<![CDATA[This study examined the effectiveness of STEAM pre-vocational learning in terms of the development of students’ psychomotor skills and affective attitudes. The study employed a descriptive quantitative approach with a project-based observation method among 23 fifth-grade students at an elementary school in Indonesia. The learning process was developed based on a Mini Solar Panel House project, which was an authentic pre-vocational learning task that integrated science, technology, engineering, art, and mathematics. The data were gathered using a validated process observation tool, which had content validity confirmed by Aiken’s V-value ( 0.75) and high internal consistency for both psychomotor (α = 0.87) and affective (α = 0.84) dimensions. The psychomotor dimension included four components: work safety, work preparation, work procedure, and presentation of results, while the affective dimension included responsibility, teamwork, communication, and discipline. The findings indicated that students demonstrated high performance in both dimensions with a mean score of 1.80 for psychomotor skills and 1.78 for affective attitudes on a 2.00-point scale. The highest achievements were obtained in work procedure (M = 1.90) and discipline (M = 1.93), while presentation skills (M = 1.70) and teamwork (M = 1.60) were relatively lower. The Pearson’s correlation analysis showed a strong positive correlation between the psychomotor and affective areas (r = 0.76, p 0.01), with the highest correlation between work procedure and discipline (r = 0.81). The findings of this study show that STEAM-based pre-vocational learning is an effective approach to support the integrated development of technical competencies and work-related attitudes, as measured by process-based assessment. This study adds to the existing literature on STEAM by providing empirical insights into the integration of psychomotor and affective areas in early pre-vocational settings. It has implications for the development of holistic and project-based learning models.]]>
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