KT-Bi-GRU: Student Performance Prediction with a Bi-Directional Recurrent Knowledge Tracing Neural Network

Autor/inn/en	Delianidi, Marina; Diamantaras, Konstantinos
Titel	KT-Bi-GRU: Student Performance Prediction with a Bi-Directional Recurrent Knowledge Tracing Neural Network
Quelle	In: Journal of Educational Data Mining, 15 (2023) 2, S.1-21 (21 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Delianidi, Marina) ORCID (Diamantaras, Konstantinos)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
Schlagwörter	Academic Achievement; Prediction; Cognitive Measurement; Bayesian Statistics; Models; Learning Processes; Brain; Cognitive Processes + Suchen Sie Ihr Suchwort? Schulleistung; Vorhersage; Analogiemodell; Learning process; Lernprozess; Gehirn; Cognitive process; Kognitiver Prozess
Abstract	Student performance is affected by their knowledge which changes dynamically over time. Therefore, employing recurrent neural networks (RNN), which are known to be very good in dynamic time series prediction, can be a suitable approach for student performance prediction. We propose such a neural network architecture containing two modules: (i) a dynamic sub-network including a recurrent Bi-GRU layer used for knowledge state estimation, (ii) a non-dynamic, feed-forward sub-network for predicting answer correctness based on the current question and current student knowledge state. The model modifies our previously proposed architecture and is different from all other existing models because it estimates the student's knowledge state considering only their previous responses. Thus the dynamic sub-network generates more stable knowledge state vector representations since they are independent of the current question. We studied both single-skill and multi-skill question scenarios and employed embeddings to represent questions and responses. In the multi-skill case the initialization of the question embedding matrix with pretrained word-embeddings is found to improve model performance. The experimental results showed that our current KT-Bi-GRU model and the previous one have similar performance while both surpassed the performance of previous state-of-the-art knowledge tracing models for five out of seven datasets where in some cases, the difference is quite noticeable. (As Provided).
Anmerkungen	International Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: https://jedm.educationaldatamining.org/index.php/JEDM
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01