Adaptive Learning in the Library

1. Adaptive Learning in the Library Designing a sustainable and effective online instruction program Joelle Pitts Assistant Professor | Instructional Design Librarian Kansas State University

2. Overview • Distance Student Behaviors and Expectations • Adaptive Learning • Library Applications

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

4. Great Plains IDEA Faculty • Most teach on campus • Most are under pressure • Most completed their graduate work using outdated technologies • Focus is getting content online • Most assume their audience is non-traditional • And knows how to conduct research • Information literacy gaps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

5. Online = Non-traditional?

6. Non-traditional?

7. Non-traditional?

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

9. Non-traditional GPIDEA students Behavior Expectations Consistency Format Efficiency Cost • 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

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

11. Traditional GPIDEA students Behavior Expectations Consistency Format Efficiency Cost • 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 Pew Research Center (2010)

12. GPIDEA Common Student Behaviors and Expectations Behavior Expectations Consistency Format Efficiency Cost • Library use • Aren’t aware they are able to access it as a distance student • Perception of the library is print based Information Literacy Level? Online ≠ non-traditional

13. Adaptive Learning

14. 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)

15. This…

16. Not this…

17. 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)

18. 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)

19. Adaptation Process Brusilovsky & Maybury (2002)

20. Modeling Jacko (2009)

21. Categories of Adaptation • Interaction with the system • Course/object delivery • Content adaptation • Collaborative/social support Paramythis & Liodl-Reisinger (2003)

22. 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)

23. Assessment • System feedback • Embedded assessment • Adaptive • Timing/architecture • Question level

24. Examples • Adaptive eLearning Research Group • AHA! • Andes Physics Tutor • ELM-ART • GRE • iKnow! • Learnthat • Khan Academy • Knewton • More…

25. 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

26. 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