Scholars walk: A Markov chain framework for course recommendation

Course selection is a crucial and challenging problem that students have to face while navigating through an undergraduate degree program. The decisions they make shape their future in ways that they cannot conceive in advance. Available departmental sample degree plans are not personalized for each student, and personal discussion time with an academic advisor is usually limited. Data-driven methods supporting decision making have gained importance to empower student choices and scale advice to large cohorts. We propose Scholars Walk, a random-walk-based approach that captures the sequential relationships between the different courses. Based on the "wisdom of the crowd" and the students' prior courses, we recommend a short list of courses for next semester. Our experimental evaluation illustrates that Scholars Walk outperforms other collaborative filtering and popularity-based approaches. At the same time, our framework is very efficient, easily interpretable, while also being able to take into consideration important aspects of the educational domain.