ContextKT: A Context-Based Method for Knowledge Tracing
نویسندگان
چکیده
Knowledge tracing, which is used to predict students’ performance based on their previous practices, has attracted many researchers’ attention. Especially in this rising period of intelligent education, knowledge tracing methods have been developed. However, most the existing focus personality practices and concepts but ignore contexts related studying process. In paper, we propose a context-based model, combines historical during mastery. To be specific, first define five for prediction. The basic context current state student, described by practice sequences. Then, QR-matrix defined represent relationship among questions, concepts, responses, describes questions knowledge. Furthermore, an improved LSTM model proposed capture memory forgetness, multi-head attention mechanism designed behaviors. Finally, captured contexts, prediction ContextKT established. Our evaluated two real educational datasets. experimental results show our effective efficient student prediction, it outperforms other methods.
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2022
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app12178822