Exploring Neural Network Models for the Classification of Students in Highly Interactive Environments [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, Jul 2-5, 2019).

Autor/inn/en	Käser, Tanja; Schwartz, Daniel L.
Titel	Exploring Neural Network Models for the Classification of Students in Highly Interactive Environments [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, Jul 2-5, 2019).
Quelle	(2019), (10 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Educational Environment; Interaction; Cluster Grouping; Models; Short Term Memory; Educational Games; Accuracy; Student Behavior; Prediction; Middle School Students; Grade 8; Sequential Approach; Discovery Learning; Intervention + Suchen Sie Ihr Suchwort? Lernumgebung; Pädagogische Umwelt; Schulumwelt; Interaktion; Eingruppierung; Analogiemodell; Kurzzeitgedächtnis; Educational game; Lernspiel; Student behaviour; Schülerverhalten; Vorhersage; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; School year 08; 8. Schuljahr; Schuljahr 08; Schrittfolge; Entdeckendes Lernen
Abstract	Open-ended learning environments (OELEs) allow students to freely interact with the content and to discover important principles and concepts of the learning domain on their own. However, only some students possess the necessary skills for efficient and effective exploration. Guidance in the form of targeted interventions or feedback therefore has the potential to improve educational outcomes. A promising approach for adaptation in OELEs is the design of interventions based on the detection of characteristic learning behaviors through offline clustering, followed by a real-time classification of new students. In this paper, we explore the possibility of using recurrent neural network (RNN) models for this online classification task. We extensively evaluate the predictive performance of different variants of RNNs, namely long-short term memory models and gated recurrent units, and different architectures on a data set collected from an exploration-based educational game. We also compare the prediction accuracy of the different RNN models to the performance of traditional classifiers on the same data set. Our results demonstrate that RNNs perform similar or better than traditional methods regarding early classification and therefore constitute a promising alternative for the online classification of new students. [For the full proceedings, see ED599096.] (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