  The past decade has seen a growth in the development and deployment of
educational technologies for assisting college-going students in choosing
majors, selecting courses and acquiring feedback based on past academic
performance. Grade prediction methods seek to estimate a grade that a student
may achieve in a course that she may take in the future (e.g., next term).
Accurate and timely prediction of students' academic grades is important for
developing effective degree planners and early warning systems, and ultimately
improving educational outcomes. Existing grade pre- diction methods mostly
focus on modeling the knowledge components associated with each course and
student, and often overlook other factors such as the difficulty of each
knowledge component, course instructors, student interest, capabilities and
effort. In this paper, we propose additive latent effect models that
incorporate these factors to predict the student next-term grades.
Specifically, the proposed models take into account four factors: (i) student's
academic level, (ii) course instructors, (iii) student global latent factor,
and (iv) latent knowledge factors. We compared the new models with several
state-of-the-art methods on students of various characteristics (e.g., whether
a student transferred in or not). The experimental results demonstrate that the
proposed methods significantly outperform the baselines on grade prediction
problem. Moreover, we perform a thorough analysis on the importance of
different factors and how these factors can practically assist students in
course selection, and finally improve their academic performance.
