Summary
Summary and Conclusions:
STRENGTHS:
The potential to personalize/individualize learning in a variety of ways
Increase motivation and student autonomy through intelligent implementation of feedback-loop systems
Support more inclusive education by adapting and accommodating to different student needs
Offers real time feedback
Can be easily gamified to increase student engagement and motivation
Saves instructors time and energy. Instructors can more quickly identify and close learning gaps
Ability to collect data on HOW students learn
Provides increasingly precise recommendations and levels for students
WEAKNESSES:
Confusion/ambiguity around the term "adaptive learning technology". EdTech solutions can use the word "adaptive" as a synonym for "flexible" in promotional materials without offering any of the technical features of truly adaptive solutions.
Current access to fully adaptive stand-alone OR holistic solutions is limited
There is risk pertaining to the limitations of early algorithms (ie: creating student frustration)
Privacy concerns around personal data that is collected and used by the system
Risks pertaining to how a solution is used; there are many different kinds of adaptive solutions, and Instructors may require new training and perspectives
OPPORTUNITIES:
There is a challenge for adaptive programmers to determine THE BEST WAY to adapt learning in different contexts
In the short-term, there is opportunity to make better use of student analytics in current LMS' and to help teacher's access and understand how to use the data
In the longer term, adaptive learning offers opportunities to make education more inclusive through personalization and increase student motivation and autonomy.
THREATS:
Current access to fully adaptive solutions is limited
Early solutions may create negative experiences for teachers and learners
Incorrect methods of adaptation may be applied in a given context and create negative learning experiences
Tell us What You Think:
In the ETEC 522 Blog, answer one of the questions posed in the "something to ponder" sections, or share your experience using Cerego. For your convenience, all questions have been collected for you here:
QUESTION CLUSTERS:
Take a moment to consider where adaptive learning technologies might evolve in the future. Do you think the major Learning Management Systems (LMS') will continue to enhance the use of student data/analytics or will NEW LMS' disrupt the market with advanced adaptive features? Perhaps new LMS' will force the dominant ones to innovate and offer more robust adaptive functionalities? What about stand-alone adaptive solutions? These technologies tend to compliment the current EdTech eco-system and might offer less resistance to adoption in the short-term.
Adaptive learning systems function on many of the same principles as educational games, leveraging the power of the "feedback loop" to determine the ideal difficulty level. Are adaptive learning systems the final frontier of gamification? Or is it more fair to say that all forms of gamification use adaptive algorithms but not all adaptive algorithms are gamification? Additionally, can gamified adaptive learning technologies potentially replace the role of the teacher, or should it be used as a support tool in guided learning?
Consider adaptive navigation: According to our research, it is the second most common use of adaptive learning tech, and can manifest in two basic forms:
learner controlled
system controlled
A learner controlled system acts as more of a "recommender system" whereas "system controlled" forces the learner down a particular learning pathway. Does a 'system controlled' pathway reduce a student's ability to take charge of his/her/their learning (learner autonomy), or does it reinforce it by giving students the tools they may not realize they need to be successful?
Did you enjoy the platform? Were you a fan of the constant emails?
What does being "adaptable" mean to you? There's no perfect way of teaching that works for every single student — is it worthwhile then to think more about how we can help students become more adaptable?
REFERENCES:
Awais Hassan, M., Habiba, U., Khalid, H., Shoaib, M., & Arshad, S. (2019). An Adaptive Feedback System to Improve Student Performance Based on Collaborative Behavior. IEEE Access, 7, 107171–107178. https://doi.org/10.1109/access.2019.2931565
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Besser, A., Flett, G. L., & Zeigler-Hill, V. (2020). Adaptability to a sudden transition to online learning during the COVID-19 pandemic: Understanding the challenges for students. Scholarship of Teaching and Learning in Psychology, 1-21.
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DreamBox Learning. (2020, May 4). DreamBox Learning Adaptive Learning - What Is It? DreamBox Learning. https://www.dreambox.com/adaptive-learning.
EdSurge. (2021, February 9). Shedding Light on the Adaptive Black Box: Adaptive Learning Close Up - EdSurge Research. EdSurge. https://www.edsurge.com/research/reports/adaptive-learning-close-up.
Hassan, M. A., Habiba, U., Majeed, F., & Shoaib, M. (2019). Adaptive gamification in e-learning based on students’ learning styles. Interactive Learning Environments, 29(4), 545–565. https://doi.org/10.1080/10494820.2019.1588745
Hursey, A., Thompson, K., Wierzba, J., Tidwell, E., Livingston, J., & Lewis, J. (2020). Falling forward: Lessons learned from real-life implementation of adaptive learning solutions. (pp. 117-129). Springer International Publishing. https://doi.org/10.1007/978-3-030-50788-6_9
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Moltudal, S., Høydal, K., & Krumsvik, R. J. (2020). Glimpses into real-life introduction of adaptive learning technology: A mixed methods research approach to personalised pupil learning. Designs for Learning, 12(1), 13-28. https://doi.org/10.16993/dfl.138
Taskin Y., Hecking T., Hoppe H.U., Dimitrova V., Mitrovic A. (2019) Characterizing Comment Types and Levels of Engagement in Video-Based Learning as a Basis for Adaptive Nudging. In: Scheffel M., Broisin J., Pammer-Schindler V., Ioannou A., Schneider J. (eds) Transforming Learning with Meaningful Technologies. EC-TEL 2019. Lecture Notes in Computer Science, vol 11722. Springer, Cham. https://doi.org/10.1007/978-3-030-29736-7_27