Ontology and Machine Learning-Based Recommender System for Teacher Resource Personalization

Abstract

Teachers face challenges when conducting their jobs (stress, work overload, lack of resources, lack of time, etc.), which may lead them to psychological problems. With the current information overload and sparsity of resources, it can be challenging to find relevant educational resources promptly. Recommender systems are designed to address the issue of information overload by filtering relevant information from a large volume of data based on user preferences, interests, or observed behavior. A recommender system can help mitigate teachers’ struggles by recommending personalized resources based on teachers’ needs. This paper presents previous works related to recommender systems in education. It highlights their techniques and limitations. Some papers relied on machine learning and/or ontology for building recommender systems, while others relied on a hybrid system comprising several techniques. The most employed recommendation techniques include collaborative filtering (CF), content-based (CB), and knowledge-based (KB) approaches. Each approach has its advantages and limitations. To overcome these limitations, several advanced recommendation methods have been proposed, such as social network-based recommender systems, fuzzy recommender systems, context awareness-based recommender systems, and group recommender systems. Our analysis reveals that existing recommender systems are learner-centered, often lacking an understanding of the teacher’s context. The continuous advancement of recommendation approaches and techniques has led to the implementation of numerous recommender systems and the development of numerous real-world applications. A context-aware personalized recommender system for teachers should consider personal and professional development goals and psychosocial state when presenting a recommendation. Years of experience, access to equipment, and commute time are some of the aspects that should be considered when designing such a system. Moreover, the studies surveyed provided detailed information about their evaluation methodologies. However, the evaluation of these systems is typically conducted using simulated or nonreal students, along with various assessment approaches such as algorithmic performance tests, statistical analysis, questionnaires, and qualitative observations.