Evaluation of expert-based Q-Matrices predictive quality in matrix factorization models

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DOIResolve DOI: https://doi.org/10.1007/978-3-319-24258-3_5
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  1. National Research Council Canada. Information and Communication Technologies
FormatText
TypeBook chapter
Proceedings titleDesign for Teaching and Learning in a Networked World : 10th European Conference on Technology Enhanced Learning, EC-TEL 2015, Toledo, Spain, September 15-18, 2015, Proceedings
Series titleLecture Notes In Computer Science; Volume 9307
Conference10th European Conference on Technology Enhanced Learning (EC-TEL 2015), September 15-18, 2015, Toledo, Spain
ISSN0302-9743
ISBN978-3-319-24257-6
978-3-319-24258-3
Pages56–69
SubjectCognitive models; matrix factorization; recommender systems; competency-based learning
Abstract
Matrix factorization techniques are widely used to build collaborative filtering recommender systems. These recommenders aim at discovering latent variables or attributes that are supposed to explain and ultimately predict the interest of users. In cognitive modeling, skills and competencies are considered as key latent attributes to understand and assess student learning. For this purpose, Tatsuoka introduced the concept of Q-matrix to represent the mapping between skills and test items. In this paper we evaluate how predictive expert-created Q-matrices can be when used as a decomposition factor in a matrix factorization recommender. To this end, we developed an evaluation method using cross validation and the weighted least squares algorithm that measures the predictive accuracy of Q-matrices. Results show that expert-made Q-matrices can be reasonably accurate at predicting users success in specific circumstances that are discussed at the end of this paper.
Publication date
PublisherSpringer International Publishing
LanguageEnglish
Peer reviewedYes
NPARC number21276108
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Record identifierfc3eabce-eff5-482b-b930-b88bd5393f44
Record created2015-09-24
Record modified2019-01-31
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