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Impact of personality traits on learners’ navigational behavior patterns in an online course: a lag sequential analysis approach

Personality is considered as the internal factor that defines a person’s behavior. Therefore, providing adaptive features and personalized support in online learning by considering learners’ personalities can improve their learning experiences and outcomes. In this context, several research studies...

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Autores principales: Tlili, Ahmed, Sun, Tianyue, Denden, Mouna, Kinshuk, Graf, Sabine, Fei, Cheng, Wang, Huanhuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10245556/
https://www.ncbi.nlm.nih.gov/pubmed/37292512
http://dx.doi.org/10.3389/fpsyg.2023.1071985
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author Tlili, Ahmed
Sun, Tianyue
Denden, Mouna
Kinshuk,
Graf, Sabine
Fei, Cheng
Wang, Huanhuan
author_facet Tlili, Ahmed
Sun, Tianyue
Denden, Mouna
Kinshuk,
Graf, Sabine
Fei, Cheng
Wang, Huanhuan
author_sort Tlili, Ahmed
collection PubMed
description Personality is considered as the internal factor that defines a person’s behavior. Therefore, providing adaptive features and personalized support in online learning by considering learners’ personalities can improve their learning experiences and outcomes. In this context, several research studies have investigated the impact of personality differences in online learning. However, little is known about how personality differences affect learners’ behavior while learning. To fill this gap, this study applies a lag sequential analysis (LSA) approach to understand learners’ navigational behavior patterns in an online three-months course of 65 learners based on their personalities. In this context, the five factor model (FFM) model was used to identify learners’ personalities. The findings revealed that learners with different personalities use different strategies to learn and navigate within the course. For instance, learners high in extraversion tend to be extrinsically motivated. They therefore significantly navigated between viewing the course module and their personal achievements. The findings of this study can contribute to the adaptive learning field by providing insights about which personalization features can help learners with different personalities. The findings can also contribute to the field of automatic modeling of personality by providing information about differences in navigational behavior based on learners’ personalities.
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spelling pubmed-102455562023-06-08 Impact of personality traits on learners’ navigational behavior patterns in an online course: a lag sequential analysis approach Tlili, Ahmed Sun, Tianyue Denden, Mouna Kinshuk, Graf, Sabine Fei, Cheng Wang, Huanhuan Front Psychol Psychology Personality is considered as the internal factor that defines a person’s behavior. Therefore, providing adaptive features and personalized support in online learning by considering learners’ personalities can improve their learning experiences and outcomes. In this context, several research studies have investigated the impact of personality differences in online learning. However, little is known about how personality differences affect learners’ behavior while learning. To fill this gap, this study applies a lag sequential analysis (LSA) approach to understand learners’ navigational behavior patterns in an online three-months course of 65 learners based on their personalities. In this context, the five factor model (FFM) model was used to identify learners’ personalities. The findings revealed that learners with different personalities use different strategies to learn and navigate within the course. For instance, learners high in extraversion tend to be extrinsically motivated. They therefore significantly navigated between viewing the course module and their personal achievements. The findings of this study can contribute to the adaptive learning field by providing insights about which personalization features can help learners with different personalities. The findings can also contribute to the field of automatic modeling of personality by providing information about differences in navigational behavior based on learners’ personalities. Frontiers Media S.A. 2023-05-23 /pmc/articles/PMC10245556/ /pubmed/37292512 http://dx.doi.org/10.3389/fpsyg.2023.1071985 Text en Copyright © 2023 Tlili, Sun, Denden, Kinshuk, Graf, Fei and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Tlili, Ahmed
Sun, Tianyue
Denden, Mouna
Kinshuk,
Graf, Sabine
Fei, Cheng
Wang, Huanhuan
Impact of personality traits on learners’ navigational behavior patterns in an online course: a lag sequential analysis approach
title Impact of personality traits on learners’ navigational behavior patterns in an online course: a lag sequential analysis approach
title_full Impact of personality traits on learners’ navigational behavior patterns in an online course: a lag sequential analysis approach
title_fullStr Impact of personality traits on learners’ navigational behavior patterns in an online course: a lag sequential analysis approach
title_full_unstemmed Impact of personality traits on learners’ navigational behavior patterns in an online course: a lag sequential analysis approach
title_short Impact of personality traits on learners’ navigational behavior patterns in an online course: a lag sequential analysis approach
title_sort impact of personality traits on learners’ navigational behavior patterns in an online course: a lag sequential analysis approach
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10245556/
https://www.ncbi.nlm.nih.gov/pubmed/37292512
http://dx.doi.org/10.3389/fpsyg.2023.1071985
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