• Korean Medical Education Review
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• v.23 no.2
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• pp.118-127
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• 2021

Due to the coronavirus disease 2019 pandemic, the new norm of online learning has been recognized as core to medical institutions for academic continuity, and students are expected to be motivated and engaged in learning while maintaining distance from other peers and educators. To facilitate students' and educators' newly defined roles in online medical education settings, it is crucial to understand how students are actively motivated and engaged in learning. Hence, this study explored medical students' motivational regulation profiles and examined the effects of motivational regulation strategies (MRS) on cognitive learning and learning engagement for online learning. Data were collected after the end of the first semester in 2020 from a sample of 334 medical students enrolled at a public university school of medicine. Latent profile analysis indicated three subgroups with different motivational regulation profiles: the low-profile, medium-profile, and high-profile groups. Regarding different MRS patterns in the high-profile group, mastery self-talk, performance approach self-talk, and the self-consequating strategy appeared to be most applicable for regulating learners' motivation. Analysis of variance showed that the profile groups with higher levels of MRS use were connected to a higher willingness to use cognitive learning strategies and a higher degree of engagement in online learning. The findings of this study emphasize the use of specific sets of MRS to support learning motivation and the need to design effective self-regulated learning environments in online medical education settings.

• Research in Mathematical Education
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• v.19 no.1
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• pp.81-88
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• 2015

This study used a posttest control group design and to find out differences between students' self-regulated learning strategies in traditional and non-traditional classroom. To this end, 131 first year university students within the experimental and control groups took part in the study. While ICT-based approach was used as the main medium of instruction in the experimental group, in the control group the paper-based traditional method was used. A survey adapted from Davaanyam [Davaanyam, T. (2013). The structural relationships among Mongolian students' attitudes toward mathematics, motivational beliefs, self-regulated learning strategies, and mathematics achievement. Ph. D. Dissertation. Jeonju, Jeonbuk, Korea: Chonbuk National Unversity.] was used to gather the data. The results of the study indicated a significant difference between the control and experimental groups in regard with their self-regulated learning. That is to say, the experimental group taught through ICT tools acquired higher levels of self-regulation as compared with the control group instructed through the traditional teaching method.

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.28-34
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• 2024

Since e-learning is conducted based on the learner's autonomy, motivation to continuously participate is crucial for success in e-learning. As the number of adult learners participating in lifelong education increases, it is necessary to study learner participation and the motivating factors. Drawing upon the Expectancy-Value Theory and Self-Regulated Learning Theory, this study analyzed the influence of motivational factors (value, costs, cognitive regulation, and scheduling) on learner participation. An e-learning program was implemented on MoodleCloud, and learners completed a survey before going through the program. Regression analysis was conducted using the survey response data along with the participation score, calculated using the log data. The results of the analysis demonstrated that value and scheduling significantly influenced learner participation, with gender differences found in value. This means that as adult learners perceive higher value in the e-learning program and possess better scheduling skills, they are more likely to participate. These findings can be utilized in developing teaching and learning strategies for both learners and instructors, ultimately helping to prevent dropout in e-learning.