Implementing a recommender system for CS undergraduate students using machine learning

Abstract

E-learning is a learning process that accesses educational curriculum using electronic technologies and can be operated anywhere in the world where there is facility to connect to the internet. Traditional teaching practices of face-to-face mentoring are being replaced by the non-concrete classroom where instructors and students can interact without any barriers. Prerecorded videos, eBooks (Electronic books in pdf form), short written lessons, live sessions, video calling are the main sources of E-learning. Lecturers can grade students' performance through virtual assign- ments and tests. Students may even opt for a degree certi cate after completion of the course that are no less worthy than a degree from any renowned physical institution. In this research paper, the study of E-learning is divided in two parts. Firstly, a survey was conducted on undergraduate students enrolled in Department of Computer Science and Engineering, BRAC University. After performing statisti- cal data mining on students' reviews, more useful information were interpreted that include the preferred online source to study, satisfaction extent on current obtain- able resources, any suggestions that could make their E-learning process e ortless and complaints against current online accessibility of course materials. Whether the students want an E-learning Recommender System was also deduced after this assessment. Secondly, an E-learning Recommender System containing video tuto- rials was built using content-based ltering, item-based collaborative ltering and user-based collaborative ltering. This recommender system was built using tools and libraries of Python programming language which contains massive resources for major CSE courses o ered in BRAC University. The system has also attempted to eliminate the problems attained from the initial portion of the research. Ultimately, we proposed a hybrid ltering approach for our video recommender system consid- ering our experimental results carried on the particular demography which revealed accuracy of the three used algorithms as 88%, 80% and 80%.