PnB-M01 - WITHDRAWN: A Social Cognitive Career Theory Based Measure of Statistics Goals 

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This poster presents the development of a 30-item measure assessing statistics and data science–related goal development, rooted in Social Cognitive Career Theory (SCCT). SCCT proposes that future career choices are shaped by a motivational pipeline from self-efficacy beliefs and outcome expectations to interest, goals, and actions. Items were developed in direct alignment with SCCT constructs and refined through an iterative psychometric process to situate them within statistics and data science. Data were collected from 711 undergraduate students enrolled in an introductory statistics course at a public R1 university. Students were primarily first-year undergraduates, with both online and in-person sections. Coefficient alpha estimates across subscales ranged from α = .799 to .900, indicating good reliability. Model fit statistics from a structural equation model (SEM) provide support for the proposed SCCT-based relationships between factors (CFI = .917; TLI = .908). These findings offer preliminary evidence supporting the measure’s use in assessing students’ aspirations toward statistics and data science careers. AI is increasingly used in the statistics classroom, changing how students engage with quantitative tasks. AI was not used in the design, analysis, or writing of this study. Rather, it was embedded in the classroom environment through an AI-enabled tool (RTutor.AI) used in coursework. This reflects growing interest in how AI-mediated learning environments may shape motivational processes in students. Our prior research (presented at USCOTS 2025) suggests that RTutor.AI use is differentially associated with SCCT constructs, particularly increases in outcome expectations and goal setting, but not self-efficacy, interest, or actions. This work informed instructional decisions in a large introductory statistics course, particularly the incorporation of AI tools into the course design. The resulting measure provides a domain-specific tool for capturing SCCT-based motivational processes in statistics and data science learning environments. It can be used as a pre/post assessment of change, one-time assessment of students’ motivation, and as an outcome measure for related research. This work enables instructors and researchers to examine how educational experiences—including AI-mediated tools—relate to students’ motivational development and career-relevant goal formation in statistics and data science. WITHDRAWN