A Review of AI-based Techniques in Online Course Recommendation: Metrics, Factors, and Research Methods

This paper presents a comprehensive literature review of online course recommendation systems, focusing on studies published between 2019 and 2023 across various academic databases. We selected and analyzed relevant research, covering key areas such as AI methods, research questions, influencing factors, and evaluation techniques. AI methods for course recommendation are categorized as traditional and advanced approaches. Traditional methods include Content-Based (CB) recommendation, Collaborative Filtering (CF), and related techniques, while advanced methods encompass deep learning and reinforcement learning (RL) techniques. Advanced AI methods demonstrate the ability to manage more complex data structures and provide more accurate and personalized recommendations compared to traditional methods. The common research challenges identified include accuracy, cold start, data sparsity, information overload, dynamic interest recommendation, and interpretability. We further analyzed the factors considered in these systems by categorizing user and course attributes. User attributes are divided into five categories: Demographic Information, User Behavior, Personal Preferences, Knowledge and Skills, and Emotional State and Social Interaction. Course attributes are categorized into four types: Course Metadata, Course Evaluation, Knowledge and Skills, and Social Influence. The most commonly used evaluation metrics include Recall (R), Precision (P) and F1-Score (F), which are straightforward to interpret and facilitate comparison across different studies. Additionally, we highlight future research directions, providing insights for further academic exploration in this domain.