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Professional Development in Computational Thinking for teachers in Colombia | IEEE Conference Publication | IEEE Xplore

Professional Development in Computational Thinking for teachers in Colombia


Abstract:

This Research to practice work in progress paper explores teachers' experiences about a professional development program focused on discipline-based computational thinkin...Show More

Abstract:

This Research to practice work in progress paper explores teachers' experiences about a professional development program focused on discipline-based computational thinking (CT). The goal of the program is to integrate computational practices into disciplinary learning environments at the K-12 level in Colombia. To promote this integration and to explore teachers' conceptions and possible scenarios to integrate CT in this context, the research team designed, implemented, and assessed a 20-hour professional development program with 21 teachers from public middle and high schools in Antioquia, Colombia. The program introduced the concepts and practices of CT and discussed why these are relevant for students to learn. A use-modify-create instructional sequence was used as the pedagogical approach to scaffold participants' learning process. At the end of the workshop, the teachers completed a survey regarding their experience in the program. Preliminary results indicate that the participating teachers increased their knowledge and interest to integrate computational thinking practices into their disciplinary learning environments.
Date of Conference: 13-16 October 2021
Date Added to IEEE Xplore: 20 December 2021
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Conference Location: Lincoln, NE, USA
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I. Introduction

Computational thinking (CT) is a set of practices, concepts, and methods from computer science that support problem-solving and representation of complex phenomena across disciplinary areas [1]. These practices include abstraction, decomposition, algorithm design, automation, and debugging among others. Professionals from any discipline can use these practices for modeling complex phenomena, representing knowledge, and processing large datasets. These practices can support student learning within disciplinary learning environments, while the disciplines provide a meaningful context to develop computational thinking skills [2].

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