Abstract
Student retention and timely graduation are enduring challenges in higher education. With the rapidly expanding collection and availability of learning data and related analytics, student performance can be accurately monitored, and possibly predicted ahead of time, thus, enabling early warning and degree planning “expert systems” to provide disciplined decision support to counselors, advisors, and educators. Previous work in educational data mining has explored matrix factorization techniques for grade prediction, albeit without taking contextual information into account. Temporal information should be informative as it distinguishes between the different class offerings and indirectly captures student experience as well. To exploit temporal and/or other kinds of context, we develop three approaches under the framework of collaborative filtering (CF). Two of the proposed approaches build upon coupled matrix factorization with a shared latent matrix factor. The third utilizes tensor factorization to model grades and their context, without introducing a new mode per context dimension as is common in the CF literature. The latent factors obtained can be used to predict grades and context, if desired. We evaluate these approaches on grade data obtained from the University of Minnesota. Experimental results show that fairly good prediction is possible even with simple approaches, but very accurate prediction is hard. The more advanced approaches can increase prediction accuracy, but only up to a point for the particular dataset considered.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: IEEE Journal of Selected Topics in Signal Processing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.