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T-BMIRT: Estimating representations of student knowledge and educational components in online education

  • Beihang University

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

A large amount of data generated by students in online education can be used to improve the quality of education. The important task of online education is to estimate the student proficiency and the characteristics of educational components. We developed the T-BMIRT model: a temporal, multidimensional, IRT-based method for estimating the above parameters. The model added learning video parameters and modeled the student proficiencies over time as a random process, accounting for the student learning and forgetting process. And it was extended to multidimensional to estimate the educational components which contain multiple skills. So the model can describe the student learning trajectories in an online education system. In addition, we evaluated this model by predicting student next response to assessment, and found it is better than the IRT and temporal IRT models on each dataset we used, especially when the dataset contains learning videos interactions.

源语言英语
主期刊名Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
编辑Jian-Yun Nie, Zoran Obradovic, Toyotaro Suzumura, Rumi Ghosh, Raghunath Nambiar, Chonggang Wang, Hui Zang, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Xiaohua Hu, Jeremy Kepner, Alfredo Cuzzocrea, Jian Tang, Masashi Toyoda
出版商Institute of Electrical and Electronics Engineers Inc.
1301-1306
页数6
ISBN(电子版)9781538627143
DOI
出版状态已出版 - 1 7月 2017
活动5th IEEE International Conference on Big Data, Big Data 2017 - Boston, 美国
期限: 11 12月 201714 12月 2017

出版系列

姓名Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
2018-January

会议

会议5th IEEE International Conference on Big Data, Big Data 2017
国家/地区美国
Boston
时期11/12/1714/12/17

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