Context-aware Non-linear and Neural Attentive Knowledge-based Models for Grade Prediction
This addresses the problem of personalized course planning for students and advisers, but it is incremental as it builds on existing knowledge-based models.
The paper tackles grade prediction for future courses by proposing context-aware non-linear and neural attentive models to better estimate a student's knowledge state and model interactions with concurrent courses, achieving effectiveness on a dataset of over 1.5M grades.
Grade prediction for future courses not yet taken by students is important as it can help them and their advisers during the process of course selection as well as for designing personalized degree plans and modifying them based on their performance. One of the successful approaches for accurately predicting a student's grades in future courses is Cumulative Knowledge-based Regression Models (CKRM). CKRM learns shallow linear models that predict a student's grades as the similarity between his/her knowledge state and the target course. However, prior courses taken by a student can have \black{different contributions when estimating a student's knowledge state and towards each target course, which} cannot be captured by linear models. Moreover, CKRM and other grade prediction methods ignore the effect of concurrently-taken courses on a student's performance in a target course. In this paper, we propose context-aware non-linear and neural attentive models that can potentially better estimate a student's knowledge state from his/her prior course information, as well as model the interactions between a target course and concurrent courses. Compared to the competing methods, our experiments on a large real-world dataset consisting of more than $1.5$M grades show the effectiveness of the proposed models in accurately predicting students' grades. Moreover, the attention weights learned by the neural attentive model can be helpful in better designing their degree plans.