Visual analytics of video‐clickstream data and prediction of learners' performance using deep learning models in MOOCs' courses. Issue 4 (9th September 2020)

• Record Type: Journal Article
• Authors: Mubarak, Ahmed A.; Cao, Han; Zhang, Weizhen; Zhang, Wenli
• Other Names: Sunar Ayse S. guestEditor.; Leon‐Urrutia Manuel guestEditor.; White Su guestEditor.; Uhomoibhi James guestEditor.
• Is Part Of: Computer applications in engineering education. Volume 29:Issue 4(2021)
• Page Start: 710
• Page End: 732
• Publication Date: 2020-09-09
• Subjects: deep learning (LSTM) -- MOOCs courses -- prediction -- video‐clickstream -- visual analytics; Engineering -- Study and teaching (Higher) -- Periodicals; Engineering -- Computer-assisted instruction -- Periodicals; 620
• DOI: 10.1002/cae.22328
• Languages: English
• ISSNs: 1061-3773

Abstract

Abstract: 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%.