WHAT TO LEARN NEXT: INCORPORATING STUDENT, TEACHER AND DOMAIN PREFERENCES FOR A COMPARATIVE EDUCATIONAL RECOMMENDER SYSTEM

In contrast to traditional teaching methods, attention is growing in modern teaching approaches regarding the personalization of education. One important aspect of constructing a personalized leaning environment is supporting learners with automated guidance and suggestions concerning what is more suitable for them to learn. However, these recommendations may have different sources and are influenced by multiple factors. The objective of this research is to answer the question, how to fuse and balance different recommendation approaches and perspectives to give students a suitable recommendation on which topics to learn in a domain of learning? Different recommendation approaches may yield the potential for different suggestions of the recommended learning material. For this reason, the assessment of these recommendations and their influence on the student is essential to ensure that each individual learner is linked to a suitable recommending source, which in turn will increase the acceptance of the recommendation and the trust that the learner has in the recommender itself. In this paper, three recommender approaches are developed in detail, outlining the frameworks of how implementation, analysis and comparison can be undertaken. The first recommender is based on the student's learning preferences. These are acquired through a conversational engine that handles the system's interaction with the student. This conversational feedback loop provides students with a human-like interaction that enhances the experience of the learner. The second recommender reflects the preferences of the teacher, incorporating the expert opinion into the recommendation. It is designed to provide the student with the set of teachers' preferences and influence the conversation between the learner and the recommendation system. The third recommender is built upon a domain network. It represents the internal links between the topics in the learning domain, which in turn affects the recommended learning path for each student. To account for student's feedback, an evaluation process is designed and combined with the recommender in one framework, which is designed focusing on the requirements of educational environment.