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Exploring gender and educational background effects on 21st-century competencies in robot-integrated STEM education: The role of learning attitudes and satisfaction

Year: 2025
Type: article
Abstract: In the 21st century, STEM (Science, Technology, Engineering, and Mathematics) education plays a critical interdisciplinary role in cultivating students’ seven core competencies (7C) learning performance: computational thinking, collaboration, communication, problem-solving, metacognitive awareness, creative thinking, and critical thinking. While these competencies are essential for future-ready learners, limited research has quantitatively examined how learner characteristics, such as gender, education level, learning attitudes, and learning satisfaction, affect students’ 7C learning performance in robot-integrated STEM education. This study adopted a quantitative research design involving 67 students from healthcare-related programs who participated in a robot-integrated STEM learning activity. Data were collected through validated instruments measuring learning attitudes, satisfaction, and 7C learning performance. Statistical analyses, including t tests, one-way ANOVA, Pearson correlation, and multiple regression, were used to examine group differences and predictive effects. The findings revealed no significant gender differences in perceived learning needs. However, students with higher education levels demonstrated stronger development in 7C learning performance. In addition, learning attitudes and learning satisfaction significantly predicted students’ 7C learning performance, and the seven competencies were strongly intercorrelated. Based on these findings, the study proposes strategies for enhancing robot-integrated STEM and programming education, particularly within healthcare-related contexts, through interdisciplinary curriculum design. These insights provide guidance for educators, curriculum designers, and policymakers aiming to foster holistic, competency-based STEM education.
DOI: 10.30191/ets.202510_28(4).sp11
Author: Wen-Song Su, Gwo-Jen Hwang, Chengjiu Yin and Ching-Yi Chang
Language: English
Open Access status: green
OpenAlex ID: W7095033656
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