Literaturnachweis - Detailanzeige
Autor/in | Yudelson, Michael V. |
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Titel | Individualizing Bayesian Knowledge Tracing. Are Skill Parameters More Important than Student Parameters? [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (9th, Raleigh, NC, Jun 29-Jul 2, 2016). |
Quelle | (2016), (6 Seiten)
PDF als Volltext |
Sprache | englisch |
Dokumenttyp | gedruckt; online; Monographie |
Schlagwörter | Bayesian Statistics; Markov Processes; Intelligent Tutoring Systems; Goodness of Fit; Accuracy; Student Evaluation; Hierarchical Linear Modeling |
Abstract | Bayesian Knowledge Tracing (BKT) models were in active use in the Intelligent Tutoring Systems (ITS) field for over 20 years. They have been intensively studied, and a number of useful extensions to them were proposed and experimentally tested. Among the most widely researched extensions to BKT models are various types of individualization. Individualization, broadly defined, is a way to account for variability in students that are working with the ITS that uses BKT model to represent and track student learning. One of the approaches to individualizing BKT is to split its parameters into per-skill and per-student components. In this work, we are proposing an approach to individualizing BKT that is based on Hierarchical Bayesian Models (HBM) and, in addition to capturing student-level variability in the data, weighs the contribution of per-student and per-skill effects to the overall variance in the data. [For the full proceedings, see ED592609.] (As Provided). |
Anmerkungen | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org |
Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
Update | 2020/1/01 |