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A Personalized MOOC Learning Group and Course Recommendation Method Based on Graph Neural Network and Social Network Analysis

Zijin Luo, Xu Wang, Yiquan Wang, Haotian Zhang, Zhuangzhuang Li

TL;DR

New insights are offered into the potential of personalised teaching on MOOC platforms, demonstrating the value of data-driven and AI-assisted methods in improving the quality of online learning experiences, increasing student engagement, and enhancing learning outcomes.

Abstract

In order to enhance students' initiative and participation in MOOC learning, this study constructed a multi-level network model based on Social Network Analysis (SNA). The model makes use of data pertaining to nearly 40,000 users and tens of thousands of courses from various higher education MOOC platforms. Furthermore, an AI-based assistant has been developed which utilises the collected data to provide personalised recommendations regarding courses and study groups for students. The objective is to examine the relationship between students' course selection preferences and their academic interest levels. Based on the results of the relationship analysis, the AI assistant employs technologies such as GNN to recommend suitable courses and study groups to students. This study offers new insights into the potential of personalised teaching on MOOC platforms, demonstrating the value of data-driven and AI-assisted methods in improving the quality of online learning experiences, increasing student engagement, and enhancing learning outcomes.

A Personalized MOOC Learning Group and Course Recommendation Method Based on Graph Neural Network and Social Network Analysis

TL;DR

New insights are offered into the potential of personalised teaching on MOOC platforms, demonstrating the value of data-driven and AI-assisted methods in improving the quality of online learning experiences, increasing student engagement, and enhancing learning outcomes.

Abstract

In order to enhance students' initiative and participation in MOOC learning, this study constructed a multi-level network model based on Social Network Analysis (SNA). The model makes use of data pertaining to nearly 40,000 users and tens of thousands of courses from various higher education MOOC platforms. Furthermore, an AI-based assistant has been developed which utilises the collected data to provide personalised recommendations regarding courses and study groups for students. The objective is to examine the relationship between students' course selection preferences and their academic interest levels. Based on the results of the relationship analysis, the AI assistant employs technologies such as GNN to recommend suitable courses and study groups to students. This study offers new insights into the potential of personalised teaching on MOOC platforms, demonstrating the value of data-driven and AI-assisted methods in improving the quality of online learning experiences, increasing student engagement, and enhancing learning outcomes.

Paper Structure

This paper contains 40 sections, 6 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Social Network Analysis (SNA)
  4. Graph Neural Network (GNN)
  5. Group Recommendation Systems in Collaborative Learning
  6. AI assisted teaching
  7. Materials and Methods
  8. Data Collection
  9. Analysis Tools and Models
  10. User Preference Analysis
  11. The Relationship Between Preference and Learning Engagement
  12. Analysis of Learning Groups Based on GNN
  13. Building a Study Group
  14. Application of GNN Model
  15. Results
  16. ...and 25 more sections

Figures (6)

  • Figure 1: Flow Chart of Personalized Recommendation System
  • Figure 2: The GNN flowchart
  • Figure 3: The Simplified GNN Model: Student-Course-Teacher Graph
  • Figure 4: Clustering results of different preferences
  • Figure 5: Rand Index for students, working professionals, and other groups
  • ...and 1 more figures