Exploring Computational Thinking Skills in Pre-Service Science Teachers: A Rasch Model Analysis - Riskawati Riskawati; Nurul Mutmainnah Herman; Dirgah Kaso Sanusi; Ihfa Indira Nurnaifah Idris; Abdurrahman Abdurrahman; Hendra B.; Sitti Rahma Yunus (Gawi: Journal of Action Research, 2026)
Computational Thinking (CT) has been increasingly recognized as a key competency in science education, especially in preparing future educators to foster 21st-century skills such as problem-solving and digital literacy. However, limited CT instruction in undergraduate programs has resulted in inadequate readiness among pre-service science teachers, particularly in contexts like Indonesia. This study addresses the gap in the evaluation of CT skills among pre-service teachers by using robust psychometric approaches. This article investigates the psychometric properties of a CT assessment tool, examines the CT ability levels of pre-service science teachers, and evaluates the presence of gender-based item bias. The study employed a quantitative, cross-sectional design utilizing Rasch model analysis to assess instrument reliability, item fit, and differential item functioning (DIF) across gender. A total of 419 pre-service science teachers from two universities in Indonesia were selected using stratified random sampling to ensure proportional representation across academic levels. Data collection and analysis: Data were collected using the 28-item Computational Thinking Test (CTt) and analyzed using Winsteps software for Rasch modeling, including item/person reliability, separation, fit statistics, Wright maps, and DIF analysis. Results indicated high item reliability and good model fit, though person reliability was relatively low, suggesting limited ability discrimination. Several items exhibited gender-based DIF, with some favoring males and others favoring females. The CTt shows strong potential for measuring CT, though refinement is needed to improve its ability to differentiate between skill levels and ensure gender fairness. CT is increasingly recognized as an essential competency in science education to support problem-solving, analytical reasoning, and 21st-century digital literacy skills. However, empirical evidence regarding the measurement quality of CT instruments among pre-service science teachers in Indonesia remains limited. This study aimed to evaluate the psychometric properties of the CTt, describe the distribution of CT skills among pre-service science teachers, and examine gender-based item bias using the Rasch model. A quantitative cross-sectional design was employed involving 419 pre-service science teachers from two Indonesian universities selected through stratified random sampling. Data were analyzed using Winsteps software to examine reliability, separation index, item fit, Wright map distribution, and DIF. The results showed that the CTt demonstrated high item reliability and good model fit, indicating that the instrument is appropriate for measuring CT skills. However, person reliability was relatively low, suggesting limited sensitivity in distinguishing individual ability levels. Wright map analysis indicated that most respondents were distributed at a moderate ability level. In addition, DIF analysis identified several items with potential gender bias, favoring both male and female respondents. Overall, the CTt shows strong potential as an instrument for measuring CT skills, although further refinement is needed to improve measurement sensitivity and ensure gender fairness. These findings contribute to the development of Rasch model–based CT assessment in pre-service science teacher education.