Computational Thinking: Early Childhood Teachers' and Prospective Teachers' Preconceptions and Self-Efficacy

Abstract

Computational thinking (CT) is considered a group of problem-solving skills that the next generations are expected to possess. The most efficient way to make them acquire these skills is to incorporate CT into K-12 education. To this end, various education programs have been designed to improve teachers' and prospective teachers' competence in CT. Such programs designing educational experiences based on teachers' and prospective teachers' preexisting opinions and self-efficacy perceptions about CT could achieve better results. Although the acquisition of CT skills has been suggested to start early on, these beliefs of early childhood teachers and prospective teachers have been underexplored. Therefore, this exploratory study aims to examine early childhood teachers' and prospective teachers' preconceptions and self-efficacy about CT. The study was conducted with 63 teachers and 78 prospective teachers in Turkey. Data were collected via an online survey in the spring of the 2020-2021 academic year. The preconceptions were assessed using a structured questionnaire, while the CT self-efficacy was measured with the Computational Thinking Scale. The findings showed similarities between teachers and prospective teachers in the preconceptions of CT. Both of them most strongly associated CT with logical thinking, problem-solving, using algorithms, coding/programming, doing mathematics, using technology in teaching, and using computers. Yet, teachers reported stronger associations between CT and logical thinking, using algorithms, and coding/programming. Furthermore, teachers' self-efficacy perceptions in CT were significantly higher. The study findings provide some needed information to design professional development programs aiming to enhance CT practices in early education settings.