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Students attending a university can benefit from carefully planning out which courses they will take during the course of their academic career. Developing such a plan of study can be difficult because students need to consider factors such as how each course applies to their graduation requirements, whether course schedules have conflicts, whether the courses are interesting to the student, and whether registering for particular courses can fulfill prerequisites for future courses. In this work, we present a Course Recommender Ontology, which aims to represent relevant information about courses, schedules, graduation requirements, relationships among courses, and student interests to enable a recommender system. Our ontology can be used to enable rule-based recommendations that adhere to course requirements (such as prerequisite requirements) and provide explanations to students about why certain courses are recommended to them.
The Course Recommender Ontology is intended to be used together with external resources to serve the use case of recommending courses to students. The system architecture diagram below depicts how this ontology would fit in to such an application. Course information would be retrieved or stored from other sources, like a course catalog. A recommender system would ingest the information about available courses, then use the Course Recommender Ontology to help determine what courses to recommend to the user.
Let us consider the basic flow of a student entering the course recommender system and retrieving some recommended courses to sign up for. A recommendation system might function following the an activity diagram like the one below. A student would enter the system by logging in, then request some course recommendations from the system. The recommender system would use the Course Recommender Ontology to retrieve information like course prerequisites, corequisites, and topics. The system would then query some database containing information about what course sections and schedules are being offered in the next semester. These pieces of information would be leveraged by the recommendation system, together with the student profile, to decide which courses to provide as recommendations.
Sola S. Shirai - shiras2 at rpi dot edu
Owen Xie - xieo at rpi dot edu
Kelly Fellenzer - fellek at rpi dot edu
Jacob Shomstein - shomsj at rpi dot edu
A huge thank you to our professors Deborah McGuinness and Elisa Kendall for providing help and insight during the development of our Course Recommender Ontology. Additionally, we appreciate all of Sabbir Rashid’s and Shruthi Chari’s feedback we recieved on our artifacts and presentations.