Factors-Influencing-the-Selection-of-an-Adaptive

Report
Dr. Rohan Jowallah
Dr. Luke Bennett
John Raible
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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
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Context and Justification for the
Literature Review
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
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● 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?
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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
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● 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
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Twenty articles reviewed using systematic approach
Key areas of the review
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Different models of adaptive learning technologies
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Factors influencing using adaptive learning technologies
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The impact of adaptive learning technologies on the
design and delivery of courses
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Adaptive Learning Technologies have been around since the early 1900s ( Mödritscher et, al
2004)
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(Paramythis & Loidl-Reisinger, 2004)
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(Paramythis & Loidl-Reisinger, 2003)
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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)
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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)
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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
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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
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Challenges
Technical
User
Experience
Resources
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Student learning
need
Pedagogical issues
Faculty needs
Administrative
needs
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University models
MOOCs
Changing role of the instructor.
Federal support
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Major shift
Strategic decision Making
Multi-level buy-in strategy
Consider: pedagogical, administrative,
learner, and instructor needs
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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
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 eLearning 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), 207212.
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.
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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

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