The use of a personalized learning approach to implementing self-regulated online learning

Nowadays, students are encouraged to learn via online learning systems to promote students' autonomy. Scholars have found that students' self-regulated actions impact their academic success in an online learning environment. However, because traditional online learning systems cannot perso...

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Main Authors: Thanyaluck Ingkavara, Patcharin Panjaburee, Niwat Srisawasdi, Suthiporn Sajjapanroj
Format: Article
Language:English
Published: Elsevier 2022-01-01
Series:Computers and Education: Artificial Intelligence
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2666920X22000418
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author Thanyaluck Ingkavara
Patcharin Panjaburee
Niwat Srisawasdi
Suthiporn Sajjapanroj
author_facet Thanyaluck Ingkavara
Patcharin Panjaburee
Niwat Srisawasdi
Suthiporn Sajjapanroj
author_sort Thanyaluck Ingkavara
collection DOAJ
description Nowadays, students are encouraged to learn via online learning systems to promote students' autonomy. Scholars have found that students' self-regulated actions impact their academic success in an online learning environment. However, because traditional online learning systems cannot personalize feedback to the student's personality, most students have less chance to obtain helpful suggestions for enhancing their knowledge linked to their learning problems. This paper incorporated self-regulated online learning in the Physics classroom and used a personalized learning approach to help students receive proper learning paths and material corresponding to their learning preferences. This study conducted a quasi-experimental design using a quantitative approach to evaluate the effectiveness of the proposed learning environment in secondary schools. The experimental group of students participated in self-regulated online learning with a personalized learning approach, while the control group participated in conventional self-regulated online learning. The experimental results showed that the experimental group's post-test and the learning-gain score of the experimental group were significantly higher than those of the control group. Moreover, the results also suggested that the student's perceptions about the usefulness of learning suggestions, ease of use, goal setting, learning environmental structuring, task strategies, time management, self-evaluation, impact on learning, and attitude toward the learning environment are important predictors of behavioral intention to learn with the self-regulated online learning that integrated with the personalized learning approach.
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spelling doaj.art-53476181396d426c949f439e6e51e4e22022-12-22T04:21:52ZengElsevierComputers and Education: Artificial Intelligence2666-920X2022-01-013100086The use of a personalized learning approach to implementing self-regulated online learningThanyaluck Ingkavara0Patcharin Panjaburee1Niwat Srisawasdi2Suthiporn Sajjapanroj3Institute for Innovative Learning, Mahidol University, Nakhon Pathom, ThailandInstitute for Innovative Learning, Mahidol University, Nakhon Pathom, Thailand; Faculty of Education, Khon Kaen University, Khon Kaen, Thailand; Corresponding author. Institute for Innovative Learning, Mahidol University, Nakhon Pathom, Thailand.Faculty of Education, Khon Kaen University, Khon Kaen, ThailandInstitute for Innovative Learning, Mahidol University, Nakhon Pathom, ThailandNowadays, students are encouraged to learn via online learning systems to promote students' autonomy. Scholars have found that students' self-regulated actions impact their academic success in an online learning environment. However, because traditional online learning systems cannot personalize feedback to the student's personality, most students have less chance to obtain helpful suggestions for enhancing their knowledge linked to their learning problems. This paper incorporated self-regulated online learning in the Physics classroom and used a personalized learning approach to help students receive proper learning paths and material corresponding to their learning preferences. This study conducted a quasi-experimental design using a quantitative approach to evaluate the effectiveness of the proposed learning environment in secondary schools. The experimental group of students participated in self-regulated online learning with a personalized learning approach, while the control group participated in conventional self-regulated online learning. The experimental results showed that the experimental group's post-test and the learning-gain score of the experimental group were significantly higher than those of the control group. Moreover, the results also suggested that the student's perceptions about the usefulness of learning suggestions, ease of use, goal setting, learning environmental structuring, task strategies, time management, self-evaluation, impact on learning, and attitude toward the learning environment are important predictors of behavioral intention to learn with the self-regulated online learning that integrated with the personalized learning approach.http://www.sciencedirect.com/science/article/pii/S2666920X22000418Intelligent tutoring systemPersonalizationAdaptive learningE-learningTAMArtificial intelligence
spellingShingle Thanyaluck Ingkavara
Patcharin Panjaburee
Niwat Srisawasdi
Suthiporn Sajjapanroj
The use of a personalized learning approach to implementing self-regulated online learning
Computers and Education: Artificial Intelligence
Intelligent tutoring system
Personalization
Adaptive learning
E-learning
TAM
Artificial intelligence
title The use of a personalized learning approach to implementing self-regulated online learning
title_full The use of a personalized learning approach to implementing self-regulated online learning
title_fullStr The use of a personalized learning approach to implementing self-regulated online learning
title_full_unstemmed The use of a personalized learning approach to implementing self-regulated online learning
title_short The use of a personalized learning approach to implementing self-regulated online learning
title_sort use of a personalized learning approach to implementing self regulated online learning
topic Intelligent tutoring system
Personalization
Adaptive learning
E-learning
TAM
Artificial intelligence
url http://www.sciencedirect.com/science/article/pii/S2666920X22000418
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