期刊
INTERNATIONAL JOURNAL OF DISTANCE EDUCATION TECHNOLOGIES
卷 18, 期 1, 页码 36-64出版社
IGI GLOBAL
DOI: 10.4018/IJDET.2020010103
关键词
Ability Chart; Adaptive Learning; Awareness Computing; Deep Learning; Educational Data Mining; E-learning System; Online Judge System; Programming Education
资金
- JSPS KAKENHI [16K16174]
- Grants-in-Aid for Scientific Research [16K16174] Funding Source: KAKEN
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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