DESIGNING A SUSTAINABLE AND EFFECTIVE ONLINE INSTRUCTION PROGRAM ADAPTIVE LEARNING IN THE LIBRARY Joelle Pitts Assistant Professor | Instructional Design.

DESIGNING A SUSTAINABLE AND EFFECTIVE ONLINE INSTRUCTION PROGRAM

ADAPTIVE LEARNING IN THE LIBRARY

Joelle PittsAssistant Professor | Instructional Design

LibrarianKansas State University

OVERVIEW

•Distance Student Behaviors and Expectations• Adaptive Learning• Library Applications

BACKGROUND

• SLIM•Great Plains IDEA• Distance Education Consortium• Fully online degree programs/course sharing• Faculty and student interaction• Program building• Assessment• Research

GREAT PLAINS IDEA FACULTY

• Most teach on campus• Most are under pressure• Most completed their graduate work using

outdated technologies• Focus is getting content online• Most assume their audience is non-traditional• And knows how to conduct research

• Information literacy gaps• More training needed

ONLINE = NON-TRADITIONAL?

NON-TRADITIONAL?

NON-TRADITIONAL?

NON-TRADITIONAL GPIDEA STUDENTS

•Demographics• Mostly female• Avg. age 33• Non traditional• Work at least part time• Family responsibilities• Financial restrictions

NON-TRADITIONAL GPIDEA STUDENTS

Behavior

• 10+ years since undergraduate work

• Technology learning curve• Wikis• Google applications• Multimedia/collaborative

platforms

• Library use• Aren’t aware they are able to

access it as a distance student

• Perception of the library is print based

Expectations

• Consistency• Format• Efficiency• Cost

TRADITIONAL GPIDEA STUDENTS

• Fastest growing population in online education •Demographics• Millennial (18-30)• Work part time• Some family obligations

TRADITIONAL GPIDEA STUDENTS

Behavior

• Technology • Wired• Social Media• Multimedia/collaborative

platforms• Mobile• Gaming

• Library use• Aren’t aware they are

able to access it as a distance student

• Perception of the library is print based

Expectations

• Consistency• Format• Efficiency• Cost

Pew Research Center (2010)

GPIDEA COMMON STUDENT BEHAVIORS AND EXPECTATIONS

Behavior

• Library use• Aren’t aware they are

able to access it as a distance student

• Perception of the library is print based

Expectations

• Consistency• Format• Efficiency• Cost

Information Literacy Level?

Online ≠ non-traditional

ADAPTIVE LEARNING

ADAPTIVE LEARNING BASICS

• A system which collects user information and behavioral data to customize a learning experience for an individual

• Encourages active participation rather than passive receptacle

• Moves away from static hypermedia (same page content and links for all users)

• Artificial Intelligence movement

Brusilovsky (2001)

THIS…

NOT THIS…

MACHINE LEARNING

• Machine collects data and recognizes patterns in the data

• Algorithms – sequence of instructions to transform the input into output

• Intelligent systems have the ability to learn in a changing environment

Alpaydin (2010)

ADAPTATION PROCESS

• Data collection• User interaction• Direct input

• Interpret data using models• Infer user requirements and preferences• Tailored aggregation• Presentation of tailored content (adaptive effect)• Synthesis with population data

Paramythis & Liodl-Reisinger, (2003)

ADAPTATION PROCESS

Brusilovsky & Maybury (2002)

MODELING

Jacko (2009)

CATEGORIES OF ADAPTATION

• Interaction with the system

• Course/object delivery

• Content adaptation

• Collaborative/social support

Paramythis & Liodl-Reisinger (2003)

CONTENT ADAPTATION

• Adaptive presentation• content of a hypermedia page adapted to the user’s

goals, knowledge and other information

• Adaptive navigation• link presentation and functionality adapted to the goals,

knowledge and characteristics of the user

• Direct guidance• Link sorting• Link annotation• Link hiding

Brusilovsky (2000)

ASSESSMENT

• System feedback

• Embedded assessment

• Adaptive • Timing/architecture• Question level

EXAMPLES

• Adaptive eLearning Research Group• AHA!• Andes Physics Tutor• ELM-ART• GRE• iKnow!• Learnthat• Khan Academy• Knewton

• More…

REFERENCES

• Alpaydin, E. (2010). Introduction to machine learning, ch. 1. MIT Press• De Bra, P., et al. (2003) AHA! The Adaptive Hypermedia Architecture. In Proceedings of the

fourteenth ACM conference on Hypertext and Hypermedia, Nottingham, August, pp. 81-84• De Bra, P., Aroyo, L., & Chepegin, V. (2004). The next big thing: adaptive web-based systems.

Journal of Digital Information, 5(1).• Brusilovsky, P. (2000). Adaptive hypermedia: from intelligent tutoring systems to web-based

education. Intelligent Tutoring Systems: 5th International Conference.• Brusilovsky, P. (2001). Adaptive hypermedia. User modeling and user-adapted interaction. 11:

87-110.• Brusilovsky, P., & Maybury, M. T. (2002). From adaptive hypermedia to the adaptive web.

Communications of the ACM, vol. 45, No. 5.• Brusilovsky, P., & Peylo, C. (2003). Adaptive and intelligent web-based educational systems.

International Journal of Artificial intelligence in Education 13, 159-172.• Great Plains Interactive Distance Education Alliance. (2009). New student survey• Jacko, J. A. (2009). Human-computer interaction: design issues, solutions, and applications.

Taylor & Francis.• Paramythis, A., & Liodl-Reisinger, S. (2003). Adaptive learning environments and e-learning

standards. European conference on E-Learning.• Pew Research Center. (2010). Millennials: a portrait of generation next.

http://pewsocialtrends.org/files/2010/10/millennials-confident-connected-open-to-change.pdf

IMAGE CREDITS

• http://web.mit.edu/newsoffice/2009/ai-overview-1207.html• http://s425.photobucket.com/albums/pp339/

ridizle4/?action=view&current=terminator.png&newest=1• http://www.llift.com/pages/platform.htm• http://www.gw.edu/academics/off/online/• http://www.braintrack.com/college-and-work-

news/articles/non-traditional-students-becoming-the-norm-10082502• http://www.drexel.edu/univrel/digest/archive/

110306/index.html