@inproceedings{16f10bad811d46669c41f59d031d97f1,
title = "A Deep Reinforcement Learning Framework for Instructional Sequencing",
abstract = "Reinforcement Learning, a common framework for AI planing or decision making, is regarded as an effective framework fro planning students' learning sequences. However, previous research efforts rely on simulation data or students and few of them have verified effectiveness of their algorithms with teaching students in real educational environments. Also, the previous research is hard to using in real task such as online course or real classroom because of the strong assumptions and restrictions. In this paper, We propose a new deep reinforcement learning framework for instructional sequencing that can recommend students with personalized learning exercises in both MOOCs and classrooms. Based on our ATC model, is used to trace students learning process and Deep Reinforcement Learning Agent is used to induce efficient l earning sequences for students adaptively. Both simulation and experiment in classrooms confirm the effectiveness of our method.",
keywords = "Adaptive Learning, Deep Reinforcement Learning, Instructional Sequencing, Student Model",
author = "Yanjun Pu and Caimeng Wang and Wenjun Wu",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 8th IEEE International Conference on Big Data, Big Data 2020 ; Conference date: 10-12-2020 Through 13-12-2020",
year = "2020",
month = dec,
day = "10",
doi = "10.1109/BigData50022.2020.9378463",
language = "英语",
series = "Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5201--5208",
editor = "Xintao Wu and Chris Jermaine and Li Xiong and Hu, \{Xiaohua Tony\} and Olivera Kotevska and Siyuan Lu and Weijia Xu and Srinivas Aluru and Chengxiang Zhai and Eyhab Al-Masri and Zhiyuan Chen and Jeff Saltz",
booktitle = "Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020",
address = "美国",
}