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  4. Evolutionary Clustering of Apprentices' Self- Regulated Learning Behavior in Learning Journals
 
research article

Evolutionary Clustering of Apprentices' Self- Regulated Learning Behavior in Learning Journals

Mejia, Paola  
•
Marras, Mirko
•
Giang, Christian  
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August 2, 2022
IEEE Transactions on Learning Technologies

Learning journals are increasingly used in vocational education to foster self-regulated learning and reflective learning practices. However, for many apprentices, documenting working experiences is a difficult task. In this paper, we profile apprentices' learning behavior in an online learning journal. Based on a pedagogical framework, we propose a novel multi-step clustering pipeline that integrates different learning dimensions into a combined profile. Specifically, the profiles are described in terms of effort, consistency, regularity, help-seeking behavior, and quality of the written entries. Our results on two populations of chef apprentices (183 apprentices) interacting with an online learning journal (over 121K entries) show that our pipeline captures changes in learning patterns over time and yields interpretable profiles that can be related to academic performance. The obtained profiles can be used as a basis for personalized interventions, with the ultimate goal of improving the apprentices' learning experience.

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Type
research article
DOI
10.1109/TLT.2022.3195881
Author(s)
Mejia, Paola  
•
Marras, Mirko
•
Giang, Christian  
•
Cattaneo, Alberto
•
Käser, Tanja  
Date Issued

2022-08-02

Published in
IEEE Transactions on Learning Technologies
Start page

1

End page

14

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
ML4ED  
AVP-E-LEARN  
Available on Infoscience
September 23, 2022
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/190880
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