategies might explain most variancein distinct motivation and engagementfactors. Findings from these empiricalinvestigations will further illuminate, addto, and potentially qualify some of the linkssuggested herein.

Self-efficacyThe promotion of self-efficacy involvesrestructuring learning so as to maximiseopportunities for success (such as throughindividualizing tasks where possible; McIn-erney, 2000; Schunk & Miller, 2002),addressing and enhancing students’ (often-times negative) beliefs about themselves and

their competence (Beck, 1976, 1995;Meichenbaum, 1974; Wigfield & Tonks,2002), developing skills in effective goal-setting (Locke & Latham, 2002), andbreaking work into manageable and doable‘chunks’ (Martin, 2007). Such approachesare aimed at addressing cognition and/oroptimizing opportunities for success thatprovide a basis for enhancing one’s self-effi-cacy (McInerney, 2000). With regards to keyelements of LRI, four are particularly well-suited to promote these processes andoutcomes: pre-training, segmenting andorganising information, conducting prelimi-nary and spaced reviews, and modelling.

Pre-trainingSelf-efficacy builds as learning and compe-tence develop (Bandura, 2001; McInerney,2000; Schunk & Miller, 2002). Learning andcompetence are facilitated via access to asufficient amount of prior knowledge (Mayer& Moreno, 2010). For example, if teachingstudents how a motor works, there may besome pre-training on the main parts of amotor (name, location, function of part/s)that will assist subsequent learning andcompetence (referred to as the ‘pre-trainingprinciple’; Mayer & Moreno, 2010, ordepending on how and when information ispresented, the ‘isolated elements effect’;Pollock, Chandler & Sweller, 2002). Pre-training develops prior knowledge (stored inlong-term memory) which occupies fewerworking memory resources, leaving moreworking memory to acquire new knowledgeas the motor (for example) is explained morefully. Taken together, pre-training helpsmaximise the information held in students’long-term memory, helps organise informa-tion that makes it easier to understand, andstrengthens connections between workingmemory and long-term memory (Rosen-shine, 1995). Pre-training thus enhancesmemory systems that underpin learning andcompetence, and by implication, helps lay afoundation for a sense of efficacy relevant tothis learning and understanding.

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Pre-training may particularly benefitnovice or at-risk learners. Here, the teachermay provide additional instruction to somestudents prior to embarking on new units oflearning. This further ensures that essentialterms and basic skills required for a unit ofwork are known by these students. Theteacher takes some additional time todevelop this important prior knowledge tobetter ensure it is stored in long-termmemory to help working memory processnew incoming information (Mayer &Moreno, 2010) and to ensure the connectionbetween new information and prior knowl-edge is clearer. In sum, then, novices andacademically at-risk students can have diffi-culty in the early phases of learning (Martin,2015) and this is likely to impede their senseof efficacy throughout the learning process(Martin, 2012a). Pre-training enables a

stronger beginning to the learning processand potentially surer footing from a self-effi-cacy perspective.

Segmenting informationMartin (2003, 2005, 2010) has identified theimportance of competence as a basis forbuilding self-efficacy and ‘chunking’ as oneeffective strategy to achieve this. Chunkinginvolves: (a) breaking a task into moremanageable ‘chunks’ and (b) seeing thecompletion of each chunk as a success. Thefirst element helps students see the task asdoable and the second element buildscompetence and efficacy into the process ofcompleting the task (Martin, 2003, 2005,2010). This aligns closely with ‘segmenting’in the explicit instruction literature.Segmenting is a way to deal with informationthat is complex, multi-part, or substantial.

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A. Motivation and EngagementWheel factors

Adaptive motivation� Self-efficacy� Valuing � Mastery orientation

Adaptive engagement� Planning and monitoring behaviour� Task management� Persistence

Maladaptive motivation� Anxiety� Failure avoidance� Uncertain control

Maladaptive engagement� Self-handicapping� Disengagement

B. Load Reduction Instruction (LRI) elements

(1) Reducing the difficulty of a task during initiallearning

� Pre-training; Modelling important processes; Show-casing; Segmenting; Preliminary (and spaced) reviews

(2) Instructional support and scaffolding through thetask

� Reducing split-attention; Integrating; Informationintegration sequencing; Harnessing different modali-ties; Avoiding redundancy; Increasing coherence;Signalling; Organising information thematically;Allowing appropriate instructional time; Checking forunderstanding; Worked examples; Providing templates;Prompting; Personalising

(3) Ample structured practice� Deliberate practice; Mental practice; Guided practice

(4) Appropriate provision of instructional feedback� Feedback; Feedforward

(5) Independent practice and guided autonomy� Independent practice; Guided discovery learning

Table 1: The Motivation and Engagement Wheel and Load Reduction Instruction (LRI) elements.

Here the teacher breaks larger units intomore achievable segments and systematicallypresents this information as the learnergrasps the previous segment (referred to asthe ‘segmenting principle’; Mayer &Moreno, 2010; Rosenshine, 1995).Interestingly, in multimedia scenarios,

learning effectiveness is further enhancedwhen the pacing from one segment toanother is under the learner’s control (i.e.self-paced; Mayer & Moreno, 2010). Thissignals the importance of the learner’s self-determination through the process.Segmenting can also be adapted to indi-vidual students by adjusting the size andnumber of information segments presented.Thus, for expert learners, fewer and largersegments may be feasible whereas for novicelearners, more and smaller segments may bedesirable. In the above-mentioned multi-media example (where the pace from onesegment to another is in the learner’scontrol), the expert learner can move at abrisker pace while novice learners can moveat a slower pace.

Preliminary and spaced reviewsReviewing prior learning and instructionhelps students activate prior knowledge andunderstand the subject matter, buildingcompetence, and thus improving orsustaining their sense of efficacy (Marzano,1998). Review thus forms a mechanism thatnot only reinforces the prior knowledgeitself, but also affirms to the learner thathe/she has the requisite knowledge and skill,thus promoting self-efficacy. Review can bevery important at the outset of a lesson inorder to reacquaint learners with priorknowledge or material covered in a previouslesson (Hattie, 2009, 2012). According toRosenshine (1986, 2009), teachers adoptingLRI approaches will commence a lesson withabout five minutes reviewing relevant priorknowledge. This might include reviewingmathematics formulas or workings, readingsight words, revisiting chemical equations,and so on (see also Hunter, 1984). Review also has relevance at appropriately

spaced intervals to reinforce learning thatwill have occurred prior to the previouslesson or lessons. For example, Rosenshine(1986, 2009) advises weekly and monthlyreview. In earlier advice, Good and Grouws(1979) suggested teachers review theprevious week’s work every Monday and theprevious month’s work every fourth Monday.It is also important to recognise that thevalue of review depends heavily on thequality of the instructional processes thathave occurred before it (Stein et al., 1997).That is, students will require high qualityprior knowledge and a meaningful skill-setthat the spaced review is designed to rein-force. Spacing is also considered a form of‘desirable difficulty’ (Bjork, 1994) in that itstretches a student beyond immediate repeti-tion (that is less difficult) to a moredemanding act of review at a later time(more difficult; see also Bjork & Allen, 1970;Cepeda, Pashler, Vul, Wixted & Rohrer,2006). For example, it may be relatively easyto recall or reproduce algebraic knowledgeimmediately following work on algebra prob-lems. However, recalling or reproducingalgebraic knowledge one or more weeks laterrequires more effortful and demandingcognitive application and processing.

ModellingBandura (1997, 2001) makes clear the yieldsof students observing efficacious behaviourby relevant/significant others to assist thedevelopment of their own efficacy. Modellingrelevant behaviours and processes by teachersis thus a means for developing students’ effi-cacy. Thus, for example, teachers mightdemonstrate in a passage of text how theywould use procedural prompts to summarisekey and relevant information in that text(Hunter, 1984; Rosenshine, 1995). Anothermodelling strategy is for the teacher toengage in ‘think aloud’ exercises. This allowsthe novice (student) to observe how anexpert (teacher) thinks through a processthat is otherwise hidden from the student(Rosenshine, 1995; see also Biggs & Telfer,1987). The novice is then better able to repro-

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duce that function and thereby build efficacyfollowing from this sense of competence.Modelling can be adapted in the class-

room to make the most of the opportunitiesa typical classroom composition may offer.For example, in addition to the teacherengaging in think-aloud exercises to provideinsight into how an expert thinks through amathematics problem (for example), theremay be opportunities for more advancedlearners to also provide think aloud exam-ples as they work alongside novice learners(Rosenshine, 1995). Or, more developedlearners may demonstrate to novices howthey read a comprehension passage usingprocedural prompts (such as ‘who’, ‘what’,‘when’, ‘why’) to comprehend or summariseit. In each case, modelling is used to buildefficacy in the novice learner. As describedbelow, worked examples (Sweller, 2012) canplay a similar role in modelling a problemsolving procedure.

Valuing (school and schoolwork)Central to students’ valuing (of school andschoolwork) is their view that school is rele-vant, useful, meaningful, connected to theirlives now and/or in the future. Students’valuing of academic subject matter, tasks, andactivities also relies on the perceived personalrelevance, importance and utility of thatmaterial (Wigfield & Eccles, 2000). Valuing isfurther developed through connectionsstudents can make between prior and currentlearning and also between learning andlarger issues that have broader importanceand relevance (Martin, 2010). Threeelements of LRI have potential to promotethese processes and yields: integrating, organ-ising segments, and personalising.

IntegratingIntegrating stimuli is one strategy that can beused to promote connections betweendifferent facets of the learning task. Themore connections students can see amongtasks and subject matter, the greater theirsense of relevance with regards to the

learning material or task (Martin, 2003,2010). For example, punctuation is oftentaught in isolation from students’ editing oftheir own essays and assessment tasks. In suchcases, an opportunity to build a sense of rele-vance with regards to punctuation is lost.Integration might involve students beingpresented with an explicit punctuation checklist (e.g. capitalise the start of a sentence, endquestions with a question mark etc.) that theywork through after they have written anessay. Thus, there is structured and scaf-folded support for punctuation built into thestudent’s own essay writing activity thatincreases the perceived relevance andpersonal meaning associated with the punc-tuation activity. Notably, integration is the reverse of some

approaches to pre-training and segmentingdescribed above, especially with regards tothe ‘isolated elements effect’ (Pollock et al.,2002). Whether elements should be isolatedor integrated depends on available workingmemory resources that in turn depend onlevels of knowledge (Sweller, 2012) – furtherunderscoring the importance of pre-trainingif and when needed. Notwithstanding this, asa general principle, integration of informa-tion, materials, and/or activities allowsstudents to better appreciate importantconnections in learning and thus the value ofthe relevant information, materials, andactivities for other parts of their learning.

Organising information thematicallyPre-training and integration are focused onconnections among specific elements ofsubject matter. Thus, they are focused onrelatively ‘local’ and proximal connections.Valuing of school and subject matter is alsoachieved by connecting to ‘big ideas’ andmore general or indeed, global issues. Byconnecting school to broader issues andphenomena outside of school, school is moremeaningfully located in a broader scheme,again enhancing its perceived relevance(Martin, 2010). This can involve instructionvia identification of and guidance using a

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‘big idea’ (Stein et al., 1998). In history, for example, by using a

‘problem-solution’ theme many historicalevents can be taught for better under-standing, learning, and relevance. Helpingstudents understand the causes underlyinghistorical events such as war can be assistedthrough unifying segmented informationunder a ‘problem (multiple possible causes:e.g. economic, religious, human rights) –solution (multiple solutions: e.g. war,migrating, tolerating, innovating)’ frame-work (Stein et al., 1998). Another applicationin history involves the ‘problem-solution-effect” theme that ‘people and governmentsare reacting to problems, that the causes ofthose problems are small in number; andthere are a few common solutions to thoseproblems’ (Kinder & Bursuck, 1992, p.29).Kinder and Bursuck then analyse World WarI and World War II to highlight this theme.Graphic organisers such as concept maps canalso be helpful to link to big themes andideas that more meaningfully connectacademic subject matter to the broaderworld. These display segments in a way thatmake clear the link between the instructionalmaterial and a ‘big idea’ central to thecourse, topic, or task (Rosenshine, 1995). In each of these examples, there is a ‘big

idea’ that is a basis for effectively organisinginstructional material (also helpful forworking and long-term memory storage andprocessing; Mayer & Moreno, 2010) andmaking explicit connections betweenacademic subject matter and these broaderand potentially universal themes. Theseconnections improve students’ valuing ofschool and schoolwork (McInerney, 2000). Aswith integration, thematic organisation is thereverse of some approaches to pre-trainingdescribed above, especially with regards tothe ‘isolated elements effect’ (Pollock et al.,2002). Again, whether elements should beisolated or positioned under a ‘big idea’ willdepend on available working memoryresources (Sweller, 2012) – again emphasisingthe importance of pre-training andsegmenting if and when needed.

PersonalisingPersonal relevance is central to learners’valuing of subject matter (and subjects andschool more broadly; Martin, 2010; McIn-erney, 2000). Material and information canbe presented in a way that better draws thelearner into the activity and fosters personalmeaning and connection with that materialand information. The ‘personalisation prin-ciple’ holds that learners receiving informa-tion in a more personalised way will learnmore than those receiving information in amore detached, objective, and unnecessarily-formal way. Thus, instructions such as, ‘Yourgoal in this task is to …’ leads to more mean-ingful learning than instructions such as,‘The goal for this task is to …’. Recent meta-analysis supports this principle (Ginns,Martin & Marsh, 2013), finding that person-alisation of subject matter and tasks is associ-ated with perceived friendliness, effectivecognitive processing, and significant reten-tion and transfer. Similarly, experimentalstudies have also shown the positive impacton learning and motivation through inclu-sion of personally-relevant facts into instruc-tion (Cordova & Lepper, 1996; Ku & Sullivan,2002; Walkington, 2013).

Mastery orientationMastery orientation is very much concernedwith students’ focus on the task at hand,focus on learning and understanding, andthe effort required in the process (Midgley,Kaplan, Middleton & Maehr, 1998). Relativeto performance orientation (that is focusedon reward, competition, external andcomparative standards), mastery orientationreflects more an intrinsic approach andorientation with a focus on and immersionin the inherent properties of tasks andlearning (Anderman & Wolters, 2006; Elliot,2005; Linnenbrink-Garcia, Tyson & Patall,2008; Maehr & Zusho, 2009). Relative toperformance goals, mastery goals tend to beassociated with greater learning and engage-ment outcomes (Martin & Elliot, 2016; Yu &Martin, 2014). Notwithstanding this, theeffects of each orientation may differ

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according to where a student is at in thelearning process. For example, performanceorientation may be adaptive for surfacelearning of facts and content, whereasmastery orientation may be superior fordeeper and inferential learning (e.g.Linnenbrink-Garcia et al., 2008). In anycase, the literature generally supports themerits of mastery over performance orienta-tion in the learning process (Elliot, 2005;Maehr & Zusho, 2009; but see Senko &Miles, 2008). To the extent that this is thecase, the use of signalling, independentpractice, deliberate practice, and guideddiscovery learning are suggested as LRIelements that may align with or promotestudents’ mastery orientation.

SignallingOften learning material is complex andcannot be structured in a way to minimise oravoid cognitive load. In such cases, otherinstructional devices can be used. Signallingis one such device that involves providingcues to help the learner locate and focus onthe essential material in a lesson or activity(referred to as the ‘signalling principle’;Mayer & Moreno, 2010; see also De Koning,Tabbers, Rikers & Paas, 2009). For example,the teacher may ask the students to watch outfor a particular event or character in a plot,the teacher may place an emphasis on partic-ular words in the instructional process, head-ings may be used to orient the learner to animportant idea, highlighters may be used toorient the learner to key words and concepts,or advance organisers may be developed thatmake clear at the outset what major conceptsor activities are to be addressed (Mayer &Moreno, 2010). Each practice is aimed at reducing cogni-

tive load by eliminating the need for thelearner to search for relevant or essentialmaterial. Importantly, from a motivationperspective, signalling also makes explicit thetask-relevant information and demands, theimportance of focusing on task elements,and emphasising task and learning concerns(as distinct from performance concerns). In

these ways, it is suggested that signalling isfostering mastery orientation as well.

Independent practiceA major aim of explicit, structured, andsupported instruction is to develop somelevel of fluency and automaticity in learning.When fluency and automaticity are devel-oped, knowledge and skills have beencommitted to long-term memory and thelearner can access and produce this materialrelatively rapidly and with relative ease. It isat this stage the learner is now ready for moreindependent application. In the early stages of independent prac-

tice, this process is confined to the material,tasks, and activities that have been the focus ofLRI (Rosenshine, 1986, 2009). For example, ifstudents are learning about the componentsof a paragraph in an essay (e.g. comprising agood opening sentence, relevant specificdetail and evidence and argument, and asummative or linking sentence to close theparagraph), this is the focus of independentpractice. It is also important for the teacher tomove around the classroom to monitorstudents’ independent practice (Hunter,1984). Rosenshine (1986) suggests that ifcontact with the student is necessary duringthis stage, it should be kept brief, averaging nomore than 30 seconds for each interaction.Again, the emphasis is on independence.From a cognitive perspective, this rein-

forces fluency and automaticity. Notably,from a motivation perspective, independentpractice also promotes an autonomysupportive learning environment. In theeducational context, autonomy supportrefers to climates and instruction thatpromote students’ volition, autonomy, andintrinsic motivation (Collie, Shapka, Perry &Martin, 2015; Deci & Ryan, 2012; Ryan &Deci, 2000). These are elements aligned withor contributing to students’ mastery orienta-tion given the emphasis of mastery on indi-vidual and personal motivation (Martin &Elliot, 2016). Notably, however, theautonomy-promotive elements of inde-pendent practice are likely to hold only to

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the extent that they foster a mastery orienta-tion. If students are asked to independentlypractice without a good reason or rationale,or when the practice is mindless, they mayfail to make the connection between effortand outcome (violating a critical conceptunder mastery orientation; e.g. Elliot, 2005),and they may not personally endorse theactivity (violating their autonomy, also acritical concept under mastery orientationand intrinsic motivation; Collie et al., 2015;Deci & Ryan, 2012).

Deliberate practiceDeliberate practice refers to rehearsal rele-vant to a specific skill that is correctable. Itusually involves repetition and feedback andat critical points it is conducted by thestudent on his/her own (Nandagopal & Eric-sson, 2012; Purdie & Ellis, 2005). Skills areoften practiced under close supervision of ateacher and activities are well-defined, goal-directed and involve substantial feedback(Nandagopal & Ericsson, 2012). Accordingto Hattie (2012), ‘deliberate practicerequires concentration, and someone (eitherthe student, or a teacher, or a coach) moni-toring and providing feedback during thepractice’ (p.110). Deliberate practice isdifferent from mindless drill. Mindless drillmight involve students writing manycomplete essays in order to finesse theiressay’s introduction or other specific aspectsof the essay. Doing so does not expose themto enough targeted practice needed tomaster the introduction itself. Deliberatepractice would involve specific rehearsal withappropriate constructive feedback on theintroduction alone (see also Ericsson, 2014;Ericsson & Pool, 2016).According to Nuthall (1999), students

need about four exposures to content (nomore than two days apart) to sufficiently inte-grate it into their knowledge structure (seealso Marzano, 2003). It is evident, then, thatdeliberate practice is not necessarily acomfortable process. Inevitably, it createsdissonance between where a learnercurrently sits and a level of performance,

automaticity, and fluency to which he/sheaspires. This ‘requires full concentration andis effortful to maintain for extended periods.Students do not engage in deliberate prac-tice because it is inherently enjoyable, butbecause it helps them improve theirperformance’ (Van Gog et al., 2005, p.75). When the relevant skills are mastered, the

student is better able to engage in solitarypractice of activities, setting their own goalsand practice routines, and learning how topace and self-manage through this process.Further, deliberate practice helps to foster amastery orientation by reminding studentsthat their practice efforts are linked to theirperformance outcomes. When practice isdeliberate, mastery orientation is empha-sized because students set practice aims andmake the connection between their effortsand outcomes by monitoring their progresstowards practice goals.

Guided discovery learningLiem and Martin (2013) suggest that aftersufficient direct input and guided, inde-pendent, and deliberate practice, there isthen a place for guided discovery learning.That is, having moved students through theindependent and deliberate practice phaseand being satisfied that they have masteredthe material and its attendant processes, theteacher then transitions students into aguided discovery learning phase. Now thatlearners have progressed beyond novicestatus, they possess the sufficient skills andconcepts to engage in more open-endeddiscovery approaches. Proponents of explicitapproaches to instruction thus recognise thatguided discovery has a vital place in thelearning process (e.g. Mayer, 2004). Forexample, having mastered one paragraphduring independent learning, students maynow be asked to write two linked paragraphsintegrating the various skills or processeslearnt during guided and independent prac-tice. Or, it may involve the application ofone’s learning to ‘real-world’ problems (e.g.Van den Heuvel-Panhuizen & Drijvers, 2014)with appropriate support as needed.

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Guided discovery learning also entails amodest elevation in task challenge andbrings into consideration concepts such as‘desirable difficulty’ (Bjork, 1994) thatsuggests appropriate points in the learningprocess where more difficult tasks lead togreater learning than continued presenta-tion of easy tasks. Indeed, this notion of grad-uated challenge is consistent with findings inother lines of educational research. Forexample, research into personal best (PB) orgrowth goals has articulated the role ofsetting personally challenging targets (Elliot,Murayama, Kobeisy & Lichtendfeld, 2014;Elliot, Murayama & Pekrun, 2011; Martin &Elliot, 2016; Martin & Liem, 2010; Yu &Martin, 2014). Findings suggest that studentswho set personally challenging goals evinceadaptive patterns of motivation, engage-ment, and achievement (Martin & Elliot,2016; Martin & Liem, 2010; Yu & Martin,2014). The ‘Goldilocks effect’ is also alignedwith this notion of optimal difficulty andchallenge. This refers to individuals’ prefer-ence to attend to tasks and activities that areneither too easy nor too difficult (Kagan,1990). Guided discovery learning is wellsuited to this principle.From a motivation perspective, inde-

pendent practice lays a foundation forautonomy support, intrinsic motivation, andhence, mastery orientation. Guideddiscovery learning provides another opportu-nity to immerse students in the intrinsic andinherent properties of the task, therebyfurther developing their mastery orientation.Moreover, the very clear emphasis ondiscovery rather than performance furtherdistances students from a performanceorientation and more closely locates them inmastery-oriented terrain.

Planning (and monitoring) and taskmanagementPlanning and task management are verymuch concerned with students’ self-regu-lated learning skills (Zimmerman, 2002).These functions, residing under the self-regulatory umbrella, rely on students’

capacity to organise material, pace theirlearning appropriately, identify and attend tothe steps involved in learning, self-monitorand appropriately adjust as required (Martin,2007, 2009, 2010). Mental practice, guidedpractice, and worked examples are proposedas elements of LRI that have potential toenhance students’ planning, monitoring,and task management.

Mental practiceRelated to deliberate practice is the processof ‘mental practice’ (sometimes referred toas the ‘imagination effect’; Sweller, 2012).Here, learners are asked to imagine ormentally rehearse a concept or procedure.The mental rehearsal occurs in workingmemory and this assists in the transfer ofinformation to long-term memory byconstructing and automating schemata(Sweller, 2012). Research asking students tostudy a worked example and then to turnaway and rehearse the example in their mindfound these students performed better thanthe students who studied worked examplesbut were not asked to further mentallyconsider the concept (Sweller, 2012). The ‘planning and monitoring’ compo-

nent of the Wheel relies on the learner’scapacity to mentally represent the variousdemands before him/her. This mental repre-sentation might involve the components of aparticular task or the key parts of a scheduleof activities (Martin, 2010). Further, theextent to which learners are able to monitortheir progress will very much depend on howwell this representation is stored in long-termmemory. Mental practice may be an idealmeans of helping learners better mentallyrepresent what they are required to do andthe steps involved in doing it – all key to plan-ning and monitoring from a motivation andengagement perspective.

Worked examplesWorked examples involve presenting newmaterial to learners with completed samplesof work that show how a particular problemcan be solved or how a task can be completed.

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Teachers would ask students to studynumerous worked examples showing howdifferent types of problems can be solved.Research shows that worked examples helplearners acquire schemas that they can thenapply to solve problems quickly and effi-ciently (Atkinson et al., 2000; Renkle, 2014;Renkl & Atkinson, 2010; Rosenshine, 1986,1995, 2009; Sweller, 2012). Worked examplesmight include fully worked mathematics solu-tions, sample essays, and completed sciencepracticum reports. In their review of instruc-tional methods, Lee and Anderson (2012)were struck by the power of providing exam-ples of problem solutions to assist learning.Indeed, they went so far as to suggest thatdiscovery-based approaches are effective tothe extent that they are example-based. As learning develops, the student is

presented with partially completed workedexamples to solve (referred to as the“problem completion effect”; Sweller, 2012).Ultimately, the worked examples are fullyfaded and learners are ready for completelyunworked tasks and problem solving (Mayer& Moreno, 2010; Sweller, 2012) that may beideal for guided discovery opportunities. It is also the case that more developed

learners (experts) do not need such substan-tial exposure to worked examples. They maystudy just one worked example beforeproceeding to a partially worked example, orto a fully unworked problem itself (Sweller,2012). The ‘guidance fading effect’ (or‘guided activity principle’) is apparent whenthe effectiveness of worked examples slowlyfades, requiring learners to complete moreof the problem task themselves to extendlearning (Moreno & Mayer, 2010; Renkl &Atkinson, 2010; Sweller, 2012). Research has also identified the effective-

ness of teachers eliciting students’ self-expla-nations of what they are doing or why theyhave selected a particular response as theyengage in partially completed examples.Asking for self-explanations during partiallycompleted worked examples takes advan-tage of the reduced cognitive load (and freed cognitive capacity) created by

the worked example (Renkl & Atkinson,2010; Rosenshine, 1986, 2009). This hasbeen referred to as ‘self-explanation’ or the‘reflection principle’ which helps learnersconnect new learning with prior knowledge inlong-term memory (Moreno & Mayer, 2010).Not only are worked examples effective in

enhancing long-term memory and easing theload on working memory as new informationor tasks are learned, they are also effective inpromoting planning, monitoring, and taskmanagement. Specifically, worked examplesexplicitly identify the components of a taskthat the learner will need to plan for in theirown task completion, emphasise theelements that are important to monitor inorder to stay on task, and provide a clearersense of what components and processes areinvolved in order to effectively manage thetask demands.

Guided practiceA related process is guided practice (Hunter,1984). Here students are systematicallyguided through the steps of learning orproblem solution. This can involveprompting responses through a task,providing part of a solution for a student tocomplete, or being readily available for ques-tions and guidance at each step (Rosenshine,1986, 2009). Importantly, it seems thatteachers should strive to ensure a reasonablyhigh success rate during this process, with theoptimal success rate on assigned tasks or activ-ities approximately 75-80 per cent duringguided practice. Thus, the teacher’s task is tocombine success with reasonable challenge(Rosenshine, 1986, 2009). In so doing, thestudent moves through learning material at areasonable pace, experiences efficacy ashe/she progresses, but makes sufficienterrors to enable corrective feedback and newlearning (Martin, 2007). As with workedexamples, guided practice makes explicit thecomponents of a task to be performed orlearning to be achieved. Knowing thesecomponents is important for a student’scapacity to plan what he/she is to accomplishthrough the task, monitor and pace through

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the task, and manage the process to comple-tion – again, all critical foundations for plan-ning, monitoring, and task management.

PersistencePersistence refers to students’ continuedefforts in the face of large tasks, task diffi-culty, initial error or misunderstanding, oruncertainty as to the requirements or steps ina task (Martin, 2007, 2010; Miller, Greene,Montalvo, Ravindran & Nichols, 1996). LRIstrategies that teachers might implement toenhance and sustain students’ persistenceinclude: checking for understanding, usingtemplates, and using procedural prompts.These are aimed at keeping students effica-ciously involved in the process (e.g. byensuring they understand), and ensuringthey have a clear understanding of taskrequirements and what is required to persistthrough them.

Checking for understandingAccording to Hattie (2009, 2012), effectiveteachers tend to see assessment as an oppor-tunity for feedback to them about the effec-tiveness of their pedagogy. Similarly,Rosenshine (1986, 2009; see also Hunter,1984) reports that effective teachers dedicateample time to checking for student under-standing and engage in checking strategiesthat are qualitatively superior to otherteachers. For example, they will frequentlypose questions, ask students to summarisemajor points, repeat explanations and direc-tions, and ask students’ opinions on subjectmatter as it is taught. These teachers tend notto ask non-specific questions (such as ‘Arethere any questions’ or ‘Who doesn’t under-stand?’) and tend not to call on volunteers tocheck for student learning. Instead, they willask questions to individual students andthese questions are appropriately tailored(by difficulty or substance) to each student tomore authentically gauge understanding(Rosenshine, 1986, 2009). Some have suggested using simple tools

to check student understanding as the lessonproceeds. For example, students might

record quick responses to teacher questionson small white boards for the teacher toknow if he/she can proceed or if some re-teaching is required (DeRuvo, 2009). Simi-larly, the ‘traffic light’ formative assessmentsignalling method is another widely advo-cated and implemented technique (Black,Harrison & Lee, 2004). Here, studentspresent a red card to indicate ‘I don’t under-stand’ or ‘I need help’, a yellow card to indi-cate ‘I think I understand’ or ‘I may need abit of help’, and a green card to indicate ‘Iunderstand’. DeRuvo (2009) suggests that tokeep a brisk pace, it may also be appropriateto allow brief or abbreviated answers as theaim is often to simply check for under-standing, not require students to articulatefull responses. Adams and Engelmann (1996) have

provided guidelines on acceptable levels ofaccuracy that can be a basis for checking thatstudents have sufficiently understood. Theysuggest teachers check that students are atleast 70 per cent correct on core informationand knowledge from the preceding lessonand nearly 100 per cent correct on coreinformation and knowledge presented inthat lesson. However, these guidelines mayvary depending on the student and thesubject matter. Others have suggested morefrequent intra-lesson assessment to check forstudent understanding. Black and colleagues(2004; see also Black & Wiliam, 2004), forexample, found substantial gains from intra-lesson formative assessment and feedback inmathematics and science. ‘Rapid formativeassessment’ (Wiliam, 2011) has also beensuggested three to five times each week (seealso Hattie, 2012).Collectively, these efforts are aimed at

ensuring students remain on task, are in touch with the run of the lesson and under-stand what is being taught, therebyenhancing engagement and connectionthrough the task or lesson and reducing thepotential inclination to give up, lose track, orswitch off. Accordingly, persistence through atask and through a lesson is promoted. Theseefforts may also foster the belief that persist-

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ence leads to results. This belief may be animportant regulatory mechanism that guidesstudents to persist, indeed, suggesting some-thing of a ‘persistence self-concept’ or ‘persis-tence schema’ that may further promoteperseverance through a task or lesson.

Templates Sometimes persistence is a problem forstudents when they get stuck or lost midwaythrough a task. In such cases, it may be usefulto provide templates for students to checktheir own progress. For example, studentscan have difficulty editing their own work andmay abandon efforts to do so (Collie, Martin& Scott-Curwood, 2015). Templates are mate-rials formatted or structured to help thelearner stay on track or that list the importantfeatures of an essay or report to include. Thismay be a checklist that asks the student tocheck that each sentence begins with a capitalletter, all sentences end with a punctuationmark, proper nouns are capitalised etc. (Steinet al., 1998). The student checks off eachelement as it is completed and this checklistmay also be submitted with the essay or reportfor assessment. A similar strategy has beensuggested by Van Merriënboer (1992) using‘process worksheets’ that lists the stepsinvolved in completing tasks or solving prob-lems. As students work through the process,they check off each step as it is achieved.Some teachers may write abbreviated instruc-tions in bullet-form on the board for studentsto refer to as they progress (DeRuvo, 2009).In each case, there is a mechanism in place toassist a student through a task to completion.In so doing, the teacher has promoted astudent’s persistence.

PromptsProcedural prompts have been suggested byRosenshine (1995; see also Purdie & Ellis,2005) as a cognitive strategy that helpslearners to persist with and complete lessstructured tasks such as those found incomprehension and writing activities. Themost common procedural prompts are wordssuch as ‘what’, ‘who’, ‘why’, and ‘when’ that

help students extract specific informationfrom text and provide prompts they can useto articulate an answer or response. This toois aimed at facilitating effort and persistencein the face of blockages that can arise in thecourse of learning and task completion.

Anxiety, failure avoidance, and self-handicappingAnxiety is associated with reduced or limitedworking memory span (Ashcraft & Kirk,2001; Eysenck & Calvo, 1992). It has beensuggested that intrusive thoughts, distrac-tions, frustration, and negative emotional/affective experiences may act as a source ofextraneous cognitive load and tap the limitedcapacity of working memory (referred to asthe ‘processing efficiency theory’; Eysenck &Calvo, 1992; see also Fraser, Huffman, Ma,Sobczak, McIlwrick, Wright & McLaughlin,2014; Kalyuga, 2011). Further, it has beensuggested that anxiety operates much like adual task setting, comprising a preoccupa-tion with one’s fears as well as a resource-demanding secondary task (Ashcraft &Krause, 2007). Thus, alongside anxiety is theissue of fear of failure (or, failure avoidanceconcerns) that may pervade a task, poten-tially further burdening working memory. Researchers have also identified that

anxiety and fear of failure underpinstudents’ tendency to self-handicap. Self-handicapping refers to self-defeating behav-iour (e.g. procrastination, wasting time,investing little or no effort) that can providea self-worth protecting excuse or alibi in theevent of poor performance (Baumeister &Scher, 1988; Covington, 1992, 1998, 2000;Martin, Marsh & Debus, 2001a, 2001b, 2003;Martin, Marsh, Williamson & Debus, 2003;Midgley, Arunkumar & Urdan, 1996; Rhode-walt & Davison, 1986; Thompson, 1994). Ithas been established that poor performancerisks a threat to one’s self-worth, particularlyif that poor performance is seen as due to alack of ability (Covington, 2000). Thus, whena student is anxious or fearful that he/shemay fail a task, the student may strategicallymanoeuvre so that the poor performance is

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seen as due to a lack of effort (not so threat-ening to self-worth) rather than a lack ofability (threatening to self-worth). Motivation researchers have identified

educational intervention strategies andapproaches to alleviate anxiety, fear offailure, and self-handicapping (e.g.Covington, 1992, 2000; Martin, 2007, 2010;McInerney, 2000; McInerney, Marsh & McIn-erney, 1999; Pintrich & DeGroot, 1990).However, the connection between anxiety,fear of failure, self-handicapping andworking memory suggests LRI approaches(that reduce load on working memory) mayalso play a part in addressing these maladap-tive factors. Alongside the numerous strate-gies described above that are aimed at easingworking memory or improving processingbetween working and long-term memory,reducing split attention in a task as well asintegrating information sequencing areother approaches to reduce the burden onworking memory. In so doing, the teachermay also assist in reducing anxiety, fear offailure, and the consequent motive to self-handicap. Or, it may be that even if a studentdoes experience the negative impact ofanxiety on working memory, effective uses ofLRI will reduce this impact.

Reducing split-attentionLRI very much rests on learning material thatis carefully structured by the teacher. Whenmaterial is poorly structured, there can be excessive load on working memory,thereby impeding learning – and potentiallyincreasing anxiety and fear of failure thatmay sow the seeds of self-handicapping(Covington, 2000; Thompson, 1994). The‘split attention effect’ represents one waymaterial can be poorly structured. Here,information to solve a problem is presentedin more than one area of the learning space(Ginns, 2006; Mayer & Moreno, 2010;Sweller, 2012). For example, a diagram ispresented at the top of a page or screen andexplanatory material required to interpretthe diagram is presented elsewhere on thepage or screen. Working memory is strained

because the learner must hold informationin working memory from one part of thelearning space to understand the material inthe other part of the learning space. Thissplits the attention capacity, is inefficient,and increases cognitive load (Sweller, 2012)that may elevate anxiety. It is thereforeimportant for material to be integrated wher-ever possible – not only to reduce cognitiveload for learning, but also to reduce anxietyand fear processes. For example, the mathematics teacher

might integrate the equation for finding anangle into the angle itself. Or, the scienceteacher may integrate a physics equation intoa problem statement (Sweller, 2012). Struc-turing learning material and processesmindful of split attention effects (andmodality effects, see below) is particularlycritical to novices and students of lowerability (Sweller, 2012). When students arebeginning to learn new concepts, workingmemory comes under most strain and thusinstructional design should place emphasison strategies that reduce load on workingmemory (Sweller, 2012).

Information integration sequencingJust as material presented at different places inthe learning space can split attention and over-load working memory, material presented atdifferent points in time can also burdenworking memory (referred to as the ‘temporalcontiguity effect’; Mayer & Moreno, 2010). Forexample, in a multimedia exercise demon-strating lightning, if the first part of the instruc-tion provides a narration of how lightening isformed and this is then followed by an anima-tion of that process, this requires the learner tohold one piece of information (the narration)in working memory to then integrate with thenext piece of information (the animation).Integrating narration and animation into theone piece of information removes this exces-sive load. This would involve providing narra-tion to accompany each part of the animationas it is presented. In this case, information inte-gration sequencing would help reduce load onworking memory. To the extent it reduces such

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load, it also has potential to reduce anxiety andfear that may develop as the learner strugglesto manage the excessive cognitive demands.

Uncertain controlUncertain control reflects a student’s uncer-tainty as to how to perform a task, uncertaintyas to whether his/her efforts will lead tosuccess, a lack of perceived autonomy, and apotential sense of helplessness that may ariseas a result of this uncertainty and lack ofautonomy (Abramson, Seligman & Teasdale,1978; Connell, 1985; Martin, 2007, 2010;Skinner, 1996; Weiner, 1985). Motivationalintervention aimed at promoting a sense ofcontrol involves encouraging students to seethe connection between their effort andstrategy (both controllable elements of theirschoolwork) and academic outcomes. Devel-opments in self-determination theory (SDT)have also identified the important role ofstructure in autonomy-supportive environ-ments (e.g. Reeve, Deci & Ryan, 2004;Sierens, Vansteenkiste, Goossens, Soenens &Dochy, 2009). A sense of control can befurther enhanced by providing feedback ineffective and consistent ways. This ofteninvolves task-based feedback on students’work that is clear about how they can improve(Craven, Marsh & Debus, 1991; Martin et al.,2001b). Numerous LRI approaches are alsoeffective in providing a greater sense of howto accomplish tasks, being autonomy-supportive, and providing feedback that isaimed at enhancing clarity and performance.Two approaches discussed here are show-casing and feedback (and feedforward).

ShowcasingLRI is geared to taking the mystery out of whatgood work is and how to do it. There are manyopportunities for teachers to showcase exam-ples of good practices and good work that canprovide clarity to students and enhance theirsense of control through a task. DeRuvo(2009), for example, suggests explicit instruc-tion on teaching students how to take notes inclass. This might involve giving five-minuteinstruction on a concept and then distributing

a sample notes page that shows what informa-tion has been recorded and how to record itquickly and accurately. The teacher then pres-ents the five-minute instruction again, butmore slowly as students study the notesrecorded on the page. During this instruction, students might

also be taught how to use symbols and short-hand for common words such as ‘and’,‘since/because’, ‘change’, ‘therefore’,‘between’, ‘increase/decrease’, and the like.Indeed, a table of these might also beprovided and exercises assigned for thestudent to practice and memorise these inorder to automate them in long-termmemory. Here, students’ sense of control isbuilt by showing them how to perform thecore academic task of note-taking andautomating this for future application. Asstudents’ academic lives are increasinglydigital and technological, similar suchapproaches may be adapted to showcase howto type effective class notes on their laptopsor tablets in class. Indeed, this somewhat structured

approach is not inconsistent with suggestionsunder SDT that have identified the impor-tance of assistive structure in promotingautonomy-supportive environments that inturn promote students’ sense of autonomy(Reeve et al., 2004; Sierens et al., 2009), oneindicant of perceived control (Skinner,1996). In fact, Sierens et al. found that theinteraction between structure and autonomysupport (high structure, high autonomysupport) leads to enhanced engagement,suggesting an important synergy betweenstructure, motivation, and engagement.Showcasing can also involve students

closely studying samples of good work. Forexample, teachers may provide all studentswith a copy of an excellent (anonymised)science practicum report from the previousyear. The teacher then dedicates a lesson tounpacking each section of the report, identi-fying why and how the report is an excellentwork sample. The teacher might thenpresent a partially worked example of ascience report and ask students to complete

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this worked example to practice key compo-nents. Here, a sense of control is built byshowcasing good work, identifying keyelements of good work, and having studentsengage in practice that helps automate theskills involved.

Feedback and feedforwardIn one way or another, feedback represents amajor part of LRI-oriented models. It is alsoestablished as a major means by whichstudents’ sense of control can be developed.This is because feedback provides diagnosticinformation on what students have done,makes clear what elements are to be retainedgoing forward, and what needs to beimproved in subsequent tasks (Martin, 2010).Outcomes are further optimised when theprovision of feedback is matched by thelearner’s willingness and capacity to receiveand act on the feedback (Algiraigri, 2014).In addition, following the positive link

between structure and students’ sense ofautonomy (an indicant of perceived control;Skinner, 1996) described above, Sierens et al.(2007) have suggested that feedback isanother means by which students’ autonomycan be promoted: teachers building appro-priate structure into their lessons tend to doso via competence-relevant feedback andfeedback that communicates a confidence instudents’ capacity to achieve on subsequentlearning tasks and activities (Connell, 1990;Reeve et al., 2004). Indeed, given this future-oriented dimension to feedback, the term‘feed-forward’ has been suggested (e.g. Basso& Belardinelli, 2006; Dowrick, Kim-Rupnow& Power, 2006; Dowrick, Tallman & Connor,2005).Moreno and Mayer (2010) report that

feedback providing: (a) information on thecorrectness of an answer and (b) informationon how performance can be improved leadsto better performance and motivation. Kulikand Kulik (1979) found that immediate feed-back is ideal and that doing further study andassessment if performance does not reach apre-determined criterion is also ideal in the

feedback process.Schute (2008) conducted a wide-ranging

review of feedback, deriving the followingrecommendations from analysis of theoryand research: focus feedback on the task, notthe learner; provide elaborated feedback inorder to enhance learning; present elabo-rated feedback in manageable segments;ensure feedback is clear and specific; providefeedback that is as simple as possible, but nosimpler (the latter will be based on learnerneeds and instructional constraints); deliverfeedback that is unbiased, objective, andideally in written form or via computer; and,promote a motivation to attain mastery viathe feedback. In all cases, the objective is tocreate greater task-related clarity about thelearner’s performance in one task (thus,feedback) and provide greater clarity abouthow to perform the next task (thus, feedfor-ward). In so doing, learners develop a height-ened sense of control.In Schute’s (2008) review, guidance was

also provided on how to administer feedbackfor different types of learners. For highachievers: delayed feedback, facilitative (notdirective) feedback, or verification feedback(i.e. whether they are on track) may beappropriate. For low achievers: immediatefeedback, specific feedback, directive orcorrective feedback, scaffolded (supporting)feedback, and elaborated feedback (i.e. whythey are correct) are more appropriate. Rosenshine (1986, 2009) also suggested

how feedback can be useful for differentia-tion in the classroom. If a student is correctand confident, the teacher can respond ‘verygood’ and move on to maintain themomentum of practice and the developmentof automaticity. If the student is correct buthesitant, or has a history of difficulty, theteacher may confirm the answer is correctbut then also provide process feedback thatexplains how or why the answer is correct.For students who have made an error orcontinually struggle, the teacher might notonly provide feedback, but also simplify thequestion, provide hints and prompts, or

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reteach the material.In sum, these feedback (and feedfor-

ward) efforts and suggestions can providestudents with important diagnostic informa-tion about what they have done, make salientwhat knowledge and skills are to be retainedgoing forward, and what needs to beimproved in subsequent tasks (Martin,2010). In so doing, they provide clarity anddirection to students that are important forpromoting perceived control.

DisengagementDisengagement is complex and can arise formany reasons (Finn & Zimmer, 2013). It maybe that the student lacks particular skills in adomain such as literacy or numeracy, or self-regulation skills such as study and organisa-tional skills (Covington, 1992, 2000). In somecases there are motivational problems such aslow self-efficacy (Bandura, 2001), low valuingof the domain or tasks within it (Wigfield &Eccles, 2000), or uncertain control leading tohelplessness (Abramson et al., 1978; Weiner,1985). From a cognitive psychology perspec-tive, it may be a function of the instruction ortask itself that over-burdens some learners’cognitive capacity or renders the instructionalmaterial uninteresting and repetitious,leading to abandonment of effort (Sweller,2012). Approaches under the LRI umbrellacan be a means of addressing many of thesefactors that can underpin disengagement.Here the discussion centres on usingdifferent modalities, avoiding redundancy,increasing coherence, and providing appro-priate instructional time.

Using different modalitiesLearners can be cognitively exhaustedthrough having to attend to information in away that burdens a particular processor. Forexample, if too much information ispresented visually (e.g. via text, call-outboxes, a diagram, a table etc.), the visualprocessor reaches capacity and so the learnermust direct increasing energy to maintain it(Mayer & Moreno, 2010; Sweller, 2012). This

excessive load risks the learner struggling tokeep up and disengaging from the task(indeed, the split-attention effect may alsoexcessively burden the learner in such ways).Rather than overloading the visual processorwith, say, an image and text, some of theinformation can be offloaded onto the audi-tory processor as audible narrative (Mayer &Moreno, 2010; Sweller, 2012). Thus, wherethere is diverse material available, theeducator might present different pieces ofmaterial in a different modality such as animage with a narrative that learners can listento (referred to as the ‘modality effect’; Ginns,2005; Penney, 1989). Importantly, however,the information across modes must bedifferent – simply repeating the same infor-mation in written and narrated form is ineffi-cient and redundant. It is also apparent thatany verbal or narrated information must beconcise so as not to overload the auditoryprocessor (Sweller, 2012).In an adaptation of the modality effect,

Moreno and Mayer (2010) identified the‘multimedia principle’ as one with particularyield for novice learners. For learners withlow prior knowledge, presenting material indual modes (e.g. text and illustrations ornarration and animation) can result in moremeaningful learning. Because novices do nothave prior knowledge to guide processing ofnew information, they may be assisted byadditional modality to help structure infor-mation in working memory (Moreno &Mayer, 2010). On the other hand, expertstend not to need this additional modality.

Avoiding redundancy and increasing coherenceAs noted above, it is important that materialis not presented in a way that renders someof it redundant. Presenting the same infor-mation twice requires the learner to recon-cile the two incoming sources of informationand this adds to the processing required byworking memory (Mayer & Moreno, 2010). Italso runs the risk of rendering the instruc-tional material uninteresting and repetitious,

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thereby increasing the risk of disengagementfrom the task. For example, if there is a self-evident

diagram presented, there is not a need for anexplanatory text alongside it. In this case, thetext is redundant and interferes with cogni-tive capacity (referred to as the ‘redundancyeffect’; Mayer & Moreno, 2010; Sweller,2012). Thus, in a diagram on blood circula-tion in the heart, lungs and body, there canbe arrows indicating the passage of blood –but not also statements below the diagramproviding the same information about bloodflow. The diagram is intelligible without thestatements below it. It is important to also distinguish between

redundancy (which is ineffective) fromrehearsal and repetition (which are effec-tive). Redundancy involves presenting thesame and unnecessary material simultane-ously (which overloads working memory).Rehearsal involves presenting the same orsimilar material successively (which does notoverload working memory; Sweller, 2012).Notwithstanding this, for some learnersredundancy may be appropriate. Forexample, non-English speaking backgroundstudents may benefit from the same materialpresented via text and narration. Obviouslyalso, students with disabilities particular tothe modality will also benefit from redun-dancy; hearing impaired students, forexample, require that the same informationis visually presented (Mayer & Moreno,2010). In fact, more generally, Mayer andJohnson (2008) have also provided evidencethat a small amount of redundancy in multi-media learning can support learning.It is also important to organise material so

that extraneous or overly elaborate materialthat may be tangential to essential learning isreduced or removed (Marzano, 2003; Purdie& Ellis, 2005). Presenting only the essentialinformation to allow the full capacity ofworking memory to process it is referred toas the ‘coherence principle’ (Mayer &Moreno, 2010). Sometimes in efforts to makethings interesting for learners, teachers maypresent sound effects or video break-outs.

However, these added elements may be extra-neous to the essential learning required andthus run the risk of burdening andexhausting the working memory that isrequired for the central learning (Mayer &Moreno, 2010). This is because informationthat is essential and should be presentedexplicitly to novices, becomes redundant formore knowledgeable learners – and thusreduced and then excluded. As relevant tomotivation and engagement, emphasizingand presenting the essential information tolearners identifies the key components ofwhat is to be learned or accomplished andreduces the risk of rendering the instruc-tional material uninteresting and repetitious,thereby reducing the risk of disengagementfrom the task.A necessary first step in establishing

coherence is for the teacher to clearly differ-entiate the content and skills students must master from the content and skills not so necessary to master. This involvesestablishing a hierarchy of essential contentand skill (Marzano, 2003). Instructionalapproaches then revolve around this essen-tial material, giving careful thought to whatadded elements may distract or burden thelearner. Thus, there are clear cognitive yieldsthrough optimizing coherence.

Allowing appropriate instructional timeEstimates of how much instructional timestudents receive in class vary, with some as lowas 21 per cent of class time and some as highas 69 per cent (Marzano, 2003 for a review).Using the lower bound, approximately 1–2hours is devoted to instruction each day.Using the upper estimate, students receiveapproximately 3–4 hours instruction per day.This is a substantial difference in instruc-tional time and, according to Marzano (2003)plays a major role in whether students getclose to covering the full standards-drivencurriculum. To the extent that some studentsdo not cover the curriculum, their relativeperformance is likely to decline and thiselevates the risk of disengagement(Covington, 2000; Finn, 1989).

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LRI recognises there is a need for suffi-cient instructional time in a given task, unit,or topic. Effective teachers tend to generatemore instructional time that is spentproviding additional explanations, assigningmore examples, and checking for under-standing more frequently and deeply(Evertson, Anderson, Anderson & Brophy,1980). This also means sufficient time todevelop fluency and automaticity beforemoving on to independent practice andguided discovery learning. In contrast, lesseffective teachers tend to have less instruc-tional time, provide shorter presentations,explanations and examples, and have lesstime to develop fluency and automaticitybefore moving students on to independentpractice (Rosenshine, 1986, 2009). A lack ofappropriate instructional time and prepara-tion increases the risk of disengagementfrom the task or unit.There are two ways that instructional time

can be increased. The first is in terms of howmuch instructional time occurs in a lessonand across the day. This requires the schoolleadership to closely consider how the schoolday is organised and the scheduling of lessontime and order. It also requires teachers tominimise disruptions within the lesson inorder to optimise actual instructional time.The second is in terms of specific teacher-ledinstructional moments. For example, in deter-mining appropriate teacher-led instructionaltime, it has been suggested that teacherspresent for about 8–10 minutes before anypractice activity (Rosenshine, 1986, 2009).Others have suggested the age-to-minute rule:here, for example, a teacher would present forno more than 11 minutes for 11-year-olds or15 minutes for 15-year-olds (Martin, 2010).There would then be an appropriately timedand guided application for students tocomplete, at which point they would return tothe teacher for further instructional input.Taken together, across lesson scheduling

and specific teacher-led instructionalmoments, it is important that students havegreater access to curriculum material, havemore time to cover this material, and

receive appropriate time and direction fromthe expert (the teacher) as they move fromnovice status to become more developedlearners. This helps students keep up withcurriculum demands and subject matter asit is taught, thereby reducing the potentialfor disengagement.

Synthesis and implementation ofmotivation, engagement, and LRIelementsThe preceding discussion has been aimed ataddressing motivation and engagementfactors salient in the literature and identi-fying well recognised elements of LRIapproaches that align with or are conduciveto the development of these factors. Thisbeing the case, Table 2a, Table 2b, andFigure 4 now synthesise what was presentedin Table 1 and the subsequent analysis ofmotivation, engagement, and LRI.Although Table 2a, Table 2b, and Figure 4

are organised factor by factor and approachby approach, this organisation is notintended to be prescriptive; rather, it isindicative of what type of LRI approachesmight be considered for different motivationand engagement dimensions. Thus, forexample, some LRI elements identified asrelevant to addressing uncertain control (e.g.showcasing and feedback/feedforward) mayalso be effective in promoting students’persistence and self-efficacy.Also, the range of motivation and engage-

ment factors and the range of LRI approachesin Table 2a, Table 2b, and Figure 4 are notintended to be exhaustive or definitive.Indeed, other motivation and engagementframeworks and operationalizations (e.g. viaPALS by Midgley et al., 1997; the MSLQ byPintrich et al., 1991; the SEI by Appleton et al.,2006; the ISM by McInerney et al., 2000) willemphasise some different factors. In addition,diverse branches of cognitive and instructionalpsychology (e.g. Adams & Engelmann, 1996;Mayer & Moreno, 2010; Sweller, 2012) willemphasise different aspects of instruction thatrequire distinct approaches to accommodatingworking and long-term memory.

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Using Load Reduction Instruction (LRI) to boost motivation and engagement

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Andrew J. Martin

Adaptive Motivation and Adaptive Engagement(and indicative LRI elements)

Self-efficacy� Pre-training� Segmenting information� Preliminary and spaced reviews� Modelling important processes

Valuing� Integrating� Organising information thematically� Personalising

Mastery orientation� Signalling� Independent practice� Deliberate practice� Guided discovery learning

Planning (and monitoring) and task management� Mental practice� Worked examples� Guided practice

Persistence� Checking for understanding� Providing templates � Prompting

Table 2a: Potential integration of Adaptive Motivation and Engagement Wheel Factors with Indicative Load Reduction Instruction (LRI) elements.

Maladaptive Motivation and Maladaptive Engagement(and indicative LRI elements)

Anxiety, failure avoidance, and self-handicapping� Reducing split-attention� Information integration sequencing

Uncertain control� Showcasing� Feedback and feedforward

Disengagement� Using different modalities� Avoiding redundancy and increasing coherence� Allowing appropriate instructional time

Table 2b: Potential integration of Maladaptive Motivation and Engagement Wheel Factors with indicative Load Reduction Instruction (LRI) elements.

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Using Load Reduction Instruction (LRI) to boost motivation and engagement

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Marzano (2003) also makes the impor-tant point that not all elements of an instruc-tional taxonomy must be in the one lesson.Accordingly, it is not the intention that allLRI elements of Table 2a, Table 2b, andFigure 4 are implemented in the one lesson.Marzano suggests spreading a given instruc-tional taxonomy or framework across alearning unit (not across one lesson). Hesuggests Bloom’s (1956, 1976) learning unitsas one way to consider this approach. Basedon Bloom’s analysis, students encounterabout 150 separate learning units in a year(about 7 hours each), which would translateinto about 20–30 learning units per year ineach major course. LRI taxonomies (e.g.Hunter, 1984; Marzano, 2003; Rosenshine,1986, 2009) and integrative frameworks suchas in Table 2a, Table 2b, and Figure 4 mightbe applied across one of these units. Forexample, in a given learning unit, somelessons (probably the early lessons) willemphasise pre-training, modelling,templates, worked examples and deliberatepractice, while other lessons (probably thelater lessons) will emphasise independentpractice and guided discovery learning.Across the span of a whole learning unit in agiven school subject, then, the teacher wouldlook to implement a range of LRI elementsto support broad and deep learning and arange of motivation and engagement factors.This more distributed approach to LRI

not only eliminates the pressure on theteacher to traverse all instructional elementsin one lesson; it also provides further opportunity for the teacher to exert profes-

sional judgement on how to distribute theelements across a learning unit. Hence,counter to criticisms that LRI approachesreduce teachers to mechanical practices thatconstrain their professional input, thisdistributed approach to explicit taxonomiesrelies on the teacher to engage in profes-sional decision-making as to what is imple-mented and when to implement it.According to Rosenshine (1986, 2009),

LRI-oriented frameworks can be readilyadapted in the comprehensive classroom.For the novice learner, LRI might be appliedin small steps with more frequent practiceand more guidance and support from theteacher. For the expert learner, the presenta-tion by the teacher can be longer, requiringless time in practice, less guidance from theteacher, less time spent checking for under-standing, and more independent practiceaway from the teacher. But even for theexpert learner, when the material is new,complex or hierarchically structured, there isa return to the more explicit LRI elements(e.g. pre-training, worked examples etc.) asnew learning develops. Similarly, for less ablestudents, Rosenshine (1986, 2009) suggestsmore review, less presentation, more guidedpractice, and more independent practice; formore able students, he suggests less review,more presentation, less guided practice, andless independent practice (see also Adams &Engelmann, 1996; DeRuvo, 2009; Hunter,1984; Jones & Southern, 2003; Magliaro et al., 2005; Marzano, 2003; Stein, Silbert &Carnine, 1997).

PART 4. LOAD REDUCTIONINSTRUCTION AND THE BROADERPROCESS OF LEARNINGThis review is focused on instruction thatreduces cognitive load on students. Asdetailed thus far, alongside quite explicit and directional approaches to instruction,there are discovery- and inquiry-orientedapproaches that can also reduce the cognitiveburden on students as they learn. Accordingly,guided practice, independent practice, andguided discovery learning were considered.These approaches are aimed at promotinglearner independence while managing cogni-tive load appropriately, depending on thelearner’s novice/expert status. Although theseapproaches were addressed in Part 3, furtherconsideration is given to them here with a viewto better understanding their role in thebroader process of learning and how theyconnect to LRI.

Guided discovery learningConstructivist approaches to educationalinstruction give emphasis to learning environments that are rich in discovery and exploratory opportunities, prioritisestudents’ own construction of meaning, andemphasise students’ exploration and devel-opment of concepts for themselves (Pressleyet al., 2003). The teacher’s role tends to bemore as facilitator, responsive to the studentas he/she autonomously explores issues andsolves problems (Ausubel, 1961; Bruner,1961; Pressley et al., 2003). Indeed, Hattie(2009, 2012) has made the distinctionbetween teacher as ‘facilitator’ (typicallyassociated with constructivist approaches)and teacher as ‘activator’ (more aligned withexplicit approaches).Liem and Martin (2013; see also Pressley

et al., 2003) emphasised the differencebetween pure discovery learning (predomi-nantly unsupported and unassisted inde-pendent learning) and guided discoverylearning (predominantly scaffolded,supported, monitored, assisted independentlearning). They also note that the effects ofguided discovery learning tend to be positive

when learners are more skilled and knowl-edgeable (see also Kalyuga, Chandler,Tuovinen & Sweller, 2001). This is becauseguided discovery learning (implicitly orexplicitly) recognises the limits of workingmemory, the need for accommodatingworking memory to build up long-termmemory, and the substantial burden thatpure discovery places on working memory(Kirshner et al., 2006; Paas et al., 2003;Sweller, 1988; Winne & Nesbit, 2010). Indeed, naïve emphasis on pure discovery

learning has led to some frustration amongresearchers: ‘Like some zombie that keepsreturning from its grave, pure discoverycontinues to have its advocates. However,anyone who takes an evidence-basedapproach to educational practice must askthe same question: Where is the evidencethat it works? In spite of calls for freediscovery in every decade, the supportingevidence is hard to find’ (Mayer, 2004, p.17). The role of guidance in the discovery

process is particularly important because it isa further means by which the instructor canreduce the load on working memory (Martin,2013, 2015). To the extent that this is thecase, guided discovery learning is also acomponent of LRI. If too much of the processremains undefined and uncertain, too muchof working memory must then be directed to potentially distracting and irrelevantprocesses that have the capacity to lead tomisinterpretation, inaccurate conclusions,and inadequate skill development. If theinstructor provides some guiding principles,prior information, signposts along the way,and scaffolds and assistance where needed,there is less burden on working memory.Thus, students are not denied the opportu-nity for discovery. Having developed the skillsand subject-matter knowledge, these studentsare well positioned to engage in the discoveryprocess. This inclusion of guided discoverylearning under LRI is now discussed.

Load Reduction Instruction and guideddiscovery learningIn recent years there has been something of

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a tussle between predominantly construc-tivist (and post-modernist) approaches toinstruction and more (post) positivist explicit and direct approaches to instruction.Interpretations of the former have led tostudent-centred learning, discovery andenquiry-based approaches, with the teacherseen more as a facilitator of learning. Thelatter (explicit) approach has been charac-terised as more teacher-centred, focused on explicit and structured instruction(including some deliberate practice anddrill). For a recent review of this debate, seeTobias and Duffy (2009). It is suggested that across the learning

process, students’ learning, motivation, andengagement are optimised by the teacherbeing both activator (through explicitapproaches) and facilitator (through guideddiscovery approaches). To see the two roles(and instructional approaches) as incompat-ible and mutually exclusive is to set in place afalse dichotomy. The two are compatiblewhen: (a) we consider all the stages oflearning involved when moving from noviceto expert status and (b) guided discovery is ameans to help manage the cognitive load onthe learner in this process. Having developed requisite knowledge and

skills in long-term memory and havingreduced the burden on working memory,learners can then be encouraged to apply theacquired knowledge and skill in independent,novel, and creative ways. Liem and Martin(2013) speculated that some of the low to moderate effect sizes associated withexploratory- and discovery-oriented learning(see their review and Hattie, 2009) may be aresult of these learning practices being imple-mented too early in the learning process. Liemand Martin (2013) suggest that after sufficientdirect input, guided practice and independentdemonstration of learning, there is a criticalrole for guided discovery learning. Thus, having moved beyond novice status,

the learner now has the skills and requisiteknowledge to engage in discovery-orientedapproaches. Or, from a cognitive perspective,having acquired the skill and knowledge in

long-term memory, and automated this skilland knowledge, there is no longer such a loadon working memory. Learners’ workingmemory can then be used to apply the knowl-edge and skill (that is long-term memory) inpotentially new and self-determined ways. Thisnotion lies at the heart of LRI.Notably, research has confirmed that once

learners become expert, they benefit morefrom problem solving approaches than fromstructured and explicit approaches to learning(e.g. Kalyuga, 2007; Kalyuga, Ayres, Chandler& Sweller, 2003; Kalyuga et al., 2001). If astudent knows how to solve a problem, but stillneeds to practice solving such a problem toincrease automation, it actually increases theirworking memory load to read through aworked example. In this case it is easier to solvethe problem oneself through practice thanread the worked example.In addition, for experts and students who

have mastered basic material, well-knownlimits on working memory fade faster thanfor novices and students who are not on topof the academic subject matter. For example,split attention effects disappear as expertiseand mastery develop (referred to as the‘expert reversal effect’; Kalyuga et al., 2003).Because these students have acquired suffi-cient prior knowledge, fluency and/or auto-maticity, working memory is no longerplaced under the typical strain experiencedby the novice learner. In such cases, morecomplex material can be presented to thenow expert learner. Similarly, expert learnersdo not benefit from presenting accompa-nying information in dual modalities – theyare able to learn efficiently through onemodality (e.g. just a diagram, or just a narra-tion; Sweller, 2012).

A proposed process of explicitinstruction and guided discovery learningTaken together, there comes a point in thelearning process and learner developmentwhen more complexity, novelty, and inde-pendence are not only desirable, but essen-tial for further learning (Mayer, 2004). Assummarised by Liem and Martin, ‘it seems

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constructivist approaches are better assistedby direct and structured input from theteacher that systematically and unambigu-ously builds the knowledge and skills neededto subsequently engage in meaningfuldiscovery, problem-based, and enquiry-basedlearning’ (2013, p.368). Indeed, this concept is not unfamiliar to

cognitive load researchers who also recog-nise that there is a need to distinguishbetween the optimal learning conditions forthe novice learner and the conditions thatare appropriate for more developed learnersin complex tasks. For example, Kalyuga and Singh (2015) outlined an approach that sought to smooth the typically rigiddichotomisation of explicit and discovery-oriented approaches. They suggested a moreflexible approach based on differentiatingspecific goals of various learner activities incomplex learning.Figures 5a and 5b illustrate the proposed

sequence of instruction that optimally drawson explicit through to guided inquiry,discovery, and exploratory learning. Impor-tantly, the effectiveness of each mode reliesheavily on recognition of the novice or expertstatus of the learner – and by implication, thestatus of their working memory, long-termmemory, and their fluency and automaticityat each stage of the learning sequence. Figure 5a is a general model of the LRI

process and pertains to most learners(including those lower in ability). Theselearners require ample time, attention, andresources directed at the explicit instruc-tional stage in order to lay a solid foundationfor a guided exploratory and discovery phase. Figure 5b is a high ability/expert model

of the LRI process, where relatively less time,attention, and resources are directed at theexplicit instructional stage as these learnersprogress more rapidly to a guidedexploratory and discovery phase. Althoughthe expert learner does not spend as muchtime as the novice in the explicit phase, timeengaged in this phase is nonetheless neces-sary for the expert. Thus, for both groups,explicit instruction and guided discovery are

considered necessary and desirable elementsof the LRI process.

Student-centred instruction, student-centred exchange, and student-centredlearningThis process may also be considered in termsof ‘student-centred instruction’, ‘student-centred exchange’, and ‘student-centredlearning’. Here the teacher is responsible forthe organisation and presentation of instruc-tional material with a clear and present focuson students’ needs, including their cognitiveneeds (student-centred instruction). Guidedpractice, questioning, worked examples, andchecking for understanding take placefollowing the teacher’s initial instruction(student-centred exchange). Then, withappropriate monitoring by the teacher (asneeded and appropriate), the student isresponsible for independent practice,checking and reviewing his/her own work,and engaging in further discovery or explo-ration (student-centred learning). This aligns with the recent ‘I do’, ‘We do’,

‘You do’ approach to instruction (Archer &Hughes, 2011; see DeRuvo, 2009 for asummary in relation to at-risk students). Thestudent-centred instruction corresponds tothe ‘I do’ phase. The student-centredexchange corresponds to the ‘We do’ phase.The student-centred learning corresponds tothe ‘You do’ phase. McWilliam (2009) offers related insight

into this process, identifying the teacherinitially as the ‘Sage on the stage’. Then in amore interactive and creative instructionalphase, the teacher is the ‘Meddler in themiddle’. Learning then progresses to a pointwhere the teacher is the ‘Guide on the side’. Of course, numerous pedagogical frame-

works incorporate similar such processes,with ‘gradual release of responsibility’,‘balanced instruction’, and ‘enhanceddiscovery learning’ models (e.g. Alfieri et al.,2011; Fisher & Frey, 2008; Marzano, 2011;Maynes et al., 2010; Pearson & Gallagher,1983; Pressley & Allington, 2014) beingamong the more well recognised ones. The

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point is that at different stages of the educa-tional process, teacher and student will playdifferent roles, moving from (a) studentinstructional salience to (b) more distributedteacher-student interaction to (c) studentlearning salience. This pattern of instructionand learning plays out at each point studentsencounter new and/or challenging skill andcontent that are to be mastered. Notwith-standing these alignments with LRI, LRI isdistinct in its development around cognitiveload concepts and the core need to appropri-ately reduce or manage the cognitive burdenon students to optimise their learning.Figures 6a and 6b illustrate the process of

student-centred instruction (‘I do’; ‘Sage onthe stage’), student-centred exchange (‘Youdo’; ‘Meddler in the middle’), and student-centred learning (‘We do’; ‘Guide on the

side’). Again, however, the effectiveness ofeach approach relies heavily on recognitionof the novel or expert status of the learner –and by implication, the status of theirworking memory, long-term memory, andtheir fluency and automaticity at each stageof the learning sequence. Figure 6a is a general model of the

student-centred instruction, student-centredexchange, and student-centred learningprocess. It pertains to most learners(including those lower in ability). Theselearners require ample time, attention, andresources directed at the student-centredinstruction (‘I do’) phase in order for theteacher to get a sense of their understandingand learning at the student-centredexchange (‘We do’) phase. Once satisfiedwith students’ understanding and learning at

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Explicit

Direct

Instructive

Exploratory

Discovery

Inquiry

Relatively more time dedicated to this phase,

as appropriate tolearner’s novice

status

Relatively less time dedicated to this phase,

as appropriate tolearner’s novice

statusFigure 5a: General LRI process – From explicit to exploratory,

direct to discovery, and instructive to inquiry.

this stage, there is an opportunity forstudent-centred learning (‘You do’). Figure 6b is a high ability/expert model

of the student-centred instruction, student-centred exchange, and student-centredlearning process. Here, relatively less time,attention, and resources are directed at thestudent-centred instruction (‘I do’) phase asthese learners progress more rapidly tostudent-centred exchange (‘We do’) andstudent-centred learning (‘You do’) phases.However, although the expert learner doesnot spend so much time in the ‘I do’(student-centred instruction) phase, sometime here is nonetheless necessary at keypoints of learning. Thus, for learners in thegeneral and high ability/expert models,student-centred instruction, student-centredexchange, and student-centred learning are

considered necessary and desirable elementsof the learning process. Importantly also, whereas most students

in the classroom are across the subject mattertowards the end of the ‘We do’ phase and areready to move to the ‘You do’ phase of inde-pendent practice, it is also likely that there isa minority of students who require furtherLRI (Martin, 2015). The ‘You do’ phase – inwhich the bulk of the class is engaged in inde-pendent practice – is an ideal opportunity forthese students to receive additional and one-on-one support from the teacher (or on occa-sion where appropriate, from expert peers).This ‘I do’, ‘We do’, ‘You do’ process is thusfurther effective because it also allows forindividualised and one-on-one opportunitieswith at-risk students in the class.

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Explicit

Direct

Instructive

Exploratory

Discovery

Inquiry

Relatively less time dedicated to this phase,

as appropriate tolearner’s expert

status

Relatively more time dedicated to this phase,

as appropriate tolearner’s expert

statusFigure 5b: High Ability/Expert LRI process – From explicit to exploratory,

direct to discovery, and instructive to inquiry.

A cycle of Load Reduction Instructionand academic motivation andengagementThe present review has identified the potentialfor LRI approaches to foster and facilitatestudents’ motivation and engagement. Ofcourse, this connection is not static. Researchshows there is a cycle that operates such thatlearning (‘skill’) fosters subsequent motivationand engagement (‘will’) (Covington, 1992,1998; Marsh, 2007; Marsh & Martin, 2011;Martin, 2007, 2009, 2010; Pintrich, 2000). Forexample, self-efficacy is likely to be enhanced(or sustained) through the academic knowl-edge and skill that explicit instruction is shownto develop. Similarly, self-efficacy is associatedwith enhanced academic knowledge andacademic skill (Schunk & Miller, 2002).Students who are high in self-efficacy generatealternative courses of action when at first theydo not succeed, invest greater effort andpersistence, and are better at adapting to

problem situations (Bandura, 1997). Accord-ingly, they tend to achieve more highly(Schunk & Miller, 2002). There is thus a reciprocal relationship

between students’ academic motivation andengagement on the one hand, and theiracademic learning and achievement on theother hand. These reciprocal effects havebeen demonstrated in various motivationliteratures (e.g. see Marsh, 2007; Marsh &Martin, 2011 for summaries). Indeed, in thecognitive literature it is recognised thatincreases in motivation can increase thecognitive resources devoted to a task (Paas etal., 2003). To the extent that LRI is relevantto achievement (e.g. Cromley & Byrnes,2009; Lee & Anderson, 2013; Liem & Martin,2013; Mayer, 2004; Sweller, 2012) and tomotivation and engagement (as proposed inthis review), it is a further opportunity topromote the synergistic and mutually rein-forcing relationship between achievement

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!

!!

Predominantly didactic; delivered to accommodate learner needs (‘I do’; ‘Sage on the stage’)

!

1. Student-centred instruction

2. Student-centred exchange

Guided discovery (‘You do’; ‘Guide

on the side’)

3. Student-centred learning

Interactive, questions, short tasks etc. to check

understanding (‘We do’; ‘Meddler in

the middle’)

+ Opportunity for teacher one-on-one with at-risk students

Relatively more time dedicated to the ‘I do’ and ‘We do’ phase,

as appropriate to learner’s novice status

Figure 6a: General LRI Model: Student-centred instruction (‘I do’; ‘Sage on the stage’); Student-centred exchange (‘You do’; ‘Meddler in the middle’), and Student-centred learning (‘We do’; ‘Guide on the side’).

and motivation and engagement.The cycle might also be considered along

the lines of an aptitude-treatment interaction(ATI). This concept holds that some instruc-tional strategies are more (or less) effectivefor some individuals more than others,depending on their ability or other aptitudedimensions (Cronbach & Snow, 1977; Snow,1991). When instruction is appropriatelymatched to the aptitudes of the learner,optimal learning takes place. One of the mostcommon examples of an ATI involves instruc-tion that differs in structure and complete-ness for high and low ability students. Highability students can learn with less structureand less complete instruction (though, eventhese students require structure andcompleteness in the early stages of learning;Adams & Engelmann, 1996; Rosenshine,1986, 2008, 2009), whereas lower abilitystudents have a greater need to learn undermore highly structured instruction with well-

defined sequences and components (Snow,1991). Motivation and engagement might beconsidered another lens through which toconsider ATIs with respect to LRI. LRI (thetreatment) may be an effective means ofboosting academic outcomes for students lowin motivation and engagement (the apti-tude). Thus, akin to students low in ability,students low in motivation and engagementmay benefit from some key LRI elements. Forexample, along the lines of Table 2a, studentslow in self-efficacy may benefit from anemphasis on pre-training, segmented infor-mation, preliminary and spaced reviews, andmodelling by the teacher. Following fromthis, enhanced outcomes may reflect opti-mised conditions that enable the student tomove from novice to developed learner andthus benefit from the full scope of thelearning and instructional process: fromexplicit instruction to guided discoverylearning (see Figures 5 and 6).

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!

!!!

Didactic; accommodate learner needs

(‘I do’; ‘Sage on the Stage’)

1. Student-centred instruction

2. Student-centred exchange

3. Student-centred learning

!!

!!

Relatively more time dedicated to the ‘We do’ and ‘You do’ phase, as appropriate

to learner’s expert status

Guided discovery (‘You do’; ‘Guide

on the side’)

Interactive, questions, short tasks etc. to check

understanding (‘We do’; ‘Meddler in

the middle’)

Figure 6b: High Ability/Expert LRI Model: Student-centred instruction (‘I do’; ‘Sage on the stage’); Student-centred exchange (‘You do’; ‘Meddler in the middle’),

and student-centred learning (‘We do’; ‘Guide on the side’)

PART 5: OPPORTUNITIES FORFUTURE RESEARCHThis review has brought together two majorliteratures around cognitive psychology andeducational psychology. Theory and researchwith regards to both have led to articulationof a proposed nexus between Load Reduc-tion Instruction (LRI) and students’ motiva-tion and engagement. As relatively littledirect and formal consideration has beengiven to this nexus, many of the argumentsand contentions put forward require directand formal empirical consideration. Thereare thus many opportunities for furtherresearch as we seek to gain a more completeunderstanding of how LRI and students’motivation and engagement connect.As noted in the review, some LRI research

has incorporated motivation and engagementfactors and considerations and some motiva-tion and engagement research has incorpo-rated LRI factors and considerations. However,relatively little research has examined the twodirectly, seeking to map well-established moti-vation and engagement factors and theories towell-established LRI principles. Following from this, the linking of LRI

strategies under distinct motivation andengagement factors (Table 2a, Table 2b,Figure 4) is indicative and suggestive, notprescriptive or definitive. An empirical ques-tion is thus to ascertain which specific LRIstrategies might explain most variance indistinct motivation and engagement factors.Findings from these investigations will nodoubt illuminate and qualify some of thelinks suggested herein. Findings would alsoprovide a more specific and concrete basisfor educational practice and intervention.The motivation and engagement factors

under focus in this discussion are also indica-tive, not prescriptive, definitive, or exhaus-tive. The Motivation and Engagement Wheelwas deemed a useful lens through which toconsider the present issues because it trav-erses (a) numerous salient and well-estab-lished motivation and engagement theories,(b) cognitive, affective, and behaviouralfactors, and (c) adaptive and maladaptive

dimensions (Liem & Martin, 2012; Martin,2007, 2009). It was thus an encompassingframework with which to consider LRI implications for students’ motivation andengagement. Nevertheless, there are otherframeworks and models that would beequally beneficial in considering, includingmultidimensional motivation and engage-ment frameworks reflected in the Patterns ofAdaptive Learning Survey (PALS) by Midgleyand colleagues (1997), the Motivated Strate-gies for Learning Questionnaire by Pintrichet al. (1991), the Student EngagementInstrument (SEI) by Appleton et al. (2006),and the Inventory of School Motivation(ISM) by McInerney et al. (2000). Thus,research going forward has a range of moti-vation and engagement frameworks fromwhich to choose.These motivation and engagement frame-

works also typically employ instrumentationthat meets recognised measurement stan-dards in that their scales tend to be multi-item, reliable, and validated against relevantexternal correlates. It is not uncommon forthe cognitive psychology (including cogni-tive load) research to employ instrumenta-tion that does not meet recognisedmeasurement standards (e.g. single-itemmeasures are frequently employed). Thus,when bringing LRI and student motivationand engagement together, researchers canbenefit from the long-standing tradition ofsound measurement that is predominant inthe motivation and engagement literature. Given much of this discussion has centred

on the learning process and the progressionfrom novice to developed learner (expert), itwould be helpful to explore for any shifts inmotivation and engagement as learningimproves. This brings into considerationreal-time motivation and engagementresearch through the LRI and learningprocess. Preliminary motivation and engage-ment research has been done based on theMotivation and Engagement Wheel factors(Martin, Papworth, Ginns, Malmberg, Collie& Calvo, 2015; see also Malmberg, Woolgar &Martin, 2013), but not in relation to LRI and

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the learning that occurs through a given taskin real-time.As noted at the outset of this review,

school is academically demanding andbecomes more so as students move fromelementary to middle to high school (Martin,2015). It has been shown that motivation andengagement decline over this time (Eccles etal., 1993; Eccles & Midgley, 1989; Martin,2009). Thus, there are developmental issuesas learners progress through the school years.We might therefore ask how LRI relates tomotivation and engagement over this time.Following from answers to this question, whatadjustments in LRI might need to occur froma developmental perspective?From an evolutionary psychology

perspective, there is emerging theory andresearch formally testing the implications ofbiologically primary and secondary knowl-edge for working memory and learning. Ithas been suggested that biologically primaryknowledge (e.g. communicating, moving;Geary, 2007, 2008a, 2008b) is not such aburden on working memory (Paas & Sweller,2012) and thus education that harnessesbiologically primary knowledge may relievesome burden on working memory in orderfor learners to better acquire biologicallysecondary knowledge (e.g. mathematics,science, history). Some of the most recentwork in this area has examined the role ofmovement (biologically primary knowledge)and embodied cognition in learning, findingfor example, that tracing material canenhance the subsequent reproduction (i.e.learning) of that material (e.g. Hu, Ginns &Bobis, 2015; Macken & Ginns, 2014). Indeed,for students with known executive functionimpairments – such as those with ADHD –movement in the form of physical activity,and allowing some fidgeting or squirmingwhile learning has been associated withenhanced working memory (e.g. Hartanto,Krafft, Iosif & Schweitzer, 2016; Sarver,Rapport, Kofler, Raiker & Friedman, 2015). Given that biologically primary knowledge

is seen as typically unconscious, effortless andrapid – and something that we evolve to

acquire naturally (Geary, 2007, 2008a, 2008b;hence, low burden on working memory), wemight speculate about its relationship to moti-vation. To the extent that biologically primaryknowledge is unconscious and naturallyacquired, is it also inherently and intrinsicallymotivating? To the extent that harnessingbiologically primary knowledge reduces theburden on working memory and can enhancelearning (Paas & Sweller, 2012), might it alsohave desirable motivational properties?Further, is it possible that incorporatingbiologically primary knowledge into learningprocesses and tasks has an additive effect onlearning such that it frees up working memoryfor better learning and is also intrinsicallymotivating for the learner? Extending thisspeculation, to the extent that biologicallyprimary knowledge may be intrinsically moti-vating, what are the implications of biologi-cally secondary knowledge for motivation? Asnoted earlier, there is a known decline inmotivation and engagement as students movefrom elementary to high school (e.g. Eccles etal., 1993; Eccles & Midgley, 1989; Martin,2009) and this may in part be attributed to thegreater emphasis on biologically secondaryknowledge (e.g. mathematics, science, historyetc.) in high school. Research into these ideaswould be illuminating.Whereas most LRI-related research has

focused on the role of LRI in promotinglearning and achievement, the focus of thisreview has been on LRI as relevant tostudents’ motivation and engagement. Itwould be useful to consider the relativesalience of LRI and motivation and engage-ment in promoting achievement. Indeed, itwould also be useful to understand theextent to which the two may work together toproduce more optimal learning and achieve-ment outcomes.As suggested at numerous points through

the discussion, implications of cognitive loadfor academically at-risk learners can be signif-icant. As Martin (2013, 2015) has indicated,there is a need for more motivation andengagement research among academically at-risk learners. Given the pertinence of LRI to

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at-risk learners (e.g. McMullen & Madelaine,2014; Rupley et al., 2009; Swanson & Sachse-Lee, 2000), a suggested program of motiva-tion and engagement research among theselearners might also incorporate LRI consider-ations and what LRI buys such students withregards to motivation and engagement.This review has also emphasised the

importance of appropriately adjusting LRI tothe development and expertise of thelearner. Just as LRI researchers have identi-fied boundary conditions for various LRIpractices and learning outcomes (e.g. via theexpert-reversal effect or the redundancyeffect; Kalyuga & Singh, 2015; Mayer &Moreno, 2010; Sweller, 2012) so too might weidentify boundary conditions for various LRIpractices with regards to motivation andengagement outcomes. There is work sugges-tive of the importance of this research. Forexample, sub-optimally low cognitive loadconditions can lead to boredom (Jackson,Kleitman & Aidman, 2014). Further work isneeded here.There has been some emphasis on the

need to appropriately balance ‘student-centred instruction’ (‘I do’) with ‘student-centred exchange’ (‘We do’) and‘student-centred learning’ (‘You do’)through the instructional process. There is aneed to understand boundary conditionshere as well. For example, what are the moti-vation and engagement implications for toolittle or too much time and attention to anyone of these? Conceivably, too much‘student-centred instruction’ (‘I do’) maylead to boredom and possible disengage-ment, while movement into ‘student-centredlearning’ (‘You do’) when students are notquite ready may lead to anxiety. Figures 5 and6 sought to accommodate this through repre-senting the learning process in ‘general’ and‘high ability/expert’ models – but it remainsan empirical question as to the appropriatetime, attention, and resources directed toeach phase of the learning process fordifferent learners.This review has also emphasised guided

discovery learning as a potential means to

appropriately manage the cognitive burdenon students in the learning process. It wasalso noted that pure discovery learning isrelatively less likely to lead to formal achieve-ment and learning gains (Kirschner et al.,2006; Mayer, 2004). This is because purediscovery learning (that is unsupported,unassisted, and unguided) increases cogni-tive load on the learner, impeding his/herlearning. Notwithstanding this, althoughpure discovery may not be the optimal meansto formal achievement-related ends, it hasbeen considered as a desirable end in itselfand something worthwhile for students toexperience at appropriate points in thelearning process (Bruner, 1961). Hence, thisreview does not discount the possibility thatpure discovery learning may have motivationand engagement yields that are not sodependent on the need to reduce cognitiveload and working memory demands. Futureresearch might seek to juxtapose differentlevels of (un) supported discovery learningand their links to multidimensional motiva-tion and engagement.There is also the issue of what constitutes

optimal guidance – as relevant to learnermotivation and engagement – in guideddiscovery (and similar) phases. Whilst it iseasy to advise that guidance is important,inevitably there will be cognitive load factorsto consider when deciding what, when, andhow much guidance to provide – which willlikely have motivation and engagementimplications for learners. Thus, under-standing the motivation and engagementimplications for different levels of guidanceis important.Following from this, it is important to

recognise a line of research suggesting thatminimal guidance for novices can be effec-tive for their learning. Research into‘productive failure’ is one such channel ofwork. Productive failure involves the designof conditions for learners to persist in gener-ating and exploring solution methods for solving novel, complex problems. Theprocess can initially lead to failure but thisfailure is claimed to provide an inherent effi-

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cacy that is important for learning – providedit is followed by an appropriate form ofinstructional intervention that assists subse-quent solutions and methods (e.g. Kapur,2008). To the extent that this is the case,productive failure research might providedirection for tasks or activities whereminimal guidance for novices is desirable.Thus, the present review emphasises theimportance of clear guidance and structurefor novices in most learning conditions;however, on occasions where relatively littleguidance for novices is intended, productivefailure research might be helpful to set theconditions that optimise learning in theseminimally guided situations.The discussion also identified potential

aptitude-treatment interactions (ATIs) thatmay occur in the learning process, with highability students able to learn with less struc-ture while lower ability students have agreater need to learn under structured

instruction (Snow, 1991). It was suggestedthat motivation and engagement may beanother lens through which to consider ATIswith respect to LRI. To what extent mightLRI (the treatment) be a means of boostingacademic outcomes for students low in moti-vation and engagement (the aptitude)?Finally, LRI, as defined in this review, is

broadly conceptualised. The review has notengaged in much differentiation betweenspecific LRI approaches and the implicationsfor students’ motivation and engagement.There is scope for research seeking to distin-guish motivation and engagement effects as afunction of, for example, direct and explicitinstructional approaches. Both approachesare grouped under the LRI umbrella but aredistinct in important ways (e.g. see Adams &Engelmann, 1996; Kirschner et al., 2006; Liem& Martin, 2013; Rosenshine, 1986, 2008,2009); what implications do these distinctionshold for motivation and engagement?

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CONCLUSIONThe bulk of research into instructional tech-niques that directly or indirectly reducecognitive load (i.e. Load Reduction Instruc-tion; LRI) has focused on academic learningand achievement. Findings support the roleof LRI in students’ learning and achievementgains. Less attention has been given to therole of LRI in promoting students’ motiva-tion and engagement. The present reviewhas harnessed motivation and engagement asa lens through which to consider LRI. It hasexamined key dimensions of motivation andengagement and explored the extent towhich specific approaches and strategiesunder LRI address them. It thus comple-ments the large body of work into LRI and itsachievement yields with closer consideration

of its yields for students’ motivation andengagement. The review has also consideredthe learning process more broadly and high-lighted the role of guided discoveryapproaches in the learning sequence toappropriately manage cognitive load andgenerate greater autonomy and independentlearning. Thus, it is emphasised that LRIencompasses both explicit instructionalapproaches and guided discovery-orientedlearning – and that this has significant impli-cations for students’ academic motivationand engagement. Taken together, educatorswould do well to recognise the motivatingand engaging properties of clear, structuredand well guided instruction, and the role thisplays in students’ learning and achievement.

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ReferencesAbramson, L.Y., Seligman, M.E.P. & Teasdale, J.

(1978). Learned helplessness in humans:Critique and reformulation. Journal of AbnormalPsychology, 87, 49–74. doi.org/10.1037/0021-843X.87.1.49

Adams, G. & Engelmann, S. (1996). Research on DirectInstruction: 25 years beyond DISTAR. Seattle, WA:Educational Achievement Systems.

Ainley, M.D. (2012). Students’ interest andengagement in classroom activities. InChristenson L, Reschly L & Wylie (eds). Handbookof research on student engagement. New York:Springer.

Alfieri, L., Brooks, P.J., Aldrich, N.J. & Tenenbaum,H.R. (2011). Does discovery-based instructionenhance learning? Journal of EducationalPsychology, 103, 1–18. doi.org/10.1037/a0021017

Algiraigri, A.H. (2014). Ten tips for receivingfeedback effectively in clinical practice. MedicalEducation Online, 19.doi.org/10.3402/meo.v19.25141

Anderman, E.M. (2013). Middle school transitions. InJ. Hattie & E.M. Anderman (Eds). Internationalguide to student achievement. Routledge: New York.

Anderman, E.M. & Mueller, C.E. (2010). Middleschool transitions and adolescent development.In J.L. Meece & J.S. Eccles (Eds). Handbook ofresearch on schools, schooling, and humandevelopment. Routledge: New York.

Anderman, E.M. & Patrick, H. (2012). Achievementgoal theory, conceptualization ofability/intelligence, and classroom climate. In S.L. Christenson, A.L. Reschly & C. Wylie (Eds.).Handbook of research on student engagement(pp.173–192). New York, NY: Springer.doi.org/10.1007/978-1-4614-2018-7_8

Anderman, E.M. & Wolters C. (2006). Goals, values,and affects: Influences on student motivation. InP. Alexander & P. Winne. (Eds). Handbook ofeducational psychology. New York: Simon &Schuster/Macmillan.

Appleton, J.J., Christenson, S.L., Kim, D. & Reschly,A.L. (2006). Measuring cognitive andpsychological engagement: Validation of theStudent Engagement Instrument. Journal of SchoolPsychology, 44, 427–445.doi.org/10.1016/j.jsp.2006.04.002

Archer, A.L. & Hughes, C.A. (2011). Explicitinstruction: Effective and efficient teaching. GuilfordPress.

Ashcraft, M.H. & Kirk, E.P. (2001). The relationshipsamong working memory, math anxiety, andperformance. Journal of Experimental Psychology:General, 130, 224–237. doi.org/10.1037/0096-3445.130.2.224

Ashcraft, M.H. & Krause, J.A. (2007). Workingmemory, math performance, and math anxiety.Psychonomic Bulletin and Review, 14, 243–248.doi.org/10.3758/BF03194059

Atkinson, R.K., Derry, S.J., Renkl, A. & Wortham, D.(2000). Learning from examples: Instructionalprinciples from the worked examples research.Review of Educational Research, 70, 181–214doi.org/10.3102/00346543070002181

Ausubel, D.P. (1961). Learning by discovery:Rationale and mystique. Bulletin of the NationalAssociation of Secondary School Principals, 45, 18–58.doi.org/10.1177/019263656104526904

Baddeley, A. (1994). The magical number seven: Stillmagic after all these years? Psychological Review,101, 353–356. doi.org/10.1037/0033-295X.101.2.353

Bandura, A. (1997). Self-efficacy: The exercise of control.New York: Freeman & Co.

Bandura, A. (2001). Social cognitive theory: Anagentic perspective. Annual Review of Psychology,52, 1–26. doi.org/10.1146/annurev.psych.52.1.1

Basso, D. & Belardinelli, M.O. (2006). The role of thefeedforward paradigm in cognitive psychology.Cognitive Processing, 7, 73–88.doi.org/10.1007/s10339-006-0034-1

Baumeister, R.F. & Scher, S.J. (1988). Self-defeatingbehavior patterns among normal individuals:Review and analysis of common self-destructivetendencies. Psychological Bulletin, 104, 3–22.doi.org/10.1037/0033-2909.104.1.3

Beck, A.T. (1976). Cognitive therapy and the emotionaldisorders. New York: New American Library.

Beck, A.T. (1995). Cognitive therapy: Basics and beyond.New York: Guilford.

Bessellieu, F.B., Kozloff, M.A. & Rice, J.S. (2001).Teachers’ perceptions of direct instructionteaching. Direct Instruction News, 1, 14–17

Biggs, J.B. & Telfer, R. (1987). The process of learning.(2nd ed.) Burwood: Prentice-Hall.

Bjork, R.A. (1994). Memory and metamemoryconsiderations in the training of human beings.In J. Metcalfe & A.P. Shimamura (Eds).Metacognition: Knowing about knowing.(pp.185–205). Cambridge, MA: The MIT Press.

Bjork, R.A. & Allen, T.W. (1970). The spacing effect:Consolidation or differential encoding? Journal ofVerbal Learning and Verbal Behavior, 9, 567–572.doi.org/10.1016/S0022-5371(70)80103-7

Black, P., Harrison, C. & Lee, C. (2004). Working insidethe black box: Assessment for learning in the classroom.Granada Learning.doi.org/10.1177/003172170408600105

Black, P. & Wiliam, D. (2004). The formative purpose:Assessment must first promote learning. Yearbookof the National Society for the Study of Education, 103,20–50.doi.org/10.1111/j.1744-7984.2004.tb00047.x

Bloom, B.S. (1956). Taxonomy of educational objectives,Handbook 1: The Cognitive Domain. New York:Longman.

Bloom, B.S. (1976). Human characteristics and schoollearning. New York: McGraw-Hill.

Bodkin-Andrews, G.H., Denson, N. & Bansel, P.(2013). Teacher racism, academic self-concept,and multiculturation: Investigating adaptive andmaladaptive relations with academicdisengagement and self-sabotage for Indigenousand Non-Indigenous Australian students.Australian Psychologist, 48, 226–237.doi.org/10.1111/j.1742-9544.2012.00069.x

Bong, M. (1996). Problems in academic motivationresearch and advantages and disadvantages oftheir solutions. Contemporary EducationalPsychology, 21, 149–165.doi.org/10.1006/ceps.1996.0013

Booth, M.Z. & Gerard, J.M. (2014). Adolescents’stage-environment fit in middle and high school:The relationship between students’ perceptionsof their schools and themselves. Youth and society,46, 735–755.

Borman, G.D., Hewes, G.M., Overman, L.T. & Brown,S. (2003). Comprehensive school reform andachievement: A meta-analysis. Review ofEducational Research, 73, 125–230.doi.org/10.3102/00346543073002125

Bost, L.W. & Riccomini, P.J. (2006). Effectiveinstruction: An inconspicuous strategy fordropout prevention. Remedial and SpecialEducation, 27, 301–311.doi.org/10.1177/07419325060270050501

Bruner, J.S. (1961). The act of discovery. HarvardEducational Review, 31, 21–32.

Bugler, M., McGeown, S.P. & St Clair-Thompson, H.(2015). Gender differences in adolescents' academicmotivation and classroom behavior. EducationalPsychology, 35, 541–556..

Cantwell, D.P. & Baker, L. (1991). Associationbetween attention deficit hyperactivity disorderand learning disorder. Journal of LearningDisabilities, 24, 88–95.doi.org/10.1177/002221949102400205

Carlson, C.D. & Francis, D.J. (2003). Increasing thereading achievement of at-risk children throughdirect instruction: Evaluation of the RodeoInstitute for Teacher Excellence (RITE). Journal ofEducation for Students Placed At Risk, 7, 141–166.doi.org/10.1207/S15327671ESPR0702_3

Carmichael, P., Adkins, Gaal, I., Hutchins, P., Levey,F., McCormack, J., Oberklaid, F., Pearson, C. &Storm, V. (1997). Attention deficit hyperactivitydisorder. National Health and Medical ResearchCouncil, Australia.

Cepeda, N.J., Pashler, H., Vul, E., Wixted, J.T. &Rohrer, D. (2006). Distributed practice in verbalrecall tasks: A review and quantitative synthesis.Psychological Bulletin, 132, 354–380.doi.org/10.1037/0033-2909.132.3.354

Cleary, T.J. & Zimmerman, B.J. (2012). A cyclical self-regulatory account of student engagement:Theoretical foundations and applications. In S.Christenson, A.L. Reschly & Wylie, C. (Eds.),Handbook of research on student engagement(pp.237–258). Springer. doi.org/10.1007/978-1-4614-2018-7_11

Collie, R.J., Martin, A.J. & Scott-Curwood, J. (in press,accepted September 2015). Multidimensionalmotivation and engagement for writing:Construct validation with a sample of boys.Educational Psychology, 36, 1285–1302.

Collie, R.J., Shapka, J.D., Perry, N.E. & Martin, A.J.(2015). Self-determination theory and teachers:Examining well-being, motivation, jobsatisfaction, and organizational commitment.Journal of Educational Psychology.doi.org/10.1037/edu0000088

Connell, J.P. (1985). A new multidimensionalmeasure of children's perceptions of control.Child Development, 56, 1018–1041.doi.org/10.2307/1130113

Cooper, G. & Sweller, J. (1987). Effects of schemaacquisition and rule automation on mathematicalproblem-solving transfer. Journal of EducationalPsychology, 79, 347–362. doi.org/10.1037/0022-0663.79.4.347

Cordova, D.I. & Lepper, M.R. (1996). Intrinsicmotivation and the process of learning:Beneficial effects of contextualization,personalization, and choice. Journal of EducationalPsychology, 88, 715–730. doi.org/10.1037/0022-0663.88.4.715

Covington, M.V. (1992). Making the grade: A self-worthperspective on motivation and school reform.Cambridge: Cambridge University Pressdoi.org/10.1017/CBO9781139173582

Covington, M.V. (1998). The will to learn: A guide formotivating young people. New York: CambridgeUniversity Press.

Covington, M.V. (2000). Goal theory, motivation, andschool achievement: An integrative review.Annual Review of Psychology, 51, 171–200.doi.org/10.1146/annurev.psych.51.1.171

Craven, R.G., Marsh, H.W. & Debus, R.L. (1991).Effects of internally focused feedback andattributional feedback on the enhancement ofacademic self-concept. Journal of EducationalPsychology, 83, 17–26. doi.org/10.1037/0022-0663.83.1.17

Cromley, J.G. & Byrnes, J.P. (2012). Instruction andcognition. Wiley Interdisciplinary Reviews: CognitiveScience, 3, 545–553. doi.org/10.1002/wcs.1192

Cronbach, L.J. & Snow, R.E. (1977). Aptitudes andinstructional methods. New York: Irvington.

De Koning, B.B., Tabbers, H.K., Rikers, R.M. & Paas,F. (2009). Towards a framework for attentioncueing in instructional animations: Guidelinesfor research and design. Educational PsychologyReview, 21, 113–140 doi.org/10.1007/s10648-009-9098-7

Deci, E.L. & Ryan, R.M. (2012). Motivation,personality, and development within embeddedsocial contexts: An overview of self-determinationtheory. In R.M. Ryan (Ed.), The Oxford handbook ofhuman motivation (pp.85–110). New York, NY:Oxford University Press. doi.org/10.1093/oxfordhb/9780195399820.013.0006

52 35th Vernon-Wall Lecture

Andrew J. Martin

DeRuvo, S.L. (2009). Strategies for teaching adolescentswith ADHD: Effective classroom techniques across thecontent areas. San Francisco: Jossey-Bass.

Dowrick, P.W., Kim-Rupnow, W.S. & Power, T.J.(2006). Video feedforward for reading. Journal ofSpecial Education, 39, 194–207.doi.org/10.1177/00224669060390040101

Dowrick, P.W., Tallman, B.I. & Connor, M.E. (2005).Constructing better futures via video. Journal ofPrevention and Intervention in the Community, 29,131–144. doi.org/10.1300/J005v29n01_09

Eccles, J.S. & Midgley, C. (1989). Stage-environmentfit: Developmentally appropriate classrooms foryoung adolescents. In C. Ames & R. Ames (Eds).Research on motivation in education: Goals andcognitions (Vol 3, pp.139–186). New York:Academic Press.

Eccles, J.S., Midgley, C., Wigfield, A., Buchanan, C.M., Reuman, D., Flanagan, C. & Mac Iver, D.(1993). Development during adolescence: Theimpact of stage-environment fit on youngadolescents’ experiences in schools and infamilies. American Psychologist, 48, 90–101.doi.org/10.1037/0003-066X.48.2.90

Eccles, J.S. & Roeser, R.W. (2009). Schools, academicmotivation, and stage-environment fit. In R.M.Lerner & L. Steinber (Eds.) Handbook of AdolescentPsychology (3rd ed.)(pp.404–434). Hoboken, N.J.:John Wiley & Sons.

Edgar, S. (2015). Identifying the influence of genderon motivation and engagement levels in studentphysiotherapists. Medical Teacher, 37, 348–353.doi.org/10.3109/0142159X.2014.948829

Elliot, A.J. (2005). A conceptual history of theachievement goal construct. In A.J. Elliot & C.S.Dweck (Eds). Handbook of competence andmotivation (pp.52–72). New York: Guildford.

Elliot, A.J., Murayama, K., Kobeisy, A. & Lichtendfeld,S. (2014). Potential-based achievement goals.British Journal of Educational Psychology, 85,192–206. doi:10.1111/bjep.12051

Elliot, A.J., Murayama, K. & Pekrun, R. (2011). A 3×2achievement goal model. Journal of EducationalPsychology, 103, 632–648. doi:10.1037/a0023952

Ericsson, K.A. (2014). The road to excellence: Theacquisition of expert performance in the arts and sciences,sports, and games. New York: Psychology Press.

Ericsson, K.A. & Pool, R. (2016). Peak: Secrets from thenew science of expertise. New York: Harcourt.

Evertson, C., Anderson, C., Anderson, L. & Brophy, J.E. (1980). Relationship between classroombehaviors and student outcomes in junior highmathematics and English classes. AmericanEducational Research Journal, 17, 43–60.doi.org/10.3102/00028312017001043

Ewing, B. (2011). Direct instruction in mathematics:Issues for schools with high Indigenousenrolments: A literature review. Australian Journalof Teacher Education, 36, 64–91.doi.org/10.14221/ajte.2011v36n5.5

Eysenck, M.W. & Calvo, M.G. (1992). Anxiety andperformance: The processing efficiency theory.Cognition and Emotion, 6, 409–434.doi.org/10.1080/02699939208409696

Farkota, R. (2003). The effects of a 15-minute directinstruction intervention in the regular mathematicsclass on students’ mathematical self-efficacy andachievement. Doctoral Dissertation. MonashUniversity, Australia.

Finn, J.D. (1989). Withdrawing from school. Review ofEducational Research, 59, 117–142.doi.org/10.3102/00346543059002117

Finn, J.D. & Zimmer, K. (2013. Student engagement:What is it? Why does it matter? In S. Christenson,Reschly & Wylie (Eds.). Handbook of research onstudent engagement (pp.97–131). New York:Springer.

Fisher, D. & Frey, N. (2008). Better learning throughstructured teaching: A framework for the gradual releaseof responsibility. Alexandria, VA: Association forSupervision and Curriculum Development.

Forness, S.R. (2001). Special education and relatedservices: What have we learned from meta-analysis? Exceptionality, 9, 185–197.doi.org/10.1207/S15327035EX0904_3

Forness, S.R., Kavale, K.A., Blum, I.M. & Lloyd, J.W.(1997). Mega-analysis of meta-analyses. TeachingExceptional Children, 29, 4–9.

Fraser, K., Huffman, J., Ma, I., Sobczak, M., McIlwrick,J., Wright, B. & McLaughlin, K. (2014). Theemotional and cognitive impact of unexpectedsimulated patient death: A randomizedcontrolled trial. CHEST Journal, 145, 958–963.doi.org/10.1378/chest.13-0987

Fredricks, J.A., Blumenfeld, P.C. & Paris, A.H. (2004).Student engagement: Potential of the concept,state of the evidence. Review of EducationalResearch, 74,59–109.doi.org/10.3102/00346543074001059

Geary, D.C. (2007). Educating the evolved mind:Reflections and refinements. In J.S. Carlson &J.R. Levin (Eds). Educating the evolved mind:Conceptual foundations for an evolutionaryeducational psychology (pp.177–203). Charlotte,NC: Information Age Publishing.

Geary, D.C. (2008a). An evolutionarily informededucation science. Educational Psychologist, 43,179–195. doi.org/10.1080/00461520802392133

Geary, D.C. (2008b). Whither evolutionaryeducational psychology? Educational Psychologist,43, 217–226.doi.org/10.1080/00461520802392240

Gersten, R., Chard, D., Jayanthi, M., Baker, S.,Morphy, P. & Flojo, J. (2009). A meta-analysis ofmathematics instructional interventions for studentswith learning disabilities. Los Alamitos, CA:Instructional Research Group.

Gillen-O’Neel, C. & Fuligni, A. (2013). A longitudinalstudy of school belonging and academicmotivation across high school. Child Development,84, 678–692.

Using Load Reduction Instruction (LRI) to boost motivation and engagement

35th Vernon-Wall Lecture 53

Ginns, P. (2005). Meta-analysis of the modality effect.Learning and Instruction, 15, 313–331.doi.org/10.1016/j.learninstruc.2005.07.001

Ginns, P. (2006). Integrating information: A meta-analysis of the spatial contiguity and temporalcontiguity effects. Learning and Instruction, 16,511–525.doi.org/10.1016/j.learninstruc.2006.10.001

Ginns, P., Martin, A.J., Liem, G.A.D. & Papworth, B.(2014). Structural and concurrent validity of theInternational English Big-Five Mini-Markers in anadolescent sample: Exploring analyticapproaches and implications for personalityassessment. Journal of Research in Personality, 53,182–192. doi.org/10.1016/j.jrp.2014.10.001

Ginns, P., Martin, A.J. & Marsh, H.W. (2013).Designing instructional in a conversational style:A meta-analysis. Educational Psychology Review, 25,445–472. doi.org/10.1007/s10648-013-9228-0

Good, TL. & Grouws, D.A. (1979). The MissouriMathematics Effectiveness Project. Journal ofEducational Psychology, 71, 143–155.doi.org/10.1037/0022-0663.71.3.355

Graham, C. & Hill, M. (2003). Negotiating the transitionto secondary school. Scottish Council for Researchin Education. Edinburgh: Scotland.

Guthrie, J.T. & Davis, M.H. (2003). Motivatingstruggling readers in middle school through anengagement model of classroom practice.Reading and Writing Quarterly, 19, 59–85.doi.org/10.1080/10573560308203

Haas, M. (2005). Teaching methods for secondaryalgebra: A meta-analysis of findings. NASSPBulletin, 89, 24–46.doi.org/10.1177/019263650508964204

Hallahan, D.P. (1989). Attention disorders: Specificlearning disabilities. In T. Husen & Postlethwait(Eds.). The international encyclopedia of education:Research and studies (pp.98–99). New York, NY:Pergamon.

Hanewald, R. (2013). Transition between primaryand secondary school: Why it is important andhow it can be supported. Australian Journal ofTeacher Education, 38, 62–74.doi.org/10.14221/ajte.2013v38n1.7

Hartanto, T.A., Krafft, C.E., Iosif, A.M. & Schweitzer,J.B. (2016). A trial-by-trial analysis reveals moreintense physical activity is associated with bettercognitive control performance in attention-deficit/hyperactivity disorder. ChildNeuropsychology, 22, 618–26.doi.org/10.1080/09297049.2015.1044511

Hattie, J. (2009). Visible learning: A synthesis of over 800meta-analyses relating to achievement. London andNew York: Routledge.

Hattie, J. (2012). Visible learning for teachers. Londonand New York: Routledge.

Hu, F.T., Ginns, P. & Bobis, J. (2015). Getting thepoint: tracing worked examples enhanceslearning. Learning and Instruction, 35, 85–93.doi.org/10.1016/j.learninstruc.2014.10.002

Hunter, M. (1984). Knowing, teaching andsupervising. In P. Hosford (Ed). Using what weknow about teaching (pp.169–192). Alexandria, VA:ASCD.

Jackson, S.A., Kleitman, S. & Aidman, E. (2014). Lowcognitive load and reduced arousal impedepractice effects on executive functioning,metacognitive confidence and decision making.PloS One, 9, e115689.doi.org/10.1371/journal.pone.0115689

Jitendra A.K., Edwards, L.L., Sacks, G. & Jacobson,L.A. (2004). What research says about vocabularyinstruction for students with learning disabilities.Exceptional Children, 70, 299–322.doi.org/10.1177/001440290407000303

Kagan, D.M. (1990). Ways of evaluating teachercognition: Inferences concerning the Goldilocksprinciple. Review of Educational Research, 60,419–469.

Kalyuga, S. (2007). Expertise reversal effect and itsimplications for learner-tailored instruction.Educational Psychology Review, 19, 509–539.doi.org/10.1007/s10648-007-9054-3

Kalyuga, S. (2011). Cognitive load theory: Implications foraffective computing. AAAI Publications, Twenty-Fourth International FLAIRS Conference,Florida, USA.

Kalyuga, S., Ayres, P., Chandler, P. & Sweller, J. (2003).The expertise reversal effect. EducationalPsychologist, 38, 23–31.doi.org/10.1207/S15326985EP3801_4

Kalyuga, S., Chandler, P., Tuovinen, J. & Sweller, J.(2001). When problem solving is superior tostudying worked examples. Journal of EducationalPsychology, 93, 579–588. doi.org/10.1037/0022-0663.93.3.579

Kalyuga, S. & Singh, A-M. (2015). Rethinking theboundaries of cognitive load theory in complexlearning. Educational Psychology Review.doi.org/10.1007/s10648-015-9352-0

Kamps, D., Abbott, M., Greenwood, C., Wills, H.,Veerkamp, M. & Kaufman, J. (2008). Effects ofsmall group reading instruction and curriculumdifferences for students most at risk inkindergarten: Two-year results for secondary- andtertiary-level interventions. Journal of LearningDisabilities, 41, 101–114.doi.org/10.1177/0022219407313412

Kapur, M. (2008). Productive failure. Cognition andInstruction, 26, 379–424.doi.org/10.1080/07370000802212669

Kinder, D. & Bursuck, W. (1992). The research for aunified social studies curriculum; Does historyreally repeat itself? In D. Carnine & E.J.Kameenui (Eds). Higher order thinking: Designingcurriculum for mainstream students (pp.23–38).Austin, TX: Pro-ed.

54 35th Vernon-Wall Lecture

Andrew J. Martin

Kirschner, P.A., Sweller, J. & Clark, R.E. (2006). Whyminimal guidance during instruction does notwork: An analysis of the failure of constructivist,discovery, problem-based, experiential, andinquiry-based teaching. Educational Psychologist,41, 75–86. doi.org/10.1207/s15326985ep4102_1

Klahr, D. & Nigam, M. (2004). The equivalence oflearning paths in early science instruction effectsof direct instruction and discovery learning.Psychological Science, 15, 661–667.doi.org/10.1111/j.0956-7976.2004.00737.x

Kroesbergen, E.H. & Van Luit, J.E.H. (2003).Mathematics interventions for children withspecial educational needs. Remedial and SpecialEducation, 24, 97–114.doi.org/10.1177/07419325030240020501

Ku, H.Y. & Sullivan, H.J. (2002). Studentperformance and attitudes using personalizedmathematics instruction. Educational TechnologyResearch and Development, 50, 21–34.doi.org/10.1007/BF02504959

Kulik, J. & Kulik, C.L. (1979). College teaching. In P.Peterson & H. Walberg, (Eds.). Research onteaching: Concepts, findings and implications(pp.70–93). Berkeley, CA: McCutcheon.

Kvalsund, R. (2000). The transition from primary tosecondary level in smaller and larger ruralschools in Norway: Comparing differences incontext and social meaning. International Journalof Educational Research, 33, 401–424.doi.org/10.1016/S0883-0355(00)00025-2

Lee, H.S. & Anderson, J.R. (2013). Student learning:What has instruction got to do with it? AnnualReview of Psychology, 64, 445–469.doi.org/10.1146/annurev-psych-113011-143833

Liem, G.A. & Martin, A.J. (2012). The Motivation andEngagement Scale: Theoretical framework,psychometric properties, and applied yields.Australian Psychologist, 47, 3–13.doi.org/10.1111/j.1742-9544.2011.00049.x

Liem, G.A.D. & Martin, A.J. (2013). Direct instructionand academic achievement. In J. Hattie & E.Anderman (Eds.). International guide to studentachievement. Oxford: Routledge.

Linnenbrink-Garcia, L., Tyson, D.F. & Patall, E.A.(2008). When are achievement goal orientationsbeneficial for academic achievement? A closerlook at moderating factors. International Review ofSocial Psychology, 21, 19–70.

Locke, E.A. & Latham, G.P. (2002). Buildingpractically useful theory of goal setting and taskmotivation. American Psychologist, 57, 705–717.doi.org/10.1037/0003-066X.57.9.705

Macken, L. & Ginns, P. (2014). Pointing and tracinggestures may enhance anatomy and physiologylearning. Medical Teacher, 36, 596–601.doi.org/10.3109/0142159X.2014.899684

Maehr, M.L. & Zusho, A. (2009). Achievement goaltheory: The past, present, and future. In K.R.Wentzel & A. Wigfield (Eds). Handbook ofmotivation at school (pp.77–104). New York:Routledge.

Magliaro, S.G., Lockee, B.B. & Burton, J.K. (2005).Direct instruction revisited: A key model forinstructional technology. Educational TechnologyResearch and Development, 53, 41–55.doi.org/10.1007/BF02504684

Malmberg, L-E., Woolgar, C. & Martin, A.J. (2013).Quality of the Learning ExperienceQuestionnaire (LEQ) for Personal DigitalAssistants (PDAs). International Journal ofQuantitative Research in Education, 1, 275–296.doi.org/10.1504/IJQRE.2013.057689

Marsh, H.W. (2007). Self-concept theory, measurementand research into practice: The role of self-concept ineducational psychology. Leicester, UK: BritishPsychological Society.

Marsh, H.W. & Martin, A.J. (2011). Academic self-concept and academic achievement: Relationsand causal ordering. British Journal of EducationalPsychology, 81, 59–77.doi.org/10.1348/000709910X503501

Martin, A.J. (2003). How to motivate your child for schooland beyond. Sydney: Random House/Bantam.

Martin, A.J. (2005). How to help your child fly throughlife: The 20 big issues. Sydney: RandomHouse/Bantam.

Martin, A.J. (2007). Examining a multidimensionalmodel of student motivation and engagementusing a construct validation approach. BritishJournal of Educational Psychology, 77, 413–440.doi.org/10.1348/000709906X118036

Martin, A.J. (2009). Motivation and engagementacross the academic lifespan: A developmentalconstruct validity study of elementary school,high school, and university/college students.Educational and Psychological Measurement, 69,794–824. doi.org/10.1177/0013164409332214

Martin, A.J. (2010). Building classroom success:Eliminating academic fear and failure. New York:Continuum.

Martin, A.J. (2012a). Attention Deficit HyperactivityDisorder (ADHD), perceived competence, andself-worth: Evidence and implications forstudents and practitioners. In D. Hollar (Ed.).Handbook on children with special health care needs(pp.47–72). New York: Springer.doi.org/10.1007/978-1-4614-2335-5_3

Martin, A.J. (2012b). Motivation and engagement:Conceptual, operational and empirical clarity.Section Commentary in S. Christenson, A.Reschly & C. Wylie (Eds.). Handbook of research onstudent engagement. New York: Springer.doi.org/10.1007/978-1-4614-2018-7_14

Martin, A.J. (2013). From will to skill: The psychologyof motivation, instruction and learning in today'sclassroom. InPsych – Bulletin of AustralianPsychological Society, December, 10–12.

Martin, A.J. (2015). Teaching academically at-riskstudents in middle school: The roles of explicitinstruction and guided discovery learning. In SGroundwater-Smith & N. Mockler (Eds). Big fish,little fish: Teaching and learning in the middle years.Cambridge: Cambridge University Press.

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Martin, A.J. (2016). The Motivation and EngagementScale (16th Edition). Sydney, Australia: LifelongAchievement Group.www.lifelongachievement.com

Martin, A.J., Anderson, J., Bobis, J., Way, J. & Vellar, R.(2012). Switching on and switching off inmathematics: An ecological study of future intentand disengagement amongst middle schoolstudents. Journal of Educational Psychology, 104,1–18. doi.org/10.1037/a0025988

Martin, A.J. & Elliot, A.J. (2016). The role of personalbest (PB) and dichotomous achievement goals instudents' academic motivation and engagement:A longitudinal investigation. EducationalPsychology,36 1285–1302.

Martin, A.J., Ginns, P. & Papworth, B. (2016).Motivation and engagement: Same or different? Does itmatter? Submitted for publication.

Martin, A.J. & Hau, K.-T. (2010). Achievementmotivation amongst Chinese and Australianschool students: Assessing differences of kind anddifferences of degree. International Journal ofTesting, 10, 274–294.doi.org/10.1080/15305058.2010.482220

Martin, A.J. & Liem, G.A. (2010). Academic PersonalBests (PBs), engagement, and achievement: Across-lagged panel analysis. Learning andIndividual Differences, 20, 265–270.

Martin, A.J. Marsh, H.W. & Debus, R.L. (2001a). Aquadripolar need achievement representation ofself-handicapping and defensive pessimism.American Educational Research Journal, 38, 583–610.doi.org/10.3102/00028312038003583

Martin, A.J. Marsh, H.W. & Debus, R.L. (2001b). Self-handicapping and defensive pessimism:Exploring a model of predictors and outcomesfrom a self-protection perspective. Journal ofEducational Psychology, 93, 87–102.doi.org/10.1037/0022-0663.93.1.87

Martin, A.J. Marsh, H.W. & Debus, R.L. (2003). Self-handicapping and defensive pessimism: A modelof self-protection from a longitudinal perspective.Contemporary Educational Psychology, 28, 1–36.doi.org/10.1016/S0361-476X(02)00008-5

Martin, A.J., Marsh, H.W., Williamson, A. & Debus,R.L. (2003). Self-handicapping, defensivepessimism, and goal orientation: A qualitativestudy of university students. Journal of EducationalPsychology, 95, 617–628. doi.org/10.1037/0022-0663.95.3.617

Martin, A.J., Martin, T.G. & Evans, P. (2016).Motivation and engagement in Jamaica: Testing amultidimensional framework among students inan emerging regional context. Journal ofPsychoeducational Assessment.doi.org/10.1177/0734282916674424

Martin, A.J., Papworth, B., Ginns, P. & Liem, G.A.D.(2014). Boarding school, motivation andengagement, and psychological well-being: Alarge-scale investigation. American EducationalResearch Journal, 51, 1007–1049.doi.org/10.3102/0002831214532164

Martin, A.J., Papworth, B., Ginns, P., Malmberg, L-E.,Collie, R. & Calvo, R.A. (2015). Real-timemotivation and engagement during a month atschool: Every moment of every day for everystudent matters. Learning and IndividualDifferences, 38, 26–35.doi.org/10.1016/j.lindif.2015.01.014

Martin, A.J., Way, J., Bobis, J. & Anderson, J. (2015).Exploring the ups and downs of mathematicsengagement in the middle years of school. Journalof Early Adolescence, 35, 199–244.doi.org/10.1177/0272431614529365

Martin, A.J., Yu, K. & Hau, K-T. (2014). Motivationand engagement in the 'Asian Century': Acomparison of Chinese students in Australia,Hong Kong, and Mainland China. EducationalPsychology: An International Journal of ExperimentalEducational Psychology, 34, 417–439.doi.org/10.1080/01443410.2013.814199

Martin, A.J., Yu, K., Papworth, B., Ginns, P. & Collie,R.J. (2015). Motivation and engagement in USA,Canada, United Kingdom, Australia, and China:Testing a multidimensional framework. Journal ofPsychoeducational Assessment, 33, 103–114.doi.org/10.1177/0734282914546287

Marzano, R.J. (1998). A theory-based meta-analysis ofresearch on instruction. McREL, Aurora, Colorado

Marzano, R.J. (2003). What works in schools.Alexandria, VA: ASCD.

Marzano, R.J. (2011). Art and science of teaching /The perils and promises of discovery learning.Educational Leadership, 69, 86–87.

Mastropieri, M.A., Scruggs, T.E., Bakken, J.P. &Whedon, C. (1996). Reading comprehension: Asynthesis of research in learning disabilities.Advances in Learning and Behavioral Disabilities,10B, 201–227.

Matlen, B.J. & Klahr, D. (2010, June). Sequentialeffects of high and low guidance on children'searly science learning. In Proceedings of the 9thInternational Conference of the Learning Sciences-Volume 1 (pp.1016–1023). International Society ofthe Learning Sciences.

Mayer, R.E. (2004). Should there be a three-strikesrule against pure discovery learning? The case forguided methods of instruction. AmericanPsychologist, 59, 14–19. doi.org/10.1037/0003-066X.59.1.14

Mayer, R.E. & Johnson, C.I. (2008). Revising theredundancy principle in multimedia learning.Journal of Educational Psychology, 100, 380–386doi.org/10.1037/0022-0663.100.2.380

Mayer, R.E. & Moreno, R. (2010). Techniques thatreduce extraneous cognitive load and manageintrinsic cognitive load during multimedialearning. (pp.131–152). In J.L. Plass., R. Moreno.& R. Brunken (Eds). Cognitive load theory.Cambridge: Cambridge University Press.

Maynes, N., Julien-Schultz, L. & Dunn, C. (2010).Modeling and the Gradual Release ofResponsibility: What Does It Look Like in theClassroom? Brock Education Journal, 19, 65–77.

56 35th Vernon-Wall Lecture

Andrew J. Martin

McInerney, D.M. (2000). Helping kids achieve their best.Sydney: Allen and Unwin.

Mcinerney, V., Marsh, H. W. & Mcinerney, D. M.(1999). The designing of the computer anxietyand learning measure (CALM): Validation ofscores on a multidimensional measure of anxietyand cognitions relating to adult learning ofcomputing skills using structural equationmodeling. Educational and Psychological Measurement,59, 451–470.doi.org/10.1177/00131649921969974

McInerney, D.M., Yeung, A.S. & McInerney, V. (2000).Cross-cultural validation of the Inventory ofSchool Motivation (ISM): Motivation orientationsof Navajo and Anglo students. Journal of AppliedMeasurement, 2, 135–153.

McKinney, J.D., Montague, M. & Hocutt, A.M. (1993).Educational assessment of students with attentiondeficit disorder. Exceptional Children, 60, 125–131.

McMullen, F. & Madelaine, A. (2014) Why is there somuch resistance to Direct Instruction? AustralianJournal of Learning Difficulties, 19, 137–151doi.org/10.1080/19404158.2014.962065

McWilliam, E.L. (2009) Teaching for creativity: Fromsage to guide to meddler. Asia Pacific Journal ofEducation, 29, 281–293.doi.org/10.1080/02188790903092787

Meichenbaum, D. (1974). Cognitive behavior modification.Morristown, NJ: General Learning Press.

Midgley, C., Arunkumar, R. & Urdan, T. C. (1996). ‘IfI don’t do well tomorrow, there’s a reason’:Predictors of adolescents’ use of academic self-handicapping strategies. Journal of EducationalPsychology, 88, 423–434. doi.org/10.1037/0022-0663.88.3.423

Midgley, C., Maehr, M., Hicks, L., Roesser, R., Urdan, T.,Anderman, E., Kaplan, A., Arunkumar. R. &Middleton, M. (1997). Patterns of Adaptive Learning(PALS). Ann Arbor, MI: University of Michigan.

Miller, R.B., Greene, B.A., Montalvo, G.P., Ravindran,B. & Nichols, J.D. (1996). Engagement inacademic work: The role of learning goals, futureconsequences, pleasing others, and perceivedability. Contemporary Educational Psychology, 21,388–422. doi.org/10.1006/ceps.1996.0028

Moreno, R. & Mayer, R.E. (2010). Techniques thatincrease generative processing in multimedialearning: Open questions for cognitive loadresearch. (pp.153–177). In J.L. Plass., R. Moreno.& R. Brunken (Eds). Cognitive load theory.Cambridge: Cambridge University Press.

Murphy, P.K. & Alexander, P.A. (2000). A motivatedexploration of motivation terminology.Contemporary Educational Psychology, 25, 3–53.doi.org/10.1006/ceps.1999.1019

Nagabhushan, P. (2012). Academic motivation andengagement: An examination of its factorstructure in senior school years. The EuropeanJournal of Behavioural and Social Sciences, 2,260–277.doi.org/10.15405/FutureAcademy/ejsbs(2301-2218).2012.2.11

Nandagopal, K. & Ericsson, K.A. (2012). Enhancingstudents' performance in traditional education:Implications from the expert performanceapproach and deliberate practice (pp.257-293).In K.R. Harris., S. Graham. & T. Urdan (Eds).APA educational psychology handbook. Washington:American Psychological Association.doi.org/10.1037/13273-010

Nuthall, G. (1999). The way students learn: Acquiringknowledge from an integrated science and socialstudies unit. The Elementary School Journal, 99,303–341 doi.org/10.1086/461928

Paas, F., Renkl, A. & Sweller, J. (2003). Cognitive loadtheory and instructional design: Recentdevelopments. Educational Psychologist, 38, 1–4.doi.org/10.1207/S15326985EP3801_1

Paas, F. & Sweller, J. (2012). An evolutionary upgradeof cognitive load theory: Using the human motorsystem and collaboration to support the learningof complex cognitive tasks. Educational PsychologyReview, 24, 27–45. doi.org/10.1007/s10648-011-9179-2

Pearson, P.D. & Gallagher, M. (1983). The instructionof reading comprehension. ContemporaryEducational Psychology, 8, 317–344.doi.org/10.1016/0361-476X(83)90019-X

Penney, C.G. (1989). Modality effects and thestructure of short-term memory. Memory andCognition, 17, 398–442.doi.org/10.3758/BF03202613

Pintrich, P.R. (2000). Educational psychology at themillennium: A look back and a look forward.Educational Psychologist, 35, 221–226.doi.org/10.1207/S15326985EP3504_01

Pintrich, P.R. (2003). A motivational scienceperspective on the role of student motivation inlearning and teaching contexts. Journal ofEducational Psychology, 95, 667–686.doi.org/10.1037/0022-0663.95.4.667

Pintrich, P.R. & DeGroot, E. (1990). Motivational andself-regulated learning components of classroomacademic performance. Journal of EducationalPsychology, 82, 33–40. doi.org/10.1037/0022-0663.82.1.33

Pintrich, P.R., Smith, D.A.F., Garcia, T. & McKeachie,W.J. (1991). A manual for the use of the MotivatedStrategies for Learning Questionnaire (MSLQ). AnnArbor, MI: National Center for Research toImprove Postsecondary Teaching and Learning.

Plenty, S. & Heubeck, B.G. (2011). Mathematicsmotivation and engagement: an independentevaluation of a complex model with Australianrural high school students. Educational Researchand Evaluation, 17, 283–299.doi.org/10.1080/13803611.2011.622504

Plenty, S. & Heubeck, B.G. (2013). Amultidimensional analysis of changes inmathematics motivation and engagement duringhigh school. Educational Psychology, 33, 14–30.doi.org/10.1080/01443410.2012.740199

35th Vernon-Wall Lecture 57

Using Load Reduction Instruction (LRI) to boost motivation and engagement

58 35th Vernon-Wall Lecture

Andrew J. Martin

Pollock, E., Chandler, P. & Sweller, J. (2002).Assimilating complex information. Learning andInstruction, 12, 61–86. doi.org/10.1016/S0959-4752(01)00016-0

Pressley, M. & Allington, R.L. (2014). Readinginstruction that works: The case for balanced teaching.New York, NY: Guilford Publications.

Pressley, M., Roehrig, A.D., Raphael, L., Dolezal, S.,Bohn, C., Mohan, L., Wharton-McDonald, R.,Bogner, K. & Hogan, K. (2003). Teachingprocesses in elementary and secondaryeducation. In W.M. Reynolds & G.E. Miller (Eds).Handbook of educational psychology. New Jersey:John Wiley & Sons.doi.org/10.1002/0471264385.wei0708

Przychodzin-Havis, A. M., Marchand-Martella, N.E.,Martella, R.C. & Azim, D. (2004). Direct Instructionmathematics programs: An overview and researchsummary. Journal of Direct Instruction, 4, 53–84.

Przychodzin-Havis, A.M., Marchand-Martella, N.E.,Martella, R.C., Miller, D.A., Warner, L., Leonard,B. & Chapman, S. (2005). An analysis ofcorrective reading research. Journal of DirectInstruction, 5, 37–65.

Purdie, N. & Ellis, L. (2005). A review of the empiricalevidence identifying effective interventions and teachingpractices for students with learning difficulties in Years4, 5, and 6. Melbourne: Australian Council forEducational Research.

Reeve, J. (2012). A self-determination theoryperspective on student engagement. In S.L.Christenson, A.L. Reschly & C. Wylie (Eds.).Handbook of research on student engagement(pp.149–172). New York. NY: Springer.doi.org/10.1007/978-1-4614-2018-7_7

Reeve, J. (2015). A grand theory of motivation: Whynot? Motivation and Emotion, 40, 31–35.doi.org/10.1007/s11031-015-9538-2

Reeve, J., Deci, E.L. & Ryan, R.M. (2004). Self-determination theory: A dialectical frameworkfor understanding socio-cultural influences onstudent motivation. In D. M. McInerney & S. VanEtten (Eds.). Big theories revisited (pp.31–60).Greenwich, CT: Information Age Press.

Reeves, J. (2010). Teacher learning by script.Language Teaching Research, 14, 241–258.doi.org/10.1177/1362168810365252

Renkl, A. (2014). Toward an instructionally orientedtheory of example‐based learning. CognitiveScience, 38, 1–37. doi.org/10.1111/cogs.12086

Renkl, A. & Atkinson, R.K. (2010). Learning fromworked-out examples and problem solving.(pp.91-108). In J.L. Plass., R. Moreno. & R.Brunken (Eds). Cognitive load theory. Cambridge:Cambridge University Press.

Reschly, A.L. & Christenson, S.L. (2012). Jingle,jangle, and conceptual haziness: Evolution andfuture directions of the engagement construct. InS. Christenson, A. Reschly & C. Wylie (Eds.).Handbook of research on student engagement. NewYork: Springer. doi.org/10.1007/978-1-4614-2018-7_1

Rhodewalt, F. & Davison, J. (1986). Self-handicappingand subsequent performance: Role of outcomevalance and attributional certainty. Basic andApplied Social Psychology, 7, 307–322.doi.org/10.1207/s15324834basp0704_5

Robins, P. M. (1992). A comparison of behavioral andattentional functioning in children diagnosed ashyperactive or learning disabled. Journal ofAbnormal Child Psychology, 20, 65–82.doi.org/10.1007/BF00927117

Rosenshine, B.V. (1986). Synthesis of research onexplicit teaching. Educational Leadership, 43, 60-69.

Rosenshine, B.V. (1995). Advances in research oninstruction. The Journal of Educational Research, 88,262–268.doi.org/10.1080/00220671.1995.9941309

Rosenshine, B.V. (2008). Five meanings of directinstruction. Lincoln: IL: Center on Innovation andImprovement .

Rosenshine, B.V. (2009). The empirical support fordirect instruction. In S. Tobias & T.M. Duffy(Eds). Constructivist instruction: Success or failure?New York: Routledge.

Rupley, W.H., Blair, T.R. & Nichols, W.D. (2009).Effective reading instruction for strugglingreaders: The role of direct/explicit teaching.Reading and Writing Quarterly, 25, 125–138.doi.org/10.1080/10573560802683523

Ryan, R.M. & Deci, E.L. (2000). Self-determinationtheory and the facilitation of intrinsic motivation,social development, and well-being. AmericanPsychologist, 55, 68–78. doi.org/10.1037/0003-066X.55.1.68

Sarver, D.E., Rapport, M.D., Kofler, M.J., Raiker, J.S. &Friedman, L.M. (2015). Hyperactivity inattention-deficit/hyperactivity disorder (ADHD):Impairing deficit or compensatory behavior?Journal of Abnormal Child Psychology, 43,1219–1232. doi.org/10.1007/s10802-015-0011-1

Schieffer, C., Marchand-Martella, N.E., Martella, R.C., Simonsen, F.L. & Waldron-Soler, K.M. (2002).An analysis of the Reading Mastery program:Effective components and research review.Journal of Direct Instruction, 2, 87–119.

Schunk, D.H. & Miller, S.D. (2002). Self-efficacy andadolescents’ motivation. In F. Pajares & T. Urdan(Eds). Academic motivation of adolescents.Connecticut: Information Age Publishing.

Schunk, D.H. & Mullen, C.A. (2012). Self-efficacy asan engaged learner. In S. Christenson, A. Reschly& C. Wylie (Eds.). Handbook of research on studentengagement. New York: Springer.doi.org/10.1007/978-1-4614-2018-7_10

Schunk, D.H., Pintrich, P.R. & Meece, J.L. (2008).Motivation in education. Upper Saddle River, NJ:Pearson Education.

Schute, V.J. (2008). Focus on formative feedback.Review of Educational Research, 78, 153–189.doi.org/10.3102/0034654307313795

Senko, C. & Miles, K. M. (2008). Pursuing their ownlearning agenda: How mastery-oriented studentsjeopardize their class performance. ContemporaryEducational Psychology, 33, 561–583.

Sierens, E., Vansteenkiste, M., Goossens, L., Soenens,B. & Dochy, F. (2009). The synergisticrelationship of perceived autonomy support andstructure in the prediction of self‐regulatedlearning. British Journal of Educational Psychology,79, 57–68. doi.org/10.1348/000709908X304398

Sirin, S.R. (2005). Socioeconomic status andacademic achievement: A meta-analytic review ofresearch. Review of Educational Research, 75,417–453. doi.org/10.3102/00346543075003417

Skinner, E. A. (1996). A guide to constructs ofcontrol. Journal of Personality and Social Psychology,71, 549–570. doi.org/10.1037/0022-3514.71.3.549

Snow, R.E. (1991). Aptitude-treatment interaction asa framework for research on individualdifferences in psychotherapy. Journal of Consultingand Clinical Psychology, 59, 205–216.doi.org/10.1037/0022-006X.59.2.205

Stein, M., Carnine, D. & Dixon, R. (1998). Directinstruction: Integrating curriculum design andeffective teaching practice. Intervention in Schooland Clinic, 33, 227–234.doi.org/10.1177/105345129803300405

Stein, M., Silbert, J. & Carnine, D. (1997). Designingeffective mathematics instruction: A direct instructionapproach. Prentice Hall: Upper Saddle river, NJ:Merrill/Prentice Hall.

Stockard, J. (2010). Promoting reading achievementand countering the "Fourth-Grade Slump": Theimpact of Direct Instruction on readingachievement in fifth grade. Journal of Education forStudents Placed at Risk, 15, 218–240.doi.org/10.1080/10824669.2010.495687

Stockard, J. (2011). Increasing reading skills in ruralareas: An analysis of three school districts. Journalof Research in Rural Education, 26, 1–19.

Strand-Cary, M. & Klahr, D. (2008). Developingelementary science skills: Instructionaleffectiveness and path independence. CognitiveDevelopment, 23, 488–511.doi.org/10.1016/j.cogdev.2008.09.005

Swanson, H.L., Carson, C. & Sachse-Lee, C.M. (1996).A selective synthesis of intervention research forstudents with learning disabilities. SchoolPsychology Review, 25, 370–391.

Swanson, H.L. & Hoskyn, M. (1998). A synthesis ofexperimental intervention literature for studentswith learning disabilities: A meta-analysis oftreatment outcomes. Review of EducationalResearch, 68, 271–321.doi.org/10.3102/00346543068003277

Swanson, H.L. & Sachse-Lee, C. (2000). A meta-analysis of single-subject-design interventionresearch for students with LD. Journal of LearningDisabilities, 33, 114–136.doi.org/10.1177/002221940003300201

Sweller J. (1988). Cognitive load during problemsolving: Effects on learning. Cognitive Science, 12,257–85. doi.org/10.1207/s15516709cog1202_4

Sweller, J. (2003). Evolution of human cognitivearchitecture. In Ross, B. (ed.), The psychology oflearning and motivation, Vol. 43. Academic Press,San Diego, pp.215–266. doi.org/10.1016/s0079-7421(03)01015-6

Sweller, J. (2004). Instructional design consequencesof an analogy between evolution by naturalselection and human cognitive architecture.Instructional Science, 32, 9–31.doi.org/10.1023/B:TRUC.0000021808.72598.4d

Sweller, J. (2012). Human cognitive architecture: Whysome instructional procedures work and othersdo not (pp.295–325). In K.R. Harris., S. Graham.& T. Urdan (Eds). APA educational psychologyhandbook. Washington: American PsychologicalAssociation. doi.org/10.1037/13273-011

Sweller, J., Ayres, P. & Kalyuga, S. (2011). Cognitiveload theory. New York: Springer.doi.org/10.1007/978-1-4419-8126-4

Sweller, J. & Cooper, G.A. (1985). The use of workedexamples as a substitute for problem solving inlearning algebra. Cognition and Instruction, 2,59–89. doi.org/10.1207/s1532690xci0201_3

Tabassam, W. & Grainger, J. (2002). Self-concept,attributional style and self-efficacy beliefs ofstudents with learning disabilities with andwithout Attention Deficit Hyperactivity Disorder.Learning Disability Quarterly, 25, 141–151.doi.org/10.2307/1511280

Tarver, S. (1998). Myths and truths about directinstruction. Effective School Practices, 17, 18–22.

Tarver, S. & Jung, J.S. (1995). A comparison ofmathematics achievement and mathematicsattitudes of first and second graders instructedwith either a discovery-learning mathematicscurriculum or a Direct Instruction curriculum.Effective School Practices, 14, 49–57.

Thompson, T. (1994). Self-worth protection: Review andimplications for the classroom. Educational Review,46, 259-274. doi.org/10.1080/0013191940460304

Tinker, S. & Elphinstone, B. (2014). Learningengagement: The importance of meaning,belonging and academic momentum. TheInternational Journal of the First Year in HigherEducation, 5, 93–105.

Tobias, S. (1982). When do instructional methodsmake a difference? Educational Researcher, 11,4–10. doi.org/10.2307/1174134

Tobias, S. & Duffy, T.M. (Eds.). (2009). Constructivistinstruction: Success or failure? New York, NY:Routledge.

Tomlinson, C.A. (2001). How to differentiate instructionin mixed-ability classrooms. Alexandria, VA: ASCD.

Van den Heuvel-Panhuizen, M. & Drijvers, P. (2014).Realistic Mathematics Education. In S. Lerman(Ed.), Encyclopedia of mathematics education(pp.521–525). Dordrecht, Heidelberg, New York,London: Springer doi.org/10.1007/978-94-007-4978-8_170

35th Vernon-Wall Lecture 59

Using Load Reduction Instruction (LRI) to boost motivation and engagement

Van Gog, T., Ericsson, K.A., Rikers, R.M.J. . & Paas, F.(2005). Instructional design for advancedlearners: Establishing connections between thetheoretical frameworks of cognitive load anddeliberate practice. Educational Technology,Research and Development, 53, 73–81.doi.org/10.1007/BF02504799

Van Keer, H. & Verhaeghe, J.P. (2005). Effects ofexplicit reading strategies instruction and peertutoring on second and fifth graders' readingcomprehension and self-efficacy perceptions. TheJournal of Experimental Education, 73, 291–329.doi.org/10.3200/JEXE.73.4.291-329

Van Merriënboer, J.J. (1992). Training complexcognitive skills: A four-component instructionaldesign model for technical training. EducationalTechnology Research and Development, 40, 23–43.doi.org/10.1007/BF02297047

Van Merriënboer, J.J. & Sweller, J. (2005). Cognitiveload theory and complex learning: Recentdevelopments and future directions. EducationalPsychology Review, 17, 147–177.doi.org/10.1007/s10648-005-3951-0

Vitale, M.R. & Romance, N.R. (1992). Usingvideodisk instruction in an elementary sciencemethods course: Remediating science knowledgedeficiencies and facilitating science teachingattitudes. Journal of Research in Science Teaching, 29,915–928. doi.org/10.1002/tea.3660290903

Voelkl, K.E. (2012). School identification. In S.L.Christenson, A.L. Reschly & C.Wylie (Eds.).Handbook of research on student engagement(pp.193–218). New York: Springer.doi.org/10.1007/978-1-4614-2018-7_9

Walkington, C.A. (2013). Using adaptive learningtechnologies to personalize instruction to studentinterests: The impact of relevant contexts onperformance and learning outcomes. Journal ofEducational Psychology, 105, 932–945.doi.org/10.1037/a0031882

Wang, M.T. & Eccles, J.S. (2012). Adolescentbehavioral, emotional, and cognitive engagementtrajectories in school and their differentialrelations to educational success. Journal of Researchon Adolescence, 22, 31–39.

Weiner, B. (1985). An attributional theory ofachievement motivation and emotion.Psychological Review, 92, 548–573.doi.org/10.1037/0033-295X.92.4.548

Weiner, B. (2010). The development of anattribution-based theory of motivation: A historyof ideas. Educational Psychologist, 45, 38–36.doi.org/10.1080/00461520903433596

Wigfield, A. & Eccles, J.S. (2000). Expectancy-valuetheory of motivation. Contemporary EducationalPsychology, 25, 68–81.doi.org/10.1006/ceps.1999.1015

Wigfield, A, Guthrie, J.T., Perencevich, K.C., Taboada,A., Klauda, S.L., McRae, A. & Barbosa, P. (2008).Role of reading engagement in mediating effectsof reading comprehension instruction onreading outcomes. Psychol Schs, 45: 432–445. doi: 10.1002/pits.20307

Wigfield, A, Guthrie, J.T., Tonks, S. & Perencevich,K.C. (2004). Children’s motivation for reading:Domain specificity and instructional influences.The Journal of Educational Research, 97, 299–310.doi.org/10.3200/JOER.97.6.299-310

Wigfield, A. & Tonks, S. (2002). Adolescents'expectancies for success and achievement taskvalues during middle and high school years. In F.Pajares & T. Urdan (Eds). Academic motivation ofadolescents. Connecticut: Information AgePublishing.

Wiliam, D. (2011). Embedded formative assessment.Bloomington. IN: Solution Tree Press.

Winne, P.H. & Nesbit, J.C. (2010). The psychology ofacademic achievement. Annual Review ofPsychology, 61, 653–678.doi.org/10.1146/annurev.psych.093008.100348

Wood, D., Bruner, J.S. & Ross, G. (1976). The role oftutoring in problem solving. Journal of ChildPsychology and Psychiatry, 17, 89–100doi.org/10.1111/j.1469-7610.1976.tb00381.x

Wurf, G. & Croft-Piggin, L. (2015). Predicting theacademic achievement of first-year, pre-serviceteachers: the role of engagement, motivation,ATAR, and emotional intelligence. Asia-PacificJournal of Teacher Education, 43, 75–91.doi.org/10.1080/1359866X.2014.932328

Yeung, A.S., Barker, K., Tracey, D. & Mooney, M.(2013). School-wide positive behavior forlearning: Effects of dual focus on boys' and girls'behavior and motivation for learning.International Journal of Educational Research, 62, 1-10. doi.org/10.1016/j.ijer.2013.06.002

Yin, H. & Wang, W. (2015). Undergraduate students’motivation and engagement in China: Anexploratory study. Assessment & Evaluation inHigher Education, 41, 601–621.

Yu, K. & Martin, A.J. (2014). Personal best (PB) and‘classic’ achievement goals in the Chinesecontext: Their role in predicting academicmotivation, engagement, and buoyancy.Educational Psychology: An International Journal ofExperimental Educational Psychology, 34, 635–658.doi.org/10.1080/01443410.2014.895297

Zeedyk, M.S., Gallacher, J., Henderson, M., Hope, G.,Husband, B. & Lindsay, K. (2003). Negotiatingthe transition from primary to secondary schoolperceptions of pupils, parents and teachers.School Psychology International, 24, 67–79.doi.org/10.1177/014303430302400101

Zimmerman, B.J. (2002). Achieving self-regulation:The trial and triumph of adolescence. In F.Pajares & T. Urdan (Eds). Academic motivation ofadolescents. Connecticut: Information AgePublishing.

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