A Novel Search Ranking Method for MOOCs Using Unstructured Course Information. (23rd September 2020) - Yao, Weiqiang; Sun, Haiquan; Hu, Xiaoxuan
Abstract : Massive open online courses (MOOCs) are a technical trend in the field of education. As the number of available MOOCs continues to grow dramatically, the difficulty for learners to find courses that satisfy their personalized learning goals has also increased. Unstructured texts, such as course descriptions and course skills, contain rich course information and are useful for MOOC platforms in constructing personalized services. This paper proposes a novel search ranking method for MOOCs that integrates unstructured course information. We propose a latent Dirichlet allocation-based model to cluster courses into groups based on course descriptions. Courses in the same cluster are considered to share similar educational contents. We then propose the CourseRank algorithm based on the information of course skills to recommend and rank courses when students search for or click on a specific course. Our experiments on the dataset from Coursera indicate that our method is able to cluster courses effectively and produce satisfactory ranking results for courses in MOOC platforms.