Michael T. Giang | Searcharxiv

arXiv2026

Understanding teens' self-beliefs when learning to construct and deconstruct AI/ML systems: Developing a survey instrument

Despite growing calls to foster AI literacy, there are few available survey instruments designed for children and youth that study computational empowerment alongside construction and deconstruction activities. In such activities, learners' beliefs about their abilities and attributes can impact their engagement. In this paper, we introduce and validate a survey instrument with constructs related to construction (creative expression and problem-solving self-beliefs) and deconstruction (auditing self-efficacy and fascination with auditing), along with more general self-beliefs related to design justice and the value of learning about AI/ML. We administered the instrument to 124 teenagers and assessed the six-factor structure of the instrument using confirmatory factor analysis. In addition to confirming the structure, we found that design justice beliefs strongly correlated with problem-solving, auditing self-efficacy, and creative expression.

cs.HC↗

arXiv2023

Connecting Beliefs, Mindsets, Anxiety, and Self-Efficacy in Computer Science Learning: An Instrument for Capturing Secondary School Students' Self-Beliefs

Background and Context: Few instruments exist to measure students' CS engagement and learning especially in areas where coding happens with creative, project-based learning and in regard to students' self-beliefs about computing. Objective: We introduce the CS Interests and Beliefs Inventory (CSIBI), an instrument designed for novice secondary students learning by designing projects (particularly with physical computing). The inventory contains subscales on beliefs on problem solving competency, fascination in design, value of CS, creative expression, and beliefs about context-specific CS abilities alongside programming mindsets and outcomes. We explain the creation of the instrument and attend to the role of mindsets as mediators of self-beliefs and how CSIBI may be adapted to other K-12 project-based learning settings. Method: We administered the instrument to 303 novice CS secondary students who largely came from historically marginalized backgrounds (gender, ethnicity, and socioeconomic status). We assessed the nine-factor structure for the 32-item instrument using confirmatory factor analysis and tested the hypothesized model of mindsets as mediators with structural equation modeling. Findings: We confirmed the nine factor structure of CSIBI and found significant positive correlations across factors. The structural model results showed that problem solving competency beliefs and CS creative expression promoted programming growth mindset, which subsequently fostered students' programming self-concept. Implications: We validated an instrument to measure secondary students' self-beliefs in CS that fills several gaps in K-12 CS measurement tools by focusing on contexts of learning by designing. CSIBI can be easily adapted to other learning by designing computing education contexts.

cs.CY↗