A concept mapping-based self-regulated learning approach to promoting students’ learning achievement and self-regulation in STEM activities

ABSTRACT

STEM (Science, Technology, Engineering, and Mathematics) education is attracting increasing attention, but how to improve students’ STEM learning achievement is still a big challenge. In the process of learning, students actively set learning goals, organize learning content, set learning standards, and independently adapt learning strategies and monitoring measures in order to achieve their own learning goals and to help them learn STEM knowledge and skills. Concept mapping is considered to be an effective visualized knowledge organizing tool that helps students to manage and organize their knowledge systematically and visually. This study proposes a concept mapping-based self-regulated learning approach. A quasi-experiment was conducted using a concept mapping-based self-regulated learning system in order to evaluate the effectiveness of the approach. The experimental results show that the proposed approach significantly improved students’ STEM skills, but not their STEM knowledge compared to the conventional self-regulated learning approach. The proposed learning approach helped to improve students’ self-regulation compared to the conventional self-regulation learning approach. In short, the concept mapping-based self-regulation learning approach enhances students’ self-efficacy and self-regulation, which in turn helps students to learn STEM skills effectively.

KEYWORDS:

• STEM education
• concept mapping
• self-efficacy
• self-regulation
• learning achievement

Acknowledgements

This study is supported by the National Educational Science Plan, the People's Republic of China. The National Educational Science Plan “Thirteenth Five-Year Plan” in 2019. Project name: Big data-driven junior high school students’ academic development monitoring and precision intervention research. Contract number: BCA190089. This study is also supported by the Education Department of Zhejiang Province. Project name: System design and application of precise employment in colleges and universities based on data mining. Contract number: GH2022170.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Department of Education Zhejiang Province: [Grant Number GH2022170]; The National Educational Science Plan: [Grant Number BCA190089].

Notes on contributors

Jian-Wen Fang

Jian-Wen Fang is an Associate Professor at the College of Education, Wenzhou University. His research interests include STEM education, AI in education, and digital game-based learning.

Li-Yuan He

Li-Yuan He is a graduate student at the College of Education, Wenzhou University. Her research interests include STEM education, AI education, and digital game-based learning.

Gwo-Jen Hwang

Gwo-Jen Hwang is a chair professor at the Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology. His research interests include mobile learning, digital game-based learning, flipped classrooms and AI in education.

Xiu-Wei Zhu

Xiu-Wei Zhu is a lecture at the Admissions and Employment Department, Wenzhou University. Her research interests include career planning education, mental health education, and digital game-based learning.

Chu-Nu Bian

Chu-Nu Bian is a professor at the college of Education, Wenzhou University. Her research interests include computational thinking, deep learning, and ICT education in primary and secondary education.

Qing-Ke Fu

Qing-Ke Fu is a lecture at the college of Education, Huzhou University. His research interests include STEM education, AI education, Technology-facilitated learning and digital game-based learning.