Abstract
McGraw-Hill Education’s new adaptive flashcard application, StudyWise, implements spaced practice to help learners memorize collections of basic facts. For classroom use, subject matter experts needed a scheduling algorithm that could provide effective practice schedules to learn a pre-set number of facts over a specific interval of days. To test the pedagogical effectiveness of such schedules, we used the ACT-R model of memorization to simulate learner responses. Each schedule has one 30 min study session per day, with overall study intervals that ranged from one day for sets of less than 30 items to three weeks for sets of two hundred or more items. In each case, we succeeded in tuning our algorithm to give a high probability the simulated learner answered each item correctly by the end of the schedule. This use of artificial intelligence allowed us to optimize the algorithm before engaging large numbers of real users. As real user data becomes available for this application, the simulated user model can be further tested and refined.
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Acknowledgments
The target practice schedules were provided by Katie Ward and the MHE Higher Ed team of SMEs. The original QuickTables algorithm was developed by Jean-Claude Falmagne and Eric Cosyn.
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Riedesel, M.A., Zimmerman, N., Baker, R., Titchener, T., Cooper, J. (2017). Using a Model for Learning and Memory to Simulate Learner Response in Spaced Practice. In: André, E., Baker, R., Hu, X., Rodrigo, M., du Boulay, B. (eds) Artificial Intelligence in Education. AIED 2017. Lecture Notes in Computer Science(), vol 10331. Springer, Cham. https://doi.org/10.1007/978-3-319-61425-0_81
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DOI: https://doi.org/10.1007/978-3-319-61425-0_81
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