Measuring Computational Thinking Self-Efficacy (CT-SEI): Instrument development and preliminary evaluation

2026-07-20

AI Summary

This study addresses the current lack of valid instruments for measuring computational thinking self-efficacy among university students—a psychological construct that critically predicts learning outcomes. Drawing on a systematic review of the literature, the authors developed an initial pool of items spanning the five core dimensions of computational thinking. Following expert validation, they refined this pool through principal component analysis and confirmatory factor analysis using data from a diverse sample of 270 undergraduate students recruited via Prolific and a Costa Rican university. The final 27-item scale demonstrates strong psychometric properties and reveals a higher-order structure comprising two latent factors: “Creating Solutions” and “Evaluating Solutions.” This work provides the first validated, structured instrument for assessing computational thinking self-efficacy, offering a robust foundation for future instructional interventions and personalized support strategies.

Abstract

Much effort is put into helping students at different educational levels develop Computational Thinking (CT) skills. Self-efficacy is important for skill development. It can predict perseverance, engagement and success on educational tasks. We created an instrument to measure self-efficacy of students in higher education for the CT skills abstraction, algorithmic thinking, decomposition, evaluation and generalization. First, 91 candidate items were created by including, adapting and extending items found in the literature. These items were evaluated by experts in the field of CT and education. 54 items remained and to reduce the number of items further, data was collected from 270 students in higher education recruited both through Prolific and a university setting in Costa Rica. Through principle component analysis (PCA) using a subset of 200 responses, the number of items was reduced to 27. Confirmatory factor analysis (CFA) using the remaining responses in the dataset showed the items can be divided into two categories: (1) creating the solution and (2) evaluating the solution. The created instrument can be valuable when assessing CT self-efficacy of students in higher education. With additional validation (e.g. examination of test-retest validity), we believe the scale could be used to evaluate the effectiveness of interventions, or decide what interventions should be provided to foster CT skill development.

Problem

• Computational Thinking
• Self-Efficacy
• Instrument Development
• Higher Education
• Measurement

Innovation

• Computational Thinking
• Self-efficacy
• Instrument Development
• Principal Component Analysis
• Confirmatory Factor Analysis