Modeling and Measuring Self-Regulated Learning in Teachable Agent Environments
Our learning-by-teaching environment has students take on the role and responsibilities of a teacher to a virtual student named Betty. The environment is designed to help students learn and understand science topics for themselves as they teach and monitor their agent. This process is supported by a...
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Italian e-Learning Association
2011-05-01
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Series: | Je-LKS : Journal of e-Learning and Knowledge Society |
Online Access: | https://www.je-lks.org/ojs/index.php/Je-LKS_EN/article/view/518 |
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doaj-354fb6e34759423a8aaedabd75ec50fb2020-11-25T01:38:31ZengItalian e-Learning AssociationJe-LKS : Journal of e-Learning and Knowledge Society1826-62231971-88292011-05-017210.20368/1971-8829/518Modeling and Measuring Self-Regulated Learning in Teachable Agent EnvironmentsJohn Kinnebrew0Gautam Biswas1Vanderbilt UniversityVanderbilt UniversityOur learning-by-teaching environment has students take on the role and responsibilities of a teacher to a virtual student named Betty. The environment is designed to help students learn and understand science topics for themselves as they teach and monitor their agent. This process is supported by adaptive scaffolding and feedback through interactions with the teachable agent and a mentor agent. This paper discusses the results of a comparative study conducted in an 8th-grade science classroom, where students received two kinds of metacognitive and learning strategy feedback. We analyze student performance and learning gains as a result of the intervention. To gain further insight into student learning behaviors exhibited during the intervention, we employ a data mining methodology incorporating hidden Markov modeling and sequence mining techniques. The results illustrate both the effectiveness of the experimental agent feedback in encouraging metacognitive learning strategies and the utility of the data mining methodology.https://www.je-lks.org/ojs/index.php/Je-LKS_EN/article/view/518 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
John Kinnebrew Gautam Biswas |
spellingShingle |
John Kinnebrew Gautam Biswas Modeling and Measuring Self-Regulated Learning in Teachable Agent Environments Je-LKS : Journal of e-Learning and Knowledge Society |
author_facet |
John Kinnebrew Gautam Biswas |
author_sort |
John Kinnebrew |
title |
Modeling and Measuring Self-Regulated Learning in Teachable Agent Environments |
title_short |
Modeling and Measuring Self-Regulated Learning in Teachable Agent Environments |
title_full |
Modeling and Measuring Self-Regulated Learning in Teachable Agent Environments |
title_fullStr |
Modeling and Measuring Self-Regulated Learning in Teachable Agent Environments |
title_full_unstemmed |
Modeling and Measuring Self-Regulated Learning in Teachable Agent Environments |
title_sort |
modeling and measuring self-regulated learning in teachable agent environments |
publisher |
Italian e-Learning Association |
series |
Je-LKS : Journal of e-Learning and Knowledge Society |
issn |
1826-6223 1971-8829 |
publishDate |
2011-05-01 |
description |
Our learning-by-teaching environment has students take on the role and responsibilities of a teacher to a virtual student named Betty. The environment is designed to help students learn and understand science topics for themselves as they teach and monitor their agent. This process is supported by adaptive scaffolding and feedback through interactions with the teachable agent and a mentor agent. This paper discusses the results of a comparative study conducted in an 8th-grade science classroom, where students received two kinds of metacognitive and learning strategy feedback. We analyze student performance and learning gains as a result of the intervention. To gain further insight into student learning behaviors exhibited during the intervention, we employ a data mining methodology incorporating hidden Markov modeling and sequence mining techniques. The results illustrate both the effectiveness of the experimental agent feedback in encouraging metacognitive learning strategies and the utility of the data mining methodology. |
url |
https://www.je-lks.org/ojs/index.php/Je-LKS_EN/article/view/518 |
work_keys_str_mv |
AT johnkinnebrew modelingandmeasuringselfregulatedlearninginteachableagentenvironments AT gautambiswas modelingandmeasuringselfregulatedlearninginteachableagentenvironments |
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1725053321073393664 |