Technical Papers

To date, several SPARFA papers have been published in the machine learning community. Here we link to several of these papers for those interested in learning more about the technical aspects of the SPARFA project. Check back frequently, as we will be frequently updating this page with new SPARFA material.

The Original SPARFA Paper:

A. S. Lan, A. E. Waters, C. Studer, and R. G. Baraniuk, "Sparse Factor Analysis for Learning and Content Analytics", J. Machine Learning Research (JMLR), 2014.

Sparse Probit Factor Analysis for Learning Analytics:

A. E. Waters, A. S. Lan, C. Studer, and R. G. Baraniuk, "Sparse Probit Factor Analysis for Learning Analytics". Intl. Conf. on Acoustics, Speech, and Signal Proc (ICASSP), 2013.

Tag-aware SPARFA:

A. S. Lan, C. Studer, A. E. Waters, and R. G. Baraniuk, "Tag Aware Ordinal Sparse Factor Analysis for Learning and Content Analytics", Intl. Conf. on Educational Data Mining (EDM), 2013.

Joint Topic Modeling with SPARFA:

A. S. Lan, C. Studer, A. E. Waters, and R. G. Baraniuk, "Joint Topic Modeling and Factor Analysis of Textual Information and Graded Response Data", Intl. Conf. on Educational Data Mining (EDM), 2013.

Reducing Test Size with SPARFA:

D. Vats, C. Studer, A. S. Lan, L. Carin, and R. G. Baraniuk, "TeSR: Test-size Reduction for Concept Estimation", Intl. Conf. on Educational Data Mining (EDM), 2013.

D. Vats, C. Studer, A. S. Lan, L. Carin, and R. G. Baraniuk, "TeSR: Test-size Reduction via Sparse Factor Analysis", Preprint, 2014.

D. Vats, A. S. Lan, C. Studer, and R. G. Baraniuk, "Optimal Ranking of Test Items using the Rasch Model", Allerton Conference on Communication, Control, and Computing, 2016.

Time-varying SPARFA:

A. S. Lan, C. Studer, and R. G. Baraniuk, "Time-varying Learning and Content Analytics via Sparse Factor Analysis", ACM SIGKDD Conf. on Konwledge Discovery and Data Mining (KDD), 2014.

One-Bit Matrix Completion for Educational Datasets:

A. S. Lan, C. Studer, and R. G. Baraniuk, "SPARFA-Lite: Quantized Matrix Completion for Personalized Learning", Intl. Conf. on Educational Data Mining (EDM), 2014.

Mathematical Language Processing for Open Response Mathematical Questions:

A. S. Lan, D. Vats, A. E. Waters, and R. G. Baraniuk, "Mathematical Language Processing: Automatic Grading and Feedback for Open Response Mathematical Questions", ACM Conf. on Learning at Scale (L@S), 2015.

Self-Expressive Clustering:

A. S. Lan, C. Studer, and R. G. Baraniuk, "Self-Expressive Clustering of Binary Data via Group Sparsity", Sig. Proc. with Adaptive Sparse Structured Representations (SPARS), 2015.

Detecting Collaboration in Educational Datasets with SPARFA:

A. E. Waters, C. Studer, and R. G. Baraniuk, "Collaboration-Type Identification in Educational Datasets", J. Educational Data Mining (JEDM), 2014.

Bayesian Nonparametric SPARFA:

A. E. Waters, K. Fronczyk, M. Guindani, M. Vannucci, and R. G. Baraniuk, "A Bayesian Nonparametric Approach for the Analysis of Multiple Categorical Item Responses", J. Statistical Planning and Inference (JSPI), 2014.

Ranked Peer Grading:

A. E. Waters, D. Tinapple, and R. G. Baraniuk, "BayesRank: A Bayesian Approach to Ranked Peer Grading", ACM Conf. on Learning at Scale (L@S), 2015.

SPRITE -- A New Response Model:

A. E. Waters, A. S. Lan, R. Ning, C. Studer, and R. G. Baraniuk, "SPRITE: A Response Model For Multiple Choice Testing", Preprint, 2015.

Dealbreaker -- A New, Non-linear Student Response Model:

A. S. Lan, T. Goldstein, R. G. Baraniuk, and C. Studer, "Dealbreaker: A Nonlinear Latent Variable Model for Educational Data", Intl. Conf. on Machine Learning (ICML), 2016

Personalized Learning Action Selection:

A. S. Lan, and R. G. Baraniuk, "A Contextual Bandits Framework for Personalized Learning Action Selection", Intl. Conf. on Educational Data Mining, 2016

Response Validity Detection in Short-answer Textual Questions:

A. E. Waters, P. Grimaldi, A. S. Lan, and R. G. Baraniuk, "Short-Answer Responses to STEM Questions: Measuring Response Validity and Its Impact on Learning", Preprint, 2016