How to Optimize Student Learning Using Student Models That Adapt Rapidly to Individual Differences

An important component of many Adaptive Instructional Systems (AIS) is a ‘Learner Model’ intended to track student learning and predict future performance. Predictions from learner models are frequently used in combination with mastery criterion decision rules to make pedagogical decisions. Importan...

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Bibliographic Details
Published in:	International journal of artificial intelligence in education 2023-09, Vol.33 (3), p.497-518
Main Authors:	Eglington, Luke G., Pavlik, Philip I.
Format:	Article
Language:	eng
Subjects:	Academic Achievement Adaptive systems Artificial Intelligence Computer Assisted Instruction Computer Science Computers and Education Criteria Data analysis Datasets Decisions Educational Technology Error Patterns Individual Differences Instructional Design Instructional Systems Intelligent Tutoring Systems Knowledge Learning Learning Processes Learning Theories Mastery Learning Mathematical models Model accuracy Parameters Pedagogy Probability Regression analysis Simulation Skills Students Systematic errors Tutoring User Interfaces and Human Computer Interaction
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Summary:	An important component of many Adaptive Instructional Systems (AIS) is a ‘Learner Model’ intended to track student learning and predict future performance. Predictions from learner models are frequently used in combination with mastery criterion decision rules to make pedagogical decisions. Important aspects of learner models, such as learning rate and item difficulty, can be estimated from prior data. A critical function of AIS is to have students practice new content once the AIS predicts that they have ‘mastered’ current content or learned it to some criterion. For making this prediction, individual student parameters (e.g., for learning rate) are frequently unavailable due to having no prior data about a student, and thus population-level parameters or rules-of-thumb are typically applied instead. In this paper, we will argue and demonstrate via simulation and data analysis that even in best-case scenarios, learner models assuming equal learning rates for students will inevitably lead to systematic errors that result in suboptimal pedagogical decisions for most learners. This finding leads us to conclude that systematic errors should be expected, and mechanisms to adjust predictions to account for them should be included in AIS. We introduce two solutions that can adjust for student differences “online” in a running system: one that tracks systemic errors of the learner model (not the student) and adjusts accordingly, and a student-level performance adaptive feature. We demonstrate these solutions’ efficacy and practicality on six large educational datasets and show that these features improved model accuracy in all tested datasets.
ISSN:	1560-4292 1560-4306