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Collaborative multi-regression models for predicting students’ performance in course activities.


Methods that accurately predict the grade of a student at a given activity 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 prediction models have been developed, 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. In this work we present a class of collaborative multi-regression models that are personalized to each student and also take into account features related to student's past performance, engagement and course characteristics. These models use all historical information to estimate a small number of regression models shared by all students along with student-specific combination weights. This allows for information sharing and also generating personalized predictions. 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 and 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.

Presented by...

George Karypis, Asmaa Elbadrawy
Professor, PhD Candidate
University of Minnesota
I'm George Karypis, a Professor at the Department of Computer Science & Engineering at the University of Minnesota in the Twin Cities of Minneapolis and Saint Paul and a member of the Digital Technology Center (DTC) at the University of Minnesota.
My research interests are concentrated in the areas of bioinformatics, cheminformatics, data mining, and high-performance computing, and from time-to-time, I look at various problems in the areas of information retrieval, collaborative filtering, and electronic design automation for VLSI CAD.

Within these areas, my research focuses in developing novel algorithms for solving important existing and/or emerging problems, and on developing practical software tools implementing some of these algorithms. The results from this research have been presented in various conferences and published in leading peer reviewed journals and highly selective conference proceedings.


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Last modified Monday, November 16, 2015, 6:09 PM
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