Learning Path Recommendation System for Programming Education Based on Neural Networks

Autor/inn/en	Saito, Tomohiro; Watanobe, Yutaka
Titel	Learning Path Recommendation System for Programming Education Based on Neural Networks
Quelle	In: International Journal of Distance Education Technologies, 18 (2020) 1, S.36-64, Artikel 3 (29 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1539-3100
DOI	10.4018/IJDET.2020010103
Schlagwörter	Programming; Computer Science Education; Electronic Learning; Instructional Materials; Prediction; Human Resources; Information Technology; Management Systems; Computer Assisted Instruction; Online Systems; Teaching Methods; Academic Ability; Data Analysis; Charts; Scores; Learning Processes; Classification; Visualization; Short Term Memory; Computer Software; Problem Solving + Suchen Sie Ihr Suchwort? Programmierung; Computer science lessons; Informatikunterricht; Lehrmaterial; Lehrmittel; Unterrichtsmedien; Vorhersage; Humankapital; Informationstechnologie; Computer based training; Computerunterstützter Unterricht; Online; Teaching method; Lehrmethode; Unterrichtsmethode; Auswertung; Diagram; Diagrams; Diagramm; Tabellarische Überischt; Tabelle; Learning process; Lernprozess; Classification system; Klassifikation; Klassifikationssystem; Visualisation; Visualisierung; Kurzzeitgedächtnis; Problemlösen
Abstract	Programming education has recently received increased attention due to growing demand for programming and information technology skills. However, a lack of teaching materials and human resources presents a major challenge to meeting this demand. One way to compensate for a shortage of trained teachers is to use machine learning techniques to assist learners. This article proposes a learning path recommendation system that applies a recurrent neural network to a learner's ability chart, which displays the learner's scores. In brief, a learning path is constructed from a learner's submission history using a trial-and-error process, and the learner's ability chart is used as an indicator of their current knowledge. An approach for constructing a learning path recommendation system using ability charts and its implementation based on a sequential prediction model and a recurrent neural network, are presented. Experimental evaluation is conducted with data from an e-learning system. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01