Gaofeng Li

Introduction: Development of computational thinking (CT) in science education requires assessment tools that capture dynamic skill progression within authentic classroom settings. Existing assessment tools focus on CT overall performance with approximate sub-dimensions and level divisions, making it challenging to provide continuous, targeted guidance for fostering high school students’ CT competencies. This study aims to develop and validate the High School Students’ Computational Thinking Observation Protocol (HS-CTOP), a process-oriented tool for tracking high school students’ CT progression in science classrooms. Methods: Grounded in the Abstraction, Decomposition, Evaluation, Generalization, and Algorithmic thinking (ADEGA) framework, the HS-CTOP operationalizes CT into seventeen sub-dimensions and four proficiency levels. Data were derived from video-coded behaviors of 613 students across 120 biology classes in seven regions of mainland China, with validation conducted via the Rasch partial credit model. Results: The results confirm the HS-CTOP’s robust psychometric properties, including an item reliability of 0.96, person reliability of 0.83, and satisfactory unidimensionality. The protocol effectively distinguishes CT levels across different student groups. Discussion: The HS-CTOP provides educators with detailed insights into CT development patterns to inform differentiated instruction in science classrooms.

This study designed and validated the Engineering Thinking Assessment Scale (ETAS) to assess the engineering thinking of senior high school students in mainland China and to explore the correlation between their self-efficacy in different engineering thinking elements and their practical performance. The ETAS, comprising 28 items, was designed through four phases: defining dimensions, designing, pilot testing, and final assessment. Each item includes two tiers: the emotional tendency tier, measuring self-efficacy, and the practical performance tier, assessing actual application in engineering projects. A study with 510 students (485 valid responses) confirmed the scale's strong reliability and validity. While most students demonstrated above-average engineering thinking, higher self-efficacy did not consistently translate to better performance. Notably, 10 engineering thinking elements showed weak correlations (0.2 < |r| < 0.3) between self-efficacy and performance, indicating confidence without effective application. This study offers a detailed evaluation of engineering thinking abilities, highlighting gaps between perceived and actual competency in specific engineering elements.