Assessment of learning outcomes among students at institutions of higher education is a fascinating issue with a wide range of possible applications for all parties including students, administrators, potential employers, etc. Even while education is becoming more widely available and popular, the high drop-out rates remain a challenging issue. Choosing the best courses for your area of specialization can be difficult and time-consuming. The majority of the current course selection algorithms do not consider courses that match student talents, the user`s future professional goals, or the user`s ideal job based on such objectives. The goal is to create a powerful learning analytics system that can effectively recommend courses to students based on their preferences and skills. The collaborative filtering recommender (CF) approach, which combines KNN and decision tree approaches, was used to match courses, abilities, requirements, and interests with recommended lists. The effect of user skills on the recommendation platform was investigated by altering a number of suggestion quality features. A collaborative filtering recommender system was developed by fusing KNN and DT to suggest specialized courses for college students. This improved the standard of the recommendation system. A recommender model was constructed with cosine similarity matrix of student course descriptions in order to include new descriptions that are being suggested to the overall descriptions. Term frequency inverse document frequency was used to convert the entire course description into a vectored representation of words.

Collaborator filtering, decision tree, KNN, machine learning, recommender system

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