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Predictive learning analytics using deep learning model in MOOCs' courses videos

机译：使用Moocs课程视频中使用深学习模型的预测学习分析

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摘要

Analysis of learning behavior of MOOC enthusiasts has become a posed challenge in the Learning Analytics field, which is especially related to video lecture data, since most learners watch the same online lecture videos. It helps to conduct a comprehensive analysis of such behaviors and explore various learning patterns for learners and predict their performance by MOOC courses video. This paper exploits a temporal sequential classification problem by analyzing video clickstream data and predict learner performance, which is a vital decision-making problem, by addressing their issues and improving the educational process. This paper employs a deep neural network (LSTM) on a set of implicit features extracted from video clickstreams data to predict learners' weekly performance and enable instructors to set measures for timely intervention. Results show that accuracy rate of the proposed model is 82%-93% throughout course weeks. The proposed LSTM model outperforms baseline ANNs, Super Vector Machine (SVM) and Logistic Regression by an accuracy of 93% in real used courses' datasets.
机译：MooC爱好者的学习行为分析在学习分析领域已经成为一个构成的挑战，这与视频讲座数据特别相关，因为大多数学习者观看相同的在线讲座视频。它有助于对这些行为进行全面分析，并探索学习者的各种学习模式，并通过MooC课程视频预测其表现。本文通过分析视频点击流数据并预测学习者性能来利用时间顺序分类问题，这是一个重要的决策问题，通过解决他们的问题并改善教育过程。本文采用深度神经网络（LSTM）从视频点击流程中提取的一组隐式功能，以预测学习者的每周性能并使教师能够设置及时干预的措施。结果表明，拟议型号的准确率在课程周内为82％-93％。所提出的LSTM模型优于基线ANN，超级向量机（SVM）和Logistic回归，在真正使用的课程数据集中的准确性为93％。

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