The big data stored in massive open online course (MOOC) platforms have become a posed challenge in the Learning Analytics field to analyze the learning behavior of learners, and predict their respective performance, related especially to video lecture data, since most learners view the same online lecture videos. This helps to conduct a comprehensive analysis of such behaviors and explore various learning patterns in MOOC video interactions. This paper aims at presenting a visual analysis, which enables course instructors and education experts to analyze clickstream data that were generated by learner interaction with course videos. It also aims at predicting learner performance, which is a vital decision-making problem, by addressing their issues and improving the educational process. This paper uses a long short-term memory network (LSTM) on implicit features extracted from video-clickstreams data to predict learners' performance and enable instructors to make measures for timely intervention. Results show that the accuracy rate of the proposed model is 89%–95% throughout course weeks. The proposed LSTM model outperforms baseline Deep learning (GRU) and simple recurrent neural network by accuracy of 90.30% in the “Mining of Massive Datasets” course, and the “Automata Theory” accuracy is 89%.

中文翻译：

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在 MOOC 课程中使用深度学习模型对视频点击流数据进行可视化分析并预测学习者的表现

存储在海量开放在线课程 (MOOC) 平台中的大数据已成为学习分析领域在分析学习者的学习行为和预测他们各自的表现方面提出的挑战，尤其是与视频讲座数据相关的，因为大多数学习者看到的都是相同的在线讲座视频。这有助于对此类行为进行全面分析，并探索 MOOC 视频交互中的各种学习模式。本文旨在提供一种可视化分析，使课程讲师和教育专家能够分析学习者与课程视频互动产生的点击流数据。它还旨在通过解决学生的问题和改进教育过程来预测学习者的表现，这是一个重要的决策问题。本文对从视频点击流数据中提取的隐式特征使用长短期记忆网络 (LSTM) 来预测学习者的表现，并使教师能够采取措施及时干预。结果表明，所提出的模型在整个课程周的准确率为 89%–95%。所提出的 LSTM 模型在“海量数据集挖掘”课程中以 90.30% 的准确率优于基线深度学习 (GRU) 和简单的循环神经网络，而“自动机理论”的准确率为 89%。