Context-Aware Recommendation-Based Learning Analytics Using Tensor and Coupled Matrix Factorization

Faisal M. Almutairi, Nicholas D. Sidiropoulos, George Karypis

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


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.

Original languageEnglish (US)
Article number7931546
Pages (from-to)729-741
Number of pages13
JournalIEEE Journal on Selected Topics in Signal Processing
Issue number5
StatePublished - Aug 2017

Bibliographical note

Funding Information:
Manuscript received October 14, 2016; revised February 26, 2017 and April 24, 2017; accepted April 28, 2017. Date of publication May 18, 2017; date of current version July 18, 2017. This work was supported in part by NSF IIS-1447788. The guest editor coordinating the review of this paper and approving it for publication was Dr. Richard G. Baraniuk. (Corresponding author: Nicholas D. Sidiropoulos.) F. M. Almutairi and N. D. Sidiropoulos are with the Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455 USA (e-mail:;

Publisher Copyright:
© 2017 IEEE.


  • Alternating optimization
  • candecomp/parafac (CP) decomposition
  • collaborative filtering
  • coupled matrix factorization
  • matrix/tensor rank
  • predicting student performance
  • singular value decomposition (SVD)
  • tensor factorization


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