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Enhancing Personalization by Integrating Top-Down and Bottom-Up Approaches to Learner Modeling by Diego Zapata-Rivera; Burcu Arslan is a book available to read on EtoBox.

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Learner models are representations of the learner's knowledge, skills and other attributes used by Adaptive Instructional Systems (AISs) to personalize their interactions with the learners (e.g., by implementing adaptive feedback, and recommending tasks/activities). Top-down and bottom-up approaches to learner modeling provide various affordances and challenges in terms of the need for interpretable learner models, the amount of data available, the complexity of the model, and the amount of human effort needed to implement and validate learner models. Research shows that hybrid approaches involving both top-down and bottom-up approaches are needed to effectively deal with the challenges of learner modeling in AISs. This paper describes several learner modeling approaches for integrating top-down and bottom-up approaches to gather additional evidence for supporting assessment claims and implementing personalization approaches. We elaborate on several learner modeling issues, including (a) evidence identification and aggregation in assessment systems; (b) making sense of process data aimed at supporting assessment claims related to learner cognition; and (c) approaches for improving

Author

Diego Zapata-Rivera; Burcu Arslan

Publisher

Springer International Publishing AG

Published

2021

Language

EN

ISBN

9783030778736

Subjects

Computer Science, Psychology, Education