A Reinforcement Learning Based RecommendationSystem to Improve Performance of Students in Outcome Based Education Model

Abstract

Students are a gold asset for each country. Proper guidance/recommendation to the students regarding their education-related issues can ultimately result in uplifting the economy of a country. Different education models are followed in the world, out of which Outcome Based Education (OBE) is the one. OBE education model comprises three main components, which include Program Educational Objectives (PEOs), Program Learning Outcomes (PLOs), and Course Learning Outcomes (CLOs). CLOs are outcomes that a student achieves after studying a course. A single course may contain one or more CLOs. These CLOs are then mapped to PLOs and PLOs are then mapped to PEOs. Therefore, our objective in this work is to improve deficient/weak CLOs of students by suggesting online resources. Whereas, in the absence of this proposed system, students have to find out these resources by themselves or course teacher recommends relevant online resources. To achieve this objective, we created a dataset for the OBE education model, as to date no standard dataset exists on the OBE education model. From this dataset, we created a Student-to-CLO matrix and performed bi-clustering on this matrix to find groups of students having similar performance in different CLOs. So far, Bi-clustering has been used in the Bio-informatics field to determine similarity in gene expression data. Generated bi-clusters are sorted according to their homogeneity of contained values. These sorted bi-clusters are then mapped to a 2D grid to formulate a reinforcement-learning environment. The start state of the recommendation agent is determined using cosine similarity. If an agent visits a state, deficient CLOs of that state are recommended to the student. The agent can visit only those states that are nearby to its current state and accessible through its legal action space. An optimal sequence of actions to visit different states of a 2D grid, which can improve student’s performance, is determined using Q-learning. Online resources including research articles, YouTube videos, books, and online tutorials are suggested to the student to improve his deficient CLOs using a mobile app.

Keywords

• Outcome based education (OBE)
• recommender systems
• reinforcement learning
• Q-learning
• course learning outcome (CLO)