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The past decade has seen a growth in the development and deployment ofeducational technologies for assisting college-going students in choosingmajors, selecting courses and acquiring feedback based on past academicperformance. Grade prediction methods seek to estimate a grade that a studentmay 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 fordeveloping effective degree planners and early warning systems, and ultimatelyimproving educational outcomes. Existing grade pre- diction methods mostlyfocus on modeling the knowledge components associated with each course andstudent, and often overlook other factors such as the difficulty of eachknowledge component, course instructors, student interest, capabilities andeffort. In this paper, we propose additive latent effect models thatincorporate these factors to predict the student next-term grades.Specifically, the proposed models take into account four factors: (i) student'sacademic level, (ii) course instructors, (iii) student global latent factor,and (iv) latent knowledge factors. We compared the new models with severalstate-of-the-art methods on students of various characteristics (e.g., whethera student transferred in or not). The experimental results demonstrate that theproposed methods significantly outperform the baselines on grade predictionproblem. Moreover, we perform a thorough analysis on the importance ofdifferent factors and how these factors can practically assist students incourse selection, and finally improve their academic performance.

ALE: Additive Latent Effect Models for Grade Prediction