TY - GEN
T1 - Tag-aware ordinal sparse factor analysis for learning and content analytics
AU - Lan, Andrew S.
AU - Studer, Christoph
AU - Waters, Andrew E.
AU - Baraniuk, Richard G.
N1 - Funding Information:
Thanks to Daniel Calderon for administering the Algebra test on Amanzon’s Mechanical Turk. This work was supported by the National Science Foundation under Cyber-learning grant IIS-1124535, the Air Force Office of Scientific Research under grant FA9550-09-1-0432, the Google Faculty Research Award program, and the Swiss National Science Foundation under grant PA00P2-134155.
Publisher Copyright:
© 2013 International Educational Data Mining Society. All rights reserved.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - Machine learning offers novel ways and means to design personalized learning systems wherein each student’s educational experience is customized in real time depending on their background, learning goals, and performance to date. SPARse Factor Analysis (SPARFA) is a novel framework for machine learning-based learning analytics, which estimates a learner’s knowledge of the concepts underlying a domain, and content analytics, which estimates the relationships among a collection of questions and those concepts. SPARFA jointly learns the associations among the questions and the concepts, learner concept knowledge profiles, and the underlying question difficulties, solely based on the correct/incorrect graded responses of a population of learners to a collection of questions. In this paper, we extend the SPARFA framework significantly to enable: (i) the analysis of graded responses on an ordinal scale (partial credit) rather than a binary scale (correct/incorrect); (ii) the exploitation of tags/labels for questions that partially describe the question–concept associations. The resulting Ordinal SPARFA-Tag framework greatly enhances the interpretability of the estimated concepts. We demonstrate using real educational data that Ordinal SPARFA-Tag outperforms both SPARFA and existing collaborative filtering techniques in predicting missing learner responses.
AB - Machine learning offers novel ways and means to design personalized learning systems wherein each student’s educational experience is customized in real time depending on their background, learning goals, and performance to date. SPARse Factor Analysis (SPARFA) is a novel framework for machine learning-based learning analytics, which estimates a learner’s knowledge of the concepts underlying a domain, and content analytics, which estimates the relationships among a collection of questions and those concepts. SPARFA jointly learns the associations among the questions and the concepts, learner concept knowledge profiles, and the underlying question difficulties, solely based on the correct/incorrect graded responses of a population of learners to a collection of questions. In this paper, we extend the SPARFA framework significantly to enable: (i) the analysis of graded responses on an ordinal scale (partial credit) rather than a binary scale (correct/incorrect); (ii) the exploitation of tags/labels for questions that partially describe the question–concept associations. The resulting Ordinal SPARFA-Tag framework greatly enhances the interpretability of the estimated concepts. We demonstrate using real educational data that Ordinal SPARFA-Tag outperforms both SPARFA and existing collaborative filtering techniques in predicting missing learner responses.
KW - Block coordinate descent
KW - Factor analysis
KW - Matrix factorization
KW - Ordinal regression
KW - Personalized learning
UR - http://www.scopus.com/inward/record.url?scp=85084016806&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85084016806&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85084016806
T3 - Proceedings of the 6th International Conference on Educational Data Mining, EDM 2013
BT - Proceedings of the 6th International Conference on Educational Data Mining, EDM 2013
A2 - D'Mello, Sidney K.
A2 - Calvo, Rafael A.
A2 - Olney, Andrew
PB - International Educational Data Mining Society
T2 - 6th International Conference on Educational Data Mining, EDM 2013
Y2 - 6 July 2013 through 9 July 2013
ER -