Literaturnachweis - Detailanzeige
| Autor/inn/en | Halpern, David; Tubridy, Shannon; Wang, Hong Yu; Gasser, Camille; Popp, Pamela Osborn; Davachi, Lila; Gureckis, Todd M. |
|---|---|
| Titel | Knowledge Tracing Using the Brain [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (11th, Raleigh, NC, Jul 16-20, 2018). |
| Quelle | (2018), (10 Seiten)
PDF als Volltext kostenfreie Datei |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Learning Processes; Memory; Prediction; Second Language Learning; Diagnostic Tests; Brain Hemisphere Functions; Language Tests; Models; Accuracy; Neurosciences; Program Descriptions; Markov Processes |
| Abstract | Knowledge tracing is a popular and successful approach to modeling student learning. In this paper we investigate whether the addition of neuroimaging observations to a knowledge tracing model enables accurate prediction of memory performance in held-out data. We propose a Hidden Markov Model of memory acquisition related to Bayesian Knowledge Tracing and show how continuous functional magnetic resonance imaging (fMRI) signals can be incorporated as observations related to latent knowledge states. We then show, using data collected from a simple second-language learning experiment, that fMRI data acquired during a learning session can be used to improve predictions about student memory at test. The fitted models can also potentially give new insight into the neural mechanisms that contribute to learning and memory. [For the full proceedings, see ED593090.] (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 |
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