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Pre-Service Computer Science Teachers’ Computational Thinking Attitudes and Performance on Python Tasks

Published:17 November 2022Publication History

ABSTRACT

For more than a decade, there has been debate about how to describe the concept of computational thinking (CT), focusing on the skills of decomposition, abstraction, pattern recognition, and algorithmic thinking. As CT can also be seen as a problem-solving process, not only the skills and strategies are an important part of CT, but also the attitudes of the problem solvers. These have already been described as persistence, dealing with complexity, ambiguity, and confidence. An important competence for future computer science teachers is to confidently guide students’ problem-solving process. This work presents preliminary results on the relationship between pre-service computer science teachers’ CT attitudes and their performance on Python tasks. Therefore, N=19 pre-service computer science teachers solved three tasks in Python and rated their attitudes towards solving the tasks. However, the preliminary results only allow to draw tentative and interpretative conclusions.

References

  1. Zubair Ahsan, Unaizah Obaidellah, Mahmoud Danaee, 2022. Is Self-Rated Confidence a Predictor for Performance in Programming Comprehension Tasks?APSIPA Transactions on Signal and Information Processing 11, 1(2022).Google ScholarGoogle Scholar
  2. Valerie Barr and Chris Stephenson. 2011. Bringing computational thinking to K-12. ACM Inroads 2, 1 (feb 2011), 48. https://doi.org/10.1145/1929887.1929905Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Holger Danielsiek, Laura Toma, and Jan Vahrenhold. 2018. An instrument to assess self-efficacy in introductory algorithms courses. ACM Inroads 9, 1 (2018), 56–65.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Georgios Fessakis and Stavroula Prantsoudi. 2019. Computer Science Teachers’ Perceptions, Beliefs and Attitudes on Computational Thinking in Greece.Informatics in Education 18, 2 (2019), 227–258.Google ScholarGoogle ScholarCross RefCross Ref
  5. Anaclara Gerosa, Víctor Koleszar, Gonzalo Tejera, Leonel Gómez-Sena, and Alejandra Carboni. 2021. Cognitive abilities and computational thinking at age 5: Evidence for associations to sequencing and symbolic number comparison. Computers and Education Open 2 (2021), 100043.Google ScholarGoogle ScholarCross RefCross Ref
  6. Morris Siu-Yung Jong, Jie Geng, Ching Sing Chai, and Pei-Yi Lin. 2020. Development and predictive validity of the computational thinking disposition questionnaire. Sustainability 12, 11 (2020), 4459.Google ScholarGoogle ScholarCross RefCross Ref
  7. Siu Cheung Kong and Yi Qing Wang. 2020. Formation of computational identity through computational thinking perspectives development in programming learning: A mediation analysis among primary school students. Computers in Human Behavior 106 (2020), 106230.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Katharine McClelland and LA Grata. 2018. Review of the Importance of Computational Thinking in K-12. Proceedings of the eLmL(2018), 2–34.Google ScholarGoogle Scholar
  9. Bernadette Spieler, Ferenc Kemény, Karin Landerl, Bernd Binder, and Wolfgang Slany. 2020. The learning value of game design activities: Association between computational thinking and cognitive skills. In Proceedings of the 15th Workshop on Primary and Secondary Computing Education. 1–4.Google ScholarGoogle Scholar
  10. Bernhard Standl. 2017. Solving Everyday Challenges in a Computational Way of Thinking. In Informatics in Schools: Focus on Learning Programming: 10th International Conference on Informatics in Schools: Situation, Evolution, and Perspectives, ISSEP 2017, Helsinki, Finland, November 13-15, 2017, Proceedings, Valentina Dagiene and Arto Hellas (Eds.). Springer International Publishing, Cham, 180–191. https://doi.org/10.1007/978-3-319-71483-7_15Google ScholarGoogle ScholarCross RefCross Ref
  11. Phil Steinhorst, Andrew Petersen, and Jan Vahrenhold. 2020. Revisiting self-efficacy in introductory programming. In Proceedings of the 2020 ACM Conference on International Computing Education Research. 158–169.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Katerina Tsarava, Luzia Leifheit, Manuel Ninaus, Marcos Román-González, Martin V Butz, Jessika Golle, Ulrich Trautwein, and Korbinian Moeller. 2019. Cognitive correlates of computational thinking: Evaluation of a blended unplugged/plugged-in course. In Proceedings of the 14th Workshop in Primary and Secondary Computing Education. 1–9.Google ScholarGoogle ScholarDigital LibraryDigital Library

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  • Published in

    cover image ACM Other conferences
    Koli Calling '22: Proceedings of the 22nd Koli Calling International Conference on Computing Education Research
    November 2022
    282 pages
    ISBN:9781450396165
    DOI:10.1145/3564721

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    • Published: 17 November 2022

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