Measuring Teacher Self-Efficacy for Integrating Computational Thinking in Science (T-SelECTS)

With computational thinking (CT) emerging as a prominent component of 21st century science education, equipping teachers with the necessary tools to integrate CT into science lessons becomes increasingly important. One of these tools is confidence in their ability to carry out the integration of CT. This confidence is conceptualized as self-efficacy: the belief in one’s ability to perform a specific task in a specific context. Self-reported self-efficacy in teaching has shown promise as a measure of future behavior and is linked to teacher performance. Current measures of teacher self-efficacy to integrate CT are limited, however, by narrow conceptualizations of CT, oversight of survey design research, and limited evidence of instrument validity. We designed a valid and reliable measure of Teacher Self-Efficacy for integrating Computational Thinking in Science (T-SelECTS) that fits a single latent factor structure. To demonstrate the instrument’s value, we collected data from 58 pre-service teachers who participated in a CT module within their science methods course at a large Mid-Atlantic university. We found evidence of significant development in pre-service teachers’ self-efficacy for integrating CT into science instruction. We discuss how the T-SelECTS instrument could be used in teacher education courses and professional development to measure change in self-efficacy.