Recommendation System Model for Personalized Learning in Higher Education using Content-Based Filtering Method

- An online learning system is a learning approach that typically uses a one fit for all approach in which all student abilities are considered the same so that the provision of learning materials provided is the same for all students. In contrast, a personalization learning system is a teaching and learning approach in which students are positioned as the learning centers and the students are motivated and facilitated to overcome their learning needs based on their learning speed and understanding of the learning process carried out to achieve. In the context of learning, learning materials and learning objectives are the main components that are interrelated in achieving the expected learning outcomes. However, the relationship between learning materials, especially in prerequisite courses, must have similarities so that the learning process can continue in accordance with the learning outcomes to be achieved. This paper presents an experiment results of the proposed the recommendation system modeling for personalization learning model implemented using Content-Based Collaborative Filtering model. In the proposed recommender system model, similarities between the learning materials and the description of the course being are computed as basis for developing recommended learning materials to students. The recommendation system modeling for personalized learning is applied as a solution to achieve learning goals and minimize dropouts due to the inability of students to understand the learning process in online learning systems. The results given in the research are modeling of an adaptive learning personalization recommendation system to provide recommendations for students who do not reach the minimum competency standards.