Unlocking Deep Learning: How Self-Regulated Learning Shapes Chinese Students' Online Course Experiences
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| Title: | Unlocking Deep Learning: How Self-Regulated Learning Shapes Chinese Students' Online Course Experiences |
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| Language: | English |
| Authors: | Lingli Li (ORCID |
| Source: | SAGE Open. 2025 15(3). |
| Availability: | SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com |
| Peer Reviewed: | Y |
| Page Count: | 14 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Online Courses, Self Management, Undergraduate Students, Student Attitudes, Instructional Effectiveness, Predictor Variables, Foreign Countries, Concept Formation, Knowledge Level, Technology Uses in Education, Student Responsibility, Student Evaluation, Educational Objectives, Standards |
| Geographic Terms: | China |
| Assessment and Survey Identifiers: | Study Process Questionnaire |
| DOI: | 10.1177/21582440251365390 |
| ISSN: | 2158-2440 |
| Abstract: | The quality of online learning is generally acknowledged to be a crucial element in students' academic achievement. Using a quantitative, cross-sectional paradigm, this study sought to analyze the link between university students perceived online course experiences and deep learning, with an emphasis on the mediating function of self-regulation through the adapted instruments of the "Online Course Experience Questionnaire," "Self-regulated Learning Questionnaire," and "Deep Learning Scale." Employing a convenience sampling strategy, we collected data from an online questionnaire survey of 1,098 Chinese undergraduate students in November 2022. Structural equation modeling analysis indicated that students' perceptions of good teaching in online course experience questionnaire (OCEQ) significantly predicted deep learning during online. Additionally, our research showed that the association between students' opinions of effective instruction and deep learning strategies was mediated by self-regulation. Deep learning was found to be influenced indirectly by how clearly goals and standards were perceived, with self-regulated learning as the mediator. However, appropriate workload and assessment in OCEQ were neither directly nor indirectly related with deep learning. This study extends the understanding of student course experiences and provides actionable insights for educators to design strategies that foster deep learning in digital environments, ultimately enhancing the quality of online education systems. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1486972 |
| Database: | ERIC |
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