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Measuring self-efficacy to improve teacher professional learning opportunities in computing

Dr. Elena Prieto-Rodriguez
University of Newcastle, Australia ~ School of Education
Dr. Daniel Hickmott
The University of Sydney

Computational thinking and programming are part of mandatory curricula around the world, including many Australasian nations. Teachers across most levels of schooling now must teach these skills to students. Teaching these skills presents many challenges for teachers, as they typically were not part of their initial teacher education. There are a variety of professional learning opportunities for teachers to learn these skills, but there is a lack of research evaluating the impact of these opportunities. One of the challenges of evaluating professional learning opportunities is the lack of standardised instruments for relevant measures of impact. One measure that is often used in studies of professional learning is teachers’ self-efficacy. To measure self-efficacy in computational thinking, Bean et al. (2015) created and validated the Teachers’ Self-Efficacy in Computational Thinking (TSECT) instrument. In this talk we will present results from evaluating our programs’ impact using the TSECT instrument. Our results show that, generally, TSECT measures were low before the programs but much higher after, and that both sustained and short programs can have a positive impact on teachers’ self-efficacy in computational thinking. We will also discuss how we have used learning derived from measuring TSECT to improve the programs’ scope and delivery.



teacher professional development
computational thinking

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Cite this as:

Prieto-Rodriguez, E., & Hickmott, D. (2021, February). Measuring self-efficacy to improve teacher professional learning opportunities in computing. Paper presented at Australasian Mathematical Psychology Conference 2021. Via