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Learning Path Recommendation System for Programming Education Based on Neural Networks

Learning Path Recommendation System for Programming Education Based on Neural Networks
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Author(s): Tomohiro Saito (University of Aizu, Aizuwakamatsu, Japan) and Yutaka Watanobe (University of Aizu, Aizuwakamatsu, Japan)
Copyright: 2020
Volume: 18
Issue: 1
Pages: 29
Source title: International Journal of Distance Education Technologies (IJDET)
Editor(s)-in-Chief: Maiga Chang (Athabasca University, Canada)
DOI: 10.4018/IJDET.2020010103

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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.

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