Models for computer science teacher preparation: Developing teacher knowledge
Warner, Jayce R.
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Yadav, Aman, Connolly, Cornelia, Berges, Marc, Chytas, Christos, Franklin, Crystal, Hijón-Neira, Raquel, Leftwich, Anne, Marguliex, Lauren, Macann, Victoria, Warner, Jayce R. (2022). Models for computer science teacher preparation: Developing teacher knowledge. Paper presented at the ITiCSE '22: Proceedings of the 27th ACM Conference on Innovation and Technology in Computer Science Education, Dublin, Ireland, 8-13 July, https://doi.org/10.1145/3502717.3532166
Across the globe, Computer Science Education has grown tremendously over the past decade to teach primary and secondary students computing ideas and tools. From integrating computational thinking in disciplines to teaching computer science as a stand alone subject, models for teacher preparation range from one and done professional learning workshops to full certificate and licensure programs. The group will focus on providing a landscape of how CS teachers are prepared academically in various countries and make evidence-based recommendations for how teachers should be educated to develop knowledge and skill to teach computer sci- ence. The working group will also discuss how to develop these knowledge systems while promoting instruction that is equitable and centers students in the classroom. In addition, the working group will focus on new directions in computing education (such as, artificial intelligence and machine learning) and their implica- tions for teacher preparation. We will bring together a group of international computer science education scholars who have been engaged in teacher preparation. In addition to what knowledge teachers need to teach CS, we will also focus on how the field is preparing teachers to think critically about AI/ML and the role of computer science in the design of technology tools to achieve goals while mitigating potential societal harms.