In recent decades, the integration of technology, particularly data logging, has become a cornerstone of effective science teaching, placing increased emphasis on the cultivation of teachers’ Technological Pedagogical Content Knowledge (TPACK). While the TPACK framework has garnered attention in science education, limited research has explored the interplay of TPACK components within a constructivist context, particularly in the context of data-logging-supported chemistry class. To bridge this research gap, this study conducted a comprehensive survey on TPACK with 181 pre-service chemistry teachers, probing their TPACK as it pertains to data logging. Results from both exploratory and confirmatory factor analyses underscored the reliability and validity of the survey instrument. Subsequently, structural equation modeling was employed to illuminate the intricate relationships among various facets of TPACK. Findings suggested a sequential developmental pattern within TPACK, with teachers’ Technological Pedagogical Knowledge (TPK), Technological Content Knowledge (TCK), and Pedagogical Content Knowledge (PCK) all exerting a positive influence on their overall TPACK. Moreover, this study unveiled a significant correlation between pre-service chemistry teachers’ data-logging TPACK and their capacity for design thinking. Interestingly, gender differences in TPACK were negligible. In light of these findings, this study not only contributes to our understanding of TPACK development but also has practical implications for nurturing pre-service chemistry teachers’ proficiency in TPACK when using data-logging.

As STEM education increasingly emphasizes the development of both cognitive and non-cognitive competencies, there remains a critical gap in understanding how pedagogical approaches such as design thinking can simultaneously foster computational thinking (CT) and learning motivation in primary school contexts. This study investigates the effectiveness of a design thinking–guided STEM intervention in fostering primary students’ CT skills and learning motivation. It further examines the moderating roles of gender and educational stage, while addressing three specific gaps: the lack of empirical evidence on design thinking as a framework for CT development, the need for a nuanced understanding of differential growth across CT dimensions, and the unresolved relationship between general STEM motivation and domain-specific CT performance. A one-month quasi-experimental study was conducted with 34 fourth- to sixth-grade students (22 male, 12 female) from a public school in Shenzhen, China. Participants engaged in a micro: bit–based project to design an automated plant monitoring system, structured around the five phases of design thinking: empathize, define, ideate, prototype, and test. CT was assessed using pre- and post-test Bebras challenges across five dimensions (abstraction, algorithmic thinking, decomposition, evaluation, and generalization). Learning motivation was measured using adapted STEM self-efficacy and ability belief scales. Data were analyzed using paired t-tests, ANOVA, and content analysis. Results revealed a statistically significant overall improvement in CT following the intervention. Among the five dimensions, evaluation skills showed the most substantial growth, while algorithmic thinking remained the strongest component throughout. However, decomposition and generalization exhibited relative declines in normalized scores, suggesting these areas require additional instructional support. No significant gender or grade-level differences were found in overall CT gains, although greater performance variability was observed among female students, and Grade 5 demonstrated the highest mean post-test score. Notably, no significant correlation emerged between CT performance and STEM motivation, challenging conventional assumptions about the motivation–cognition relationship in project-based learning environments. This study provides novel empirical evidence for design thinking as an effective pedagogical framework for cultivating multi-dimensional CT in primary STEM education. It advances the field by revealing differential developmental trajectories across CT components and identifying a dissociation between general STEM motivation and CT performance, findings that underscore the need for domain-specific motivational frameworks.