June 25, 2008© Hausmann, van de Sande, & VanLehn, 2008Theoretical Relevance: 1 Theoretical Relevance Lecture 2 for the IV track of the 2008 PSLC Summer.

June 25, 2008© Hausmann, van de Sande, & VanLehn, 2008Theoretical Relevance: 1

Theoretical Relevance Lecture 2 for the IV track of the 2008

PSLC Summer School

Robert G.M. Hausmann


William E. HerpChief Executive of Linear Air

“The first lesson in sales is:

Look for the pain.”


Look for the pain in the…

Literature Classroom Science of learning

Your customers


Literature: It is painful not to know the answer to a known question

Known questions appear at the end of papers, reviews, etc.– At least one informed person cares about the

answer.

Common (bad) ways to pose research questions– Cool software– Pop psychology– I learn this way, so…

No customers


Select a question to add information and clarity to the literature

Information value (in Shannon’s sense) – High if prior probability of the answer is very

different from the answer obtained in the experiment.

– Low if experiment just confirms the expected answer.

Clarification value (real pains here)– Low if the literature is a mess, and the experiment

just adds one more fact to the mess.– High if the experiment somehow clarifies the

mess.– Moderate if there is little prior literature.


Look for (and relieve) the pain in the…

Literature– Known question– Answer would add information and/or

clarity Classroom Sciences of Learning

Nex

t


What pains the classroom?

Ask the instructor (you?) what’s most frustrating– Teaching a certain concept?– Transfer to real world?– Depth of understanding?

Ask the students…


Andes is not “selling” (can’t give it away!)

Andes teaches quantitative problem solving.

Most instructors think this is not a bottleneck.

Instead, qualitative problem solving is their concern.


Look for (and relieve) the pain in the…


clarity Classroom

– Instructors consider the question important Sciences of Learning N

ext


Where is the pain in the Learning Sciences?

Too many results No organization of the results No theory No clear implications No classic results that everyone knows No accretion Progress is more like politics than

medicine


To cure the pain, Learning Science needs a theoretical framework

Not like physics– A few basic principles from which all else follows.

More like Medicine– A few basics (anatomy, physiology, genetics)– Many specializations e.g., lymphatic cancers

Few principles; many diseases, syndromes, therapies

– A standardized, rigorous terminology– Digital libraries becoming essential


Types of theories

Computational models

“How People Learn”

principles

Shared theoretical vocabulary

Boxology


PSLC theoretical framework

Computational models

“How People Learn”

principles

Shared theoretical vocabulary

Boxology


PSLC theoretical framework

Shared terminology– Research clusters

Analytic framework

Nex

t


Shared terminology

Micro-level– Knowledge component: A principle, concept,

fact, schema, strategy, meta-strategy…– Learning event: An application of a knowledge

component

Macro-level: A taxonomy of robust learning processes– Sense-making– Fluency-building


Micro level is just (good, old fashioned) cognitive psychology

Instructional activities Prior knowledge

Cognitive processes

Knowledge components

Observable outcomes

Knowledge can be

decomposed

Learning processes

can bedecomposed

andtaxonomized


Macro level is a taxonomy of learning process

Sense making– Coordination of multiple types/sources of learning

Example: step plus a rule

– Interaction of the student with other agents Agents can be peers, experts, or tutoring systems.

Fluency– Three Mechanisms:

Strengthening Deep-feature perception Headroom


PSLC research clusters

Coordinative learning – How do students coordinate multiple

sources of information, media, representations, strategies?

Interactive communication– How does interaction between a student

and a peer, tutor or teacher affect learning?

Fluency and refinement– How does a skill become fluent?


Coordinative learning

Co-training (Blum & Mitchell) Learning from multimedia (Clark & Mayer)(Tversky) Learning from analogies (Novick & Holyoak, 1991)

(J.R. Anderson, Fincham, & Douglass, 1997) (VanLehn, 1998)

Learning from multiple representations & multiple solutions (Ainsworth, 1999)

Learning from agents (Lester, Converse, Stone, Kahler, & Barlow, 1997) (Graesser et al., 2003) (Moreno, Mayer, Spires, & Lester, 2001)


Interactive communication

Feedback and hint effects (J. A. Kulik & Kulik, 1988) (McKendree, 1990) (Hume et al., 1996) (Kluger & DeNisi, 1996) *(Corbett & Anderson, 2001) (Mathan & Koedinger, 2005) (V. J. Shute, 1992)

Learning from examples, self-explanation and fading *(Collins, Brown, & Newman, 1989) (Nguyen-Xuan, Bastide, & Nicaud, 1999) (Kalyuga, Chandler, Tuovinen, & Sweller, 2001) (Renkl, Atkinson, Maier, & Staley, 2002) (Kalyuga, Ayres, Chandler, & Sweller, 2003) (Atkinson, Renkl, & Merrill, in press) *(M. T. H. Chi, 2000) (M.T.H. Chi et al., 2001) (V. Aleven & Koedinger, 2002) (Siegler, 2002) (Corbett, Wagner, lesgold, Ulrich, & Stevens, 2006)

Tutorial dialogues vs. monologues *(VanLehn et al., in press) (Vincent Aleven, Ogan, Popescu, Torrey, & Koedinger, 2004)

Learning with a peer, including collaborative learning, peer tutoring, learning by teaching (Reif & Scott, 1999) (Okita & Schwartz, 2006)


Fluency and refinement

Practice effects, including spacing and part-whole training effects *(Newell & Rosenbloom, 1981) (J.R. Anderson et al., 1997) (Pavlik & Anderson, 2005)

Macroadaptation and mastery learning effects (Bloom, 1984) *(C. Kulik, Kulik, & Bangert-Drowns, 1990) (V. J. Shute, 1992) (V.J. Shute, 1993) (Corbett, 2001) (Ainsworth & Grimshaw, 2004) (Arroyo, Beal, Murray, Walles, & Woolf, 2004)

Implicit (practice only) vs. explicit (direct) instruction. *(Berry & Broadbent, 1984) (Singley, 1990) (K. Koedinger & Anderson, 1993) (Klahr & Nigam, 2004) (VanLehn et al., 2004)


Current research projects

Empirical projects

Alg

ebra

Geo

met

ry

Che

mis

try

Phy

sics

Fre

nch

Chi

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Fluency & refinement 2 2 4 5 3Coordinative learning 2 3 2 1 1 2 1Interactive communication 2 1 8 1

Enabling technology 7


PSLC Theoretical framework

Glossary of theoretical terms– Micro-level – Macro-level

Analytic framework

Nex

t


Learning events over timeD

urat

ion

Fourth Third Second First Fifth

While studying an example, tries to self-explain; fails; looks in text; succeeds

While solving a problem, looks up example; recalls explanation; maps it to problem

Recalls explanation; slips; corrects

Solves without slipsSolves without slips

5 sec.

10 sec.

15 sec.

25 sec.

20 sec.

Opportunity


A new analytic framework, based on an analogy

A problem is to a problem space asa learning event is to a ____________ .


A new analytic framework, based on an analogy

A problem is to a problem space asa learning event is to a learning event space.


Key ideas

A learning event space is a set of paths determined by the instruction and the student’s prior knowledge,

but it is the student who chooses which path to follow

different paths have different outcomes:– Deep learning– Shallow learning– Mis-learning– Etc.


You get to choose the granularity

Coarse grain-size: Only observable actions – Correct vs. incorrect steps– Feedback from tutor

Finer: Reportable mental actions– Recall vs. construct

Even finer?


How to use learning event spaces

Construct a learning event space such that…

it is consistent with observable actions, and…

the top level question, “Why did they learn?”

becomes two easier questions:– Path choice: Why did students tend to choose as

they did?– Path effects: Given that a student went down a

path, why did that cause the observed learning/outcomes?


A simple illustration

Maxine Eskenazi & Alan Juffs hypothesize that using authentic texts will increase vocabulary acquisition in ESL.– Students read text with a few target unfamiliar

words.– Texts come either from web or from existing

primer.– Clicking on an target word displays its definition.

Why would authenticity increase learning? How?


Learning event space (one per target word)

Start Ignore the word

– Exit, with little learning Infer meaning from context

– Exit, with “implicit” learning Click on the word; definition is displayed

– Read & understand the definition Exit, with “explicit” learning

– Go to Start


Why should authentic text help?Hypotheses based on path choices



– Exit, with implicit learning Click on the word; definition is displayed

– Read & understand the definition Exit, with explicit learning

– Go to Start

Authentic text should decrease this

Authentic text should increase this


Why should authentic text help?Hypotheses based on path effects



– Exit, with “implicit” learning Click on the word; definition is displayed

– Read & understand the definition Exit, with “explicit” learning

– Go to Start

Cue validity of this path increases

No change???

No change


To summarize the theoretical framework…

Glossary– Macro-level

Sense-making– Coordinative Learning– Interactive Communication

Fluency

– Micro level: Knowledge components, learning events…

Learning events space– Decomposes “why did they learn?” into

Path choices: Which paths were chosen? Path effects: For each path, what was learned?


Find the pain (and relieve it) in the…


clarity Classroom

– Instructors consider the question important Science of learning

– Glossary of theoretical terms– Learning event spaces


Contact Information

Robert G.M. HausmannUniversity of Pittsburgh 706 Learning Research and Development Center3939 O' Hara Street Pittsburgh, PA, 15260-5179

Web page: http://www.pitt.edu/~bobhaus

Email: [email protected]

Phone: 412.624.7536

Fax: 412.624.9149

June 25, 2008© Hausmann, van de Sande, & VanLehn, 2008Theoretical Relevance: 1 Theoretical Relevance Lecture 2 for the IV track of the 2008 PSLC Summer.

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