3554614
doi
10.5281/zenodo.3554614
oai:zenodo.org:3554614
Harley, Jason M.
McGill University
Trevors, Gregory J.
McGill University
Azevedo, Roger
McGill University
Clustering and Profiling Students According to their Interactions with an Intelligent Tutoring System Fostering Self-Regulated Learning
Bouchet, Francois
McGill University
url:https://jedm.educationaldatamining.org/index.php/JEDM/article/view/32
info:eu-repo/semantics/openAccess
Creative Commons Attribution Non Commercial No Derivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
profiling
cluster
expectation-maximization
intelligent tutoring system
agent-based system
self-regulated learning
metacognition
adaptivity
In this paper, we present the results obtained using a clustering algorithm (Expectation-Maximization) on data collected from 106 college students learning about the circulatory system with MetaTutor, an agent-based Intelligent Tutoring System (ITS) designed to foster self-regulated learning (SRL). The three extracted clusters were validated and analyzed using multivariate statistics (MANOVAs) in order to characterize three distinct profiles of students, displaying statistically significant differences over all 12 variables used for the clusters formation (including performance, use of note-taking and number of sub-goals attempted). We show through additional analyses that variations also exist between the clusters regarding prompts they received by the system to perform SRL processes. We conclude with a discussion of implications for designing a more adaptive ITS based on an identification of learners' profiles
The file is in PDF format. If your computer does not recognize it, simply download the file and then open it with your browser.
Zenodo
2013-05-01
info:eu-repo/semantics/article
3554613
1.0.0
1579533275.846116
849882
md5:747a343ea30d4b459d27de9ff17b5b59
https://zenodo.org/records/3554614/files/742736945
public
https://jedm.educationaldatamining.org/index.php/JEDM/article/view/32
Is cited by
url
10.5281/zenodo.3554613
isVersionOf
doi
Journal of Educational Data Mining
5
1
104-146
2013-05-01