Personalized Multi-Regression Models for Predicting Students' Performance in Course Activities
| Asmaa Elbadrawy, R. Scott Studham, and George Karypis |
| UMN CS 14-011, 2014 |
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| Abstract Methods that accurately predict the grade of a student at a given activity and/or course can identify students that are at risk in failing a course and allow their educational institution to take corrective actions. Though a number of approaches have been developed for building such performance prediction models, they either estimate a single model for all students based on their past course performance and interactions with learning management systems (LMS), or estimate student-specific models that do not take into account LMS interactions; thus, failing to exploit fine-grain information related to a student’s engagement and effort in a course. In this work we present a class of linear multi-regression models that are designed to produce models that are personalized to each student and also take into account a large number of features that relate to a student’s past performance, course characteristics, and student’s engagement and effort. These models estimate a small number of regression models that are shared across the different students along with student- specific linear combination functions to facilitate personalization. Our experimental evaluation on a large set of students, courses, and activities shows that these models are capable of improving the performance prediction accuracy by over 20%. In addition, we show that by analyzing the estimated models along with the student-specific combination functions we can gain insights on the effectiveness of the educational material that is made available at the courses of different departments. |
| Comments Also appears in the 5th International Learning Analytics & Knowledge conference (LAK15), 2015. |
| Research topics: Data mining | Learning analytics |