CrsRecs: A personalized course recommendation system for college students
Yiu‐Kai Ng, Jane Linn · 2017
Every college student has different needs when it comes to learning. It can be difficult to decide which course is best to take on the road to graduation, and which professor will best suit the student's learning style. CrsRecs, our proposed course/professor recommendation system, makes that process much easier. Using topic analysis, tag analysis, sentiment analysis, predicted course/professor ratings, and survey data revealing student priorities with respect to classes (i.e., easy A, quality of the class, etc.), CrsRecs ranks potential courses in order of perceived preference for the student based on a hybrid technique combining the analysis results of a course. Empirical studies conducted to evaluate the performance of CrsRecs have revealed that CrsRecs not only suggests relevant courses to users by considering all the features of a course, but also outperforms existing state-of-the-art course recommendation approaches.