Personalized Online Education Learning Strategies Based on Transfer Learning Emotion Classification Model

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.

Learning Management System in Education via Mobile App: Trends and Patterns in Mobile Learning

“…About 75% of the students did not participate in any video interactions by the end of the course, which could be one explanation. However, the approach in this paper plays a significant role in managing longer memory in big data sets, as anticipated [18].…”

Personalized Hybrid Recommendation Algorithm for MOOCs Based on Learners’ Dynamic Preferences and Multidimensional Capabilities

“…The proposed content-based MOOC recommendation algorithm has a higher accuracy rate than random recommendations. Notably, the richer the course information, the more accurate the recommendations [10,11].…”

“…It describes the proportion of learners' rated courses in the list of recommended courses. The recall rate is the proportion of correctly predicted positive samples to the actual positive samples, as shown in Equation (11); it describes the proportion of learners' rated courses in the list of recommended courses to the overall learner-rated courses in this study. L u denotes the list of recommended courses for learner u; B u denotes the list of courses rated by learner u; n denotes the total number of learners; U denotes the set of learners; P L denotes the overall recommendation precision rate; and R L denotes the overall recommendation recall rate.…”

Retracted: Personalized Online Education Learning Strategies Based on Transfer Learning Emotion Classification Model

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”