Identifying Beneficial Learning Behaviors from Large-Scale Interaction Data

Understanding the effect of learning behavior is fundamental to improving learning outcomes. In this paper, we perform a behavioral analysis based on data from a large high-stakes exam preparation platform. By measuring the importance of a set of candidate learning behaviors in predicting final exam...

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Autores principales: Cristus, Miruna, Täckström, Oscar, Tan, Lingyi, Pacifici, Valentino
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334710/
http://dx.doi.org/10.1007/978-3-030-52240-7_67
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author Cristus, Miruna
Täckström, Oscar
Tan, Lingyi
Pacifici, Valentino
author_facet Cristus, Miruna
Täckström, Oscar
Tan, Lingyi
Pacifici, Valentino
author_sort Cristus, Miruna
collection PubMed
description Understanding the effect of learning behavior is fundamental to improving learning outcomes. In this paper, we perform a behavioral analysis based on data from a large high-stakes exam preparation platform. By measuring the importance of a set of candidate learning behaviors in predicting final exam outcomes, we identify a suite of beneficial behaviors. In particular, we find that breadth (wide coverage of content per week) and intensity together with consistency (frequent and equal-length practice for a limited period) are most predictive of final exam success rate, among eleven studied behaviors.
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spelling pubmed-73347102020-07-06 Identifying Beneficial Learning Behaviors from Large-Scale Interaction Data Cristus, Miruna Täckström, Oscar Tan, Lingyi Pacifici, Valentino Artificial Intelligence in Education Article Understanding the effect of learning behavior is fundamental to improving learning outcomes. In this paper, we perform a behavioral analysis based on data from a large high-stakes exam preparation platform. By measuring the importance of a set of candidate learning behaviors in predicting final exam outcomes, we identify a suite of beneficial behaviors. In particular, we find that breadth (wide coverage of content per week) and intensity together with consistency (frequent and equal-length practice for a limited period) are most predictive of final exam success rate, among eleven studied behaviors. 2020-06-10 /pmc/articles/PMC7334710/ http://dx.doi.org/10.1007/978-3-030-52240-7_67 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Cristus, Miruna
Täckström, Oscar
Tan, Lingyi
Pacifici, Valentino
Identifying Beneficial Learning Behaviors from Large-Scale Interaction Data
title Identifying Beneficial Learning Behaviors from Large-Scale Interaction Data
title_full Identifying Beneficial Learning Behaviors from Large-Scale Interaction Data
title_fullStr Identifying Beneficial Learning Behaviors from Large-Scale Interaction Data
title_full_unstemmed Identifying Beneficial Learning Behaviors from Large-Scale Interaction Data
title_short Identifying Beneficial Learning Behaviors from Large-Scale Interaction Data
title_sort identifying beneficial learning behaviors from large-scale interaction data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334710/
http://dx.doi.org/10.1007/978-3-030-52240-7_67
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