Abstract
Due to the epidemic, online course learning has become a major learning method for students worldwide. Analyzing its massive data from the massive online education platforms becomes a challenge because most learners watch online instructional videos. Thus, analyzing learners’ learning behaviors is beneficial to implement personalized online learning strategies with sentiment classification models. To this end, we propose a context-aware network model based on transfer learning that aims to predict learner performance by solving learners’ problems and improving the educational process, contributing to a comprehensive analysis of such student behavior and exploring various learning models in MOOC video interactions. In addition, we visualize and analyze MOOC video interactions, enabling course instructors and education professionals to analyze clickstream data generated by learners interacting with course videos. The experimental results show that, in the process of “massive data mining,” personalized learning strategies of this model can efficiently enhance students’ interest in learning and enable different types of students to develop personalized online education learning strategies.
Highlights
In the last decades, technological advances have played an important and prominent role in the development of educational processes
This paper aims to propose a context-aware network model based on transfer learning, which aims to predict learners’ performance by solving their problems and improving the educational process, contributing to a comprehensive analysis of such student behavior and exploring various learning models in Massive open online course (MOOC) video interactions [22]
E behavior of learners watching videos to complete the first task in a given week is classified as a community gathering within the network based on structural clustering generated based on structural identity, which is closely related to kindness
Summary
Technological advances have played an important and prominent role in the development of educational processes. The impact of the epidemic has made learning through online course platforms (e.g., Coursera, edX, and Udacity [2]) the main way of learning for students worldwide, which offer mainly videobased course, quizzes, and forums [3]. E personalized design of the online video course plays an important role in the interest of the learners and is the main fulcrum to attract students to continue learning the course. Online learning platforms can store learner data in weblogs, which include their personal information and interactions with course content (e.g., videos, clickstreams events, forum discussions, and assessments). Most learners spend most of their learning time watching video course, and as a result, many problems with learner-video interactions have gradually emerged [4]
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