Abstract
Asking whether adaptivity improves performance is the wrong question. The right question is what kinds of adaptivity should be used to tailor the interactions between learner, context, objective, and instructional approach to maximize learning and performance. Most research on adaptive learning has focused on learning in intelligent tutoring systems and other digital learning environments. However, there is a lack of research that focuses on retention and deeper learning. This paper will define adaptivity, review different types of adaptivity used for instruction and their effects within the learning environment and longitudinally, and give some examples of how we have used adaptivity for short- and long-term improvement in performance and learning. We conclude that adaptivity in learning environments should be used to focus on deep conceptual learning promoting long term results.
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Haynes, J.A., Underwood, J.S., Pokorny, R., Spinrad, A. (2014). What Is Adaptivity? Does It Improve Performance?. In: Schmorrow, D.D., Fidopiastis, C.M. (eds) Foundations of Augmented Cognition. Advancing Human Performance and Decision-Making through Adaptive Systems. AC 2014. Lecture Notes in Computer Science(), vol 8534. Springer, Cham. https://doi.org/10.1007/978-3-319-07527-3_21
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DOI: https://doi.org/10.1007/978-3-319-07527-3_21
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