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Impact of intelligent robotics-enabled STEM on primary school students’ learning motivation

Year: 2025
Type: article
Abstract: Currently, STEM education often emphasizes performance on final exams, which can undermine the development of students’ intrinsic motivation and long-term career interests. This focus on assessment results leads to a narrow approach that neglects the broader goals of engaging students in meaningful learning experiences. This study investigates the impact of an intelligent robotics-enabled STEM education project (IRESEP) on the interest levels of elementary school students in Beijing, involving 42 students (aged 10–12, from Grades 4 to 6) and a STEM teacher. The IRESEP was designed using effective teaching strategies such as phased teaching, blended learning, life-oriented teaching, and project-based learning. A mixed-methods approach was employed, combining quantitative and qualitative analyses to examine motivation mechanisms and influencing factors. Quantitative data were gathered through student questionnaires assessing basic information, STEM motivation performance, and influencing factors, analyzed using descriptive statistics, independent sample t-tests, cluster analysis, correlation analysis, and regression modeling. Qualitative insights were derived from interviews with both teachers and students. Results indicated that the IRESEP significantly enhanced students’ cognition, interest, and abilities in STEM, while also increasing their willingness to pursue STEM careers. Key factors influencing students’ STEM motivation included Teacher Support, Collaboration Skills, and Positive Academic Emotions. These findings suggest that intelligent robotics-based STEM projects should prioritize teacher support, collaborative learning, and positive educational experiences to effectively foster student motivation. The study offers a practical framework for designing engaging STEM programs that not only enhance student interest but also support their career aspirations in primary education.
DOI: 10.30191/ets.202510_28(4).sp05
Author: Yuyue Zhang, Wen Zhou, Yu Liu and Jiyou Jia
Language: English
Open Access status: green
OpenAlex ID: W7095036418
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