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

• Main Authors: Thanyaluck Ingkavara, Patcharin Panjaburee, Niwat Srisawasdi, Suthiporn Sajjapanroj
• Other Authors: Khon Kaen University
• Format: Article
• Published: 2022
• Subjects: Computer Science; Social Sciences
• Online Access: https://repository.li.mahidol.ac.th/handle/123456789/73751
• Institution: Mahidol University

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.