1. Center for Distributed Learning Dr. Rohan Jowallah Dr. Luke Bennett John Raible

2. Center for Distributed Learning ● Context and justification for the literature review ● Questions that guided the literature review process ● Framework for the literature reviewed ● General overview and framework ● Selection of an Adaptive Learning Technology Future ● Conclusion

3. Center for Distributed Learning The justification of this literature review was to: Examine the factors influencing adaptive learning technologies Acknowledge work that has been done before in the field of adaptive learning technologies; Gain knowledge of existing theories Explore dominant issues Context and Justification for the Literature Review

4. Center for Distributed Learning ● What are the key factors influencing the selection of adaptive learning technology in higher education? ● How can educational institutions prepare for the implementation of an adaptive learning system?

5. Center for Distributed Learning According Paramythis et. al (2004) “…a learning environment is considered adaptive if it’s capable of: ● Monitoring the activities of its users ● Interpreting these on the basis of domain-specific models ● Inferring user requirements and preferences out of the interpreted activities

6. Center for Distributed Learning ● Appropriately representing these in associated models; and, finally ● Acting upon the available knowledge on its users and the subject matter at hand to dynamically facilitate the learning process ● (Vassileva, 2012) System reads the learners needs and preferences

7. Center for Distributed Learning Twenty articles reviewed using systematic approach Key areas of the review Different models of adaptive learning technologies Factors influencing using adaptive learning technologies The impact of adaptive learning technologies on the design and delivery of courses

8. Center for Distributed Learning Adaptive Learning Technologies have been around since the early 1900s ( Mödritscher et, al 2004)

9. Center for Distributed Learning (Paramythis & Loidl-Reisinger, 2004)

10. Center for Distributed Learning (Paramythis & Loidl-Reisinger, 2003)

11. Center for Distributed Learning PLE- (Thropp, Friedman, & Elliott, 2011) ELF- (Siadaty, & Taghiyareh, 2007) ALF- (Šimko, Barla, & Bieliková, M. 2010) PLE- (Thropp, Friedman, & Elliott, 2011) ELF- (Siadaty, & Taghiyareh, 2007) ALF- (Šimko, Barla, & Bieliková, M. 2010) PLE- (Thropp, Friedman, & Elliott, 2011) ELF- (Siadaty, & Taghiyareh, 2007) ALF- (Šimko, Barla, & Bieliková, M. 2010)

12. Center for Distributed Learning Improved evaluation, customized curriculum, content adaptation, and self assessment (Paramythis et. al., 2004, Challis 2005, ). Real time data (Cahllis, 2005) Instructor can apply a range of methods (Nuri and Sevin, 2013) Transformation of teacher’s role (Ruiz, et. al., 2006). Tailored to learner’s needs (Calliss 2005). Enrichment opportunities (Challis 2005 p.524)

13. Center for Distributed Learning Immersive environment (Jones and Jo 2004) Flexibility of online content delivery (Challis 2005 ) Possible reduction in cheating(Challis 2005) Provides a personalized learning experience in a field often seen as impersonal (Vassileva, 2012) At Risk student identification Can leverage technology to support large class sizes

14. Center for Distributed Learning Immersive learning Addresses different learning styles Human interaction Modification of learning content Personalize learning and evaluation Independent learning operations Interactive content Advance data analytics Transferability of learning profiles Easy interpretation of data by students and teachers

15. Center for Distributed Learning Challenges Resources User Experience Technical

16. Center for Distributed Learning Student learning need Pedagogical issuesFaculty needs Administrative needs

17. Center for Distributed Learning University models MOOCs Changing role of the instructor. Federal support

18. Center for Distributed Learning

19. Major shift Strategic decision Making Multi-level buy-in strategy Consider: pedagogical, administrative, learner, and instructor needs

20. Center for Distributed Learning Challis, D. (2005). Committing to quality learning through adaptive online assessment. Assessment & Evaluation in Higher Education, 30(5), 519-527. De Marsico, M., Sterbini, A., & Temperini, M. (2013). A Framework to Support Social-Collaborative Personalized e-Learning. In Human-Computer Interaction. Applications and Services (pp. 351-360). Springer Berlin Heidelberg. http://link.springer.com/chapter/10.1007/978-3-642-39262-7_40#page-1 http://link.springer.com/chapter/10.1007/978-3-642-39262-7_40#page-1 Mödritscher, F., Garcia-Barrios, V. M., & Gütl, C. (2004). The Past, the Present and the Future of adaptive E-Learning. Proceedings of ICL 2004. Kara, N., and Sevim, N, (2013). "Adaptive Learning Systems: Beyond Teaching Machines." Contemporary Educational Technology 4.2 (2013). Paramythis A., Loidl-Reisinger S., and Kepler J. (2004). Adaptive Learning Environments and e- Learning Standards. Electronic Journal of eLearning, EJEL: Vol 2. Issue 1, p.181-194 Ruiz, J. G., Mintzer, M. J., & Leipzig, R. M. (2006). The impact of e-learning in medical education. Academic medicine, 81(3), 207- 212. Siadaty, M., & Taghiyareh, F. (2007, July). PALS2: Pedagogically adaptive learning system based on learning styles. In Advanced Learning Technologies, 2007. ICALT 2007. Seventh IEEE International Conference on (pp. 616-618). IEEE. Šimko, M., Barla, M., & Bieliková, M. (2010). ALEF: A framework for adaptive web-based learning 2.0. In Key Competencies in the Knowledge Society (pp. 367-378). Springer Berlin Heidelberg. Thropp, S. E., Friedman, M., & Elliott, M. (2011, January). Personal Learning Environment (PLE): A learner and data centric approach. In The Interservice/Industry Training, Simulation & Education Conference (I/ITSEC) (Vol. 2011, No. 1). National Training Systems Association. Vassileva, D. (2012). Adaptive E-learning content design and delivery based on learning styles and knowledge level. Serdica Journal of Computing, 6(2), 207p-252p. Zliobaite, I., Bifet, A., Gaber, M., Gabrys, B., Gama, J., Minku, L., & Musial, K. (2012). Next challenges for adaptive learning systems. ACM SIGKDD Explorations Newsletter, 14(1), 48-55. http://eprints.port.ac.uk/7904/1/sigkddExp2.pdf http://eprints.port.ac.uk/7904/1/sigkddExp2.pdf