LAUSR

In traditional education, there is not much difference between assessment tasks designed for learners. However, learners’ learning performance may vary due to a number of factors, e.g., learning ability, academic emotion, and learners’ and teachers’ academic expectations. Considering those factors, accurately recommending personalized assessment tasks for each learner is challenging. To overcome the limitations in the current work, this paper proposed an autonomous-agent-based approach to recommend personalized assessment tasks considering multiple factors. Contributions of the proposed approach contain three aspects: (1) Considering objective factors, the proposed approach involves dynamically adjusting the assessment tasks recommended for students by applying both item response theory and the proposed academic emotion influence model. (2) Considering subjective factors, the proposed approach can dynamically predict learners’ learning performances by applying autonomous agent-based negotiation. (3) The proposed recommendation algorithm based on discrete linear programming can effectively address the issue of cold start in typical recommendation algorithms. The experiments conducted in this paper demonstrate that the proposed approach effectively recommends assessment tasks for learners by considering both objective and subjective factors. The results indicate that this approach generates better recommendation outcomes than traditional content-based and collaborative filtering recommendation algorithms. Furthermore, the experiments reveal that the teacher’s personality is the primary factor affecting the recommendation results, while the degree of similarity between the teacher’s and learner’s personality also plays a role.