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Title: Combining adaptivity with progression ordering for intelligent tutoring systems
Learning at scale (LAS) systems like Massive Open Online Classes (MOOCs) have hugely expanded access to high quality educational materials however, such materials are frequently time and resource expensive to create. In this work we propose a new approach for automatically and adaptively sequencing practice activities for a particular learner and explore its application for foreign language learning. We evaluate our system through simulation and are in the process of running an experiment. Our simulation results suggest that such an approach may be significantly better than an expert system when there is high variability in the rate of learning among the students and if mastering prerequisites before advancing is important. They also suggest it is likely to be no worse than an expert system if our generated curriculum approximately describes the necessary structure of learning in students.
Authors:
; ; ;
Award ID(s):
1657176
Publication Date:
NSF-PAR ID:
10067110
Journal Name:
Work in Progress, Learning at Scale 2018
Sponsoring Org:
National Science Foundation
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