Literaturnachweis - Detailanzeige
| Autor/inn/en | Lee, Morgan P.; Croteau, Ethan; Gurung, Ashish; Botelho, Anthony F.; Heffernan, Neil T. |
|---|---|
| Titel | Knowledge Tracing over Time: A Longitudinal Analysis [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (16th, Bengaluru, India, Jul 11-14, 2023). |
| Quelle | (2023), (6 Seiten)
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| Zusatzinformation | Weitere Informationen |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Bayesian Statistics; Models; Generalizability Theory; Longitudinal Studies; Mastery Learning; Learning Analytics; Prediction; Systems Approach; Reliability; Markov Processes |
| Abstract | The use of Bayesian Knowledge Tracing (BKT) models in predicting student learning and mastery, especially in mathematics, is a well-established and proven approach in learning analytics. In this work, we report on our analysis examining the generalizability of BKT models across academic years attributed to "detector rot." We compare the generalizability of Knowledge Training (KT) models by comparing model performance in predicting student knowledge within the academic year and across academic years. Models were trained on data from two popular open-source curricula available through Open Educational Resources. We observed that the models generally were highly performant in predicting student learning within an academic year, whereas certain academic years were more generalizable than other academic years. We posit that the Knowledge Tracing models are relatively stable in terms of performance across academic years yet can still be susceptible to systemic changes and underlying learner behavior. As indicated by the evidence in this paper, we posit that learning platforms leveraging KT models need to be mindful of systemic changes or drastic changes in certain user demographics. [For the complete proceedings, see ED630829. Additional funding was provided by the U.S. Department of Education's Graduate Assistance in Areas of National Need (GAANN) program.] (As Provided). |
| Anmerkungen | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/ |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
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