Pedagogical Interventions in SPOCs: Learning Behavior Dashboards and Knowledge Tracing Support Exercise Recommendation
Pedagogical Interventions in SPOCs: Learning Behavior Dashboards and Knowledge Tracing Support Exercise Recommendation
复制标题
SPOC 中的教学干预：学习行为仪表板和知识追踪支持练习建议
DOI：
10.1109/tlt.2023.3242712
复制
发表时间：
2023-06
影响因子：
3.7
通讯作者：
Xiaopeng Gao
中科院分区：
文献类型：
--
作者：
Han Wan;Zihao Zhong;Lina Tang;Xiaopeng Gao
小型私人在线课程（SPOCs）已经影响了中国高等教育的教与学。学习管理系统是SPOC的重要组成部分。他们可以收集与学生行为有关的各种数据，并支持教学干预。本研究使用特征工程和最近邻平滑模型来预测学生的表现。根据斯皮尔曼的等级相关系数与学生的最终成绩，选择了五个学习行为特征。通过对2020年秋季学期数据的测试，该模型获得了最高的ROC-AUC值0. 9390。基于这些模型，研究人员进行了参与干预，在2021年秋季向学生展示学习行为仪表板。在干预期间，课程平台每周更新仪表板并通知学生。通过随机对照试验进一步研究了这种干预。实验结果表明，干预可以改善学生在总学习时间，辅导阅读和视频观看方面的学习行为。此外，本研究使用一个改良的动态键-值记忆网络模型来描述学生的知识状态，并通过挖掘大量的练习记录来计算解决练习的概率。根据预测的概率，教师可以为每个学生推荐个性化的练习。2021年秋季，研究人员还对这一干预措施进行了随机对照试验，证明这种个性化的锻炼建议可以提高学生的概念掌握程度。实验表明，所提出的模式和干预措施对学生学习课程内容有积极的影响。
Small private online courses (SPOCs) have influenced teaching and learning in China's higher education. Learning management systems (LMSs) are important components in SPOCs. They can collect various data related to student behavior and support pedagogical interventions. This research used feature engineering and nearest neighbor smoothing models to predict the performance of students. Five learning behavior features were selected based on Spearman's rank correlation coefficients with students’ final grades. Through testing with data from the fall semester of 2020, the model attained the highest ROC-AUC value of 0.9390. Based on these models, the researchers conducted an engagement intervention that displayed learning behavior dashboards to students in the fall of 2021. During the intervention, the course platform updated the dashboards and notified students weekly. This intervention was further investigated through a randomized controlled trial. The experimental results suggested that the intervention could improve students’ learning behavior in terms of total study time, tutorial reading, and video viewing. In addition, this study used a modified dynamic key-value memory network model to depict a student's knowledge state and to calculate the probability of solving an exercise by mining numerous exercise records. Based on the predicted probability, instructors could recommend personalized exercises for each student. In the fall of 2021, the researchers also conducted a randomized controlled trial on this intervention, demonstrating that this personalized exercise recommendation could increase students’ concept mastery. Experiments revealed that the proposed models and interventions had a positive effect on students’ learning of course content.
登录
查看更多内容
DOI：
--
发表时间：
2019-07
期刊：
--
影响因子：
--
作者：
Fangzhe Ai;Yishuai Chen;Yuchun Guo;Yongxiang Zhao;Zhenzhu Wang;Guowei Fu;Guangyan Wang
通讯作者：
Fangzhe Ai;Yishuai Chen;Yuchun Guo;Yongxiang Zhao;Zhenzhu Wang;Guowei Fu;Guangyan Wang
影响因子：
5.5
作者：
El Aouifi H;El Hajji M;Es-Saady Y;Douzi H
通讯作者：
Douzi H
DOI：
--
发表时间：
2018-04
期刊：
J. Educ. Technol. Soc.
影响因子：
--
作者：
Samuel P. M. Choi;S. S. Lam-S.;K. Li;B. Wong
通讯作者：
Samuel P. M. Choi;S. S. Lam-S.;K. Li;B. Wong
DOI：
10.1109/iset.2018.00047
发表时间：
2018-07
期刊：
2018 International Symposium on Educational Technology (ISET)
影响因子：
--
作者：
B. Wong;K. Li
通讯作者：
B. Wong;K. Li
影响因子：
3.7
作者：
Bernardo Tabuenca;Sergio Serrano-Iglesias;Adrian Carruana-Martin;Cristina Villa-Torrano;Y. Dimitriadis;Juan I. Asensio-Pérez;Carlos Alario-Hoyos;E. Gómez-Sánchez;Miguel L. Bote-Lorenzo;A. Martínez-Monés;C. D. Kloos
通讯作者：
Bernardo Tabuenca;Sergio Serrano-Iglesias;Adrian Carruana-Martin;Cristina Villa-Torrano;Y. Dimitriadis;Juan I. Asensio-Pérez;Carlos Alario-Hoyos;E. Gómez-Sánchez;Miguel L. Bote-Lorenzo;A. Martínez-Monés;C. D. Kloos