Research Article
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Year 2024, Volume: 5 Issue: 1, 1 - 20

Taner Arabacıoglu

https://doi.org/10.52911/itall.1391292

Abstract

Covid-19 pandemisi tüm dünyayı derinden etkilemiştir. Pandemi sürecinde eğitim-öğretimin devam edebilmesi için acil uzaktan eğitim uygulamaları işe koşulmuştur. Araştırmanın amacı, blok tabanlı programlamanın pandemi başlangıcında, pandemi süresince ve pandemi sonrasında bilgi işlemsel düşünmeyi ve azmi nasıl etkilediğini belirlemektir. Çalışmada yarı deneysel desenlerden ön test-son test kontrol gruplu yöntem kullanılmıştır. Örneklem, Türkiye'deki bir devlet üniversitesinin Eğitim Fakültesi'nde öğrenim gören 104 öğretmen adayıdır. Araştırma sonucunda blok tabanlı kodlama eğitiminin, pandemi öncesi ve pandemi süreci gruplarında bilgi işlemsel düşünme ön test ve son test puanları arasında anlamlı bir farka neden olduğu görülmektedir. Söz konusu fark orta düzeyde bir etki büyüklüğüne sahiptir. Pandemi sonrası grubun ön test ve son test puanları arasında anlamlı bir fark bulunmamıştır. Gruplar arası fark incelendiğinde, pandemi öncesi ve pandemi sırasındaki grupların bilgi işlemsel düşünme becerilerinde pandemi sonrası gruba göre anlamlı derecede daha yüksek medyan değerlerine sahip olduğu görülmüştür. Bu sonuçlara göre pandeminin olumsuz etkilerinin pandemi sonrası grupta görüldüğü söylenebilir. Azim ölçeği sonuçları, ilginin tutarlılığı boyutu bağlamında uzaktan eğitimde bilişsel olmayan faktörlerin önemini vurgulamaktadır. Ayrıca azim ve bilgi işlemsel düşünme becerileri arasında anlamlı ve pozitif bir ilişkiyi işaret etmektedir.

Keywords

Scratch Bilgi işlemsel düşünme Azim Pandemi

References

  • Aho, A. V. (2012). Computation and computational thinking. The Computer Journal, 55(7), 832–835.
  • Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community? ACM Inroads, 2(1), 48-54. https://doi.org/10.1145/1929887.1929905
  • Bono, G., Reil, K., & Hescox, J. (2020). Stress and wellbeing in urban college students in the U.S. during the COVID-19 pandemic: Can grit and gratitude help?. International Journal of Wellbeing, 10(3), 39-57. https://doi.org/10.5502/ijw.v10i3.1331
  • Borghans, L., Duckworth, A. L., Heckman, J. J., & Ter Weel, B. (2008). The economics and psychology of personality traits. Journal of Human Resources, 43(4), 972-1059.
  • Bowman, A. N., Hill, P. L., Denson, N., & Bronkema, R. (2015). Keep on truckin’ or stay the course? exploring grit dimensions as differential predictors of educational achievement, satisfaction, and intentions. Social Psychological and Personality Science, 6(6), 639-645. https://dx.doi.org/10.1177/1948550615574300
  • Bozkurt, A., Karakaya, K., Turk, M. et al. (2022) The Impact of COVID-19 on Education: A Meta-Narrative Review. TechTrends 66, 883–896. https://doi.org/10.1007/s11528-022-00759-0
  • Caron, E. E., Drodt, A. C., Hicks, L. J., & Smilek, D. (2022). The impact of a global pandemic on undergraduate learning experiences: One year later. Trends in Neuroscience and Education, 28, 100184. https://doi.org/10.1016/j.tine.2022.100184
  • Cao, W., Fang, Z., Hou, G., Han, M., Xu, X., Dong, J., & Zheng, J. (2020). The psychological impact of the COVID-19 epidemic on college students in China. Psychiatry Research, 287, 112934. https://doi.org/10.1016/j.psychres.2020.112934
  • Ching, Y., Hsu, Y., & Baldwin, S. (2018). Developing computational thinking with educational technologies for young learners. TechTrends, 62, 563–573. https://doi.org/10.1007/s11528-018-0292-7
  • Christopoulou, M., Lakioti, A., Pezirkianidis, C., Karakasidou, E., & Stalikas, A. (2018). The Role of Grit in Education: A Systematic Review. Psychology, 9, 2951-2971. https://doi.org/ 10.4236/psych.2018.915171
  • Computational Thinking Teacher Resources https://www.csteachers.org/page/CompThinking
  • Corpus, J. H., Robinson, K. A., & Liu, Z. (2022). Comparing College Students’ Motivation Trajectories Before and During COVID-19:A Self-Determination Theory Approach. Frontiers in Education, 7, 848643.. https://doi.org/ 10.3389/feduc.2022.848643
  • Credé, M., Tynan, M. C., & Harms, P. D. (2017). Much ado about grit: A meta-analytic synthesis of the grit literature. Journal of Personality and Social Psychology, 113(3), 492–511. https://doi.org/10.1037/pspp0000102
  • Credé, M. (2018). What shall we do about grit? A critical review of what we know and what we don’t know. Educational Researcher, 47(9), 606–611. https://doi.org/10.3102/0013189X18801322
  • Denning, P. (2017) Remaining Trouble Spots with Computational Thinking. Comm. of the ACM, 60(6); 33-39. http://dx.doi.org/10.1145/2998438
  • Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92(6), 1087–1101. https://doi.org/10.1037/0022-3514.92.6.1087
  • Duckworth, A. L., & Quinn, P. D. (2009). Development and validation of the Short Grit Scale (Grit S). Journal of Personality Assessment, 91(2), 166-174.
  • Durak, H. Y., & Guyer, T. (2019). Programming with Scratch in primary school, indicators related to effectiveness of education process and analysis of these indicators in terms of various variables Gifted Education International. 35(3), 237-258. DOI: 10.1177/0261429419854223
  • Education in a Pandemic: The Disparate Impacts of COVID-19 on America’s Students. Accesssed October 25 2022. https://www2.ed.gov/about/offices/list/ocr/docs/20210608-impacts-of-covid19.pdf
  • Fong, C. J. (2022). Academic motivation in a pandemic context: a conceptual review of prominent theories and an integrative model. Educational Psychology. https://doi.org/10.1080/01443410.2022.2026891
  • Gabriele, L., Bertacchini, F., Tevernise, A., Vaca-Cardenas, L., Pantano, P., & Bilotta, E. (2019). Lesson Planning by Computational Thinking Skills in Italian Pre-service Teachers. Informatics in Education, 18(1), 69–104. https://doi.org/ 10.15388/infedu.2019.04
  • Glewwe, P., Huang, Q., & Park, A. (2017). Cognitive skills, noncognitive skills, and school-to-work transitions in rural China. Journal of Economic Behavior and Organization, 134, 141-164. https://doi.org/10.1016/j.jebo.2016.12.009
  • Hu, K., Godfrey, K., Ren, Q., Wang, S.,Yang, X., & Li, Q. (2022). The impact of the COVID-19 pandemic on college students in USA: Two years later. Psychiatry Research, 315, 114685. https://doi.org/10.1016/j.psychres.2022.114685
  • Hwang, M. H., Lim, H. J., & Ha, H. S. (2018). Efects of grit on the academic success of adult femalae students at Korean open university. Psychological Reports, 121(4), 705–725. https://doi.org/ 10.1177/0033294117734834
  • İlic, U. (2021). The impact of Scratch-assisted instruction on computational thinking (CT) skills of pre-service teachers. International Journal of Research in Education and Science (IJRES), 7(2), 426-444. https://doi.org/10.46328/ijres.1075
  • Jereb, E., Jerebic, J., and Uhu, M. (2022). Studying Habits in Higher Education Before and After the Outbreak of the COVID-19 Pandemic. Athens Journal of Education, 10 (1): 67–84. https://doi.org/10.30958/aje.10-1-4
  • Kerres, M., and Buchner, J. (2017). Education after the Pandemic: What We Have (Not) Learned about Learning. Education Sciences, 12, 315. https://doi.org/10.3390/educsci12050315
  • Korkmaz, Ö., Çakır, R., & Özden, M. Y. (2017). A validity and reliability study of the computational thinking scales(CTS). Computers in Human Behavior, 721, 558–569. https://doi.org/10.1016/j.chb.2017.01.005
  • Lazarinis, F., Karachristos, C. V., Stavropoulos, E., & Verykios, V. S. (2018). A blended learning course for playfully teaching programming concepts to school teachers. Education and Information Technologies. https://doi.org/ 10.1007/s10639-018-9823-2
  • Marcelino, M. J., Pessoa, T., Vieira, C., Salvador, T., & Mendes, A. J. (2018). Learning Computational Thinking and scratch at distance. Computers in Human Behavior, 80, 470–477. https://doi.org/10.1016/j.chb.2017.09.025
  • Mishra, P., & Yadav, A. (2013). Rethinking Technology & Creativity in the 21st Century. TechTrends, 57, 10–14.
  • Neroni, J., Meijs, C., Kirschner, P. A., Xu, K. M., & De Groot, R. (2022). Academic self‑efficacy, self‑esteem, and grit in higher online education: Consistency of interests predicts academic success. Social Psychology of Education, 25, 951-975. https://doi.org/ 10.1007/s11218-022-09696-5
  • Nichols, M. (2017) Can I choose to have grit? Non-cognitiveskills, behavior, and school choice. Journal of School Choice, 11(4), 622-641, https://doi.org/10.1080/15582159.2017.1395636
  • Nouri, J., Zhang, L., Mannila, L. and Noren, E. (2020), “Development of computational thinking, digital competence and 21st century skills when learning programming in K-9”, Education Inquiry, 11(1), 1-17. https://doi.org/10.1080/20004508.2019.1627844
  • Papert, S. (1980). Mindstorms: Children, computers and powerful ideas. New York: Basic Books.
  • Sarıçam, H., Çelik, İ., & Oğuz, A. (2016). Kısa Azim (Sebat) Ölçeğinin Türkçeye Uyarlanması- Geçerlik ve Güvenirlik Çalışması. Uluslararası Türkçe Edebiyat Kültür Eğitim Dergisi, 5(2), 927-935. Doi Number :http://dx.doi.org/10.7884/teke.622
  • Shirvsn, M. E., & Alamer, A. (2022). Modeling the interplay of EFL learners’ basic psychological needs, grit and L2 achievement. Journal of Multilingual and Multicultural Development. https://doi.org/ 10.1080/01434632.2022.2075002
  • Voogt, J., Fisser, P., Good, J., Mishra, P., & Yadav, A. (2015). Computational thinking in compulsory education: Towards an agenda for research and practice. Educ Inf Technol, 20, 715–728. https://doi.org/10.1007/s10639-015-9412-6
  • Weisskirch, R. S. (2018). Grit, self-esteem, learning strategies and attitudes and estimated and achieved course grades among college students. Curr Psychol, 37, 21–27. https://doi.org/ 10.1007/s12144-016-9485-4
  • Wing, J. M. (2006). Computational thinking. Communications of The ACM, 49(3), 33-35.
  • Wing, J. (2008). Computational thinking and thinking about computing. Phil. Trans. R. Soc. A, 366, 3717-3725. https://doi.org/ 10.1098/rsta.2008.0118.
  • Wolters, A. C., & Hussain, M. (2015). Investigating grit and its relations with college students’ self-regulated learning and academic achievement. Metacognition Learning, 10, 293–311. https://doi.org/ 10.1007/s11409-014-9128-9
  • Yadav, A., Stephenson, C., & Hong, H. (2017). Computational Thinking for Teacher Education. Communıcatıons Of The Acm, 60(4), 55-62. https://doi.org/ 10.1145/2994591
  • Zhao, X., Zhang, J., Li, W., Khan, K., Lu, Y., and Winters, N. 2021. Learners’ non-cognitive skills and behavioral patterns of programming: A sequential analysis. 2021 International Conference on Advanced Learning Technologies (ICALT), Tartu, July 12–15.

Scratch, computational thinking, and grit: At the beginning, during, and after the COVID-19 Pandemic

Year 2024, Volume: 5 Issue: 1, 1 - 20

Taner Arabacıoglu

https://doi.org/10.52911/itall.1391292

Abstract

The Covid-19 pandemic has deeply affected the whole world. In order to continue education during the pandemic, emergency distance education applications were utilized. The purpose of the research is to evaluate how block-based programming affects computational thinking (CT) and grit at the beginning, during and after pandemic. The study used a quasi-experimental pretest-posttest design. This sample was divided into three groups based on the stage of the COVID-19 Pandemic at which they were enrolled in a programming course: before the pandemic, during the pandemic, and after the pandemic. The participants are 104 teacher candidates in the Faculty of Education of a Turkish state university. As a result of the research, it is observed that block-based coding instruction has a significant difference between the pre-test and post-test scores of computational thinking in the pre-pandemic and pandemic groups. The difference in this case has a moderate effect size. There was no significant difference between the pretest and posttest scores of the post-pandemic group. Comparing the groups revealed that the pre-pandemic and during pandemic groups had significantly higher median scores in computational thinking skills than the post-pandemic group. According to these results, it can be argued that the negative effects of the pandemic were seen in the post-pandemic group. The results of the short grit scale emphasize the importance of non-cognitive factors in distance education in the context of the consistency of interest dimension. Moreover, it indicates a significant and positive relationship between grit and computational thinking skills.

Keywords

Scratch Computational thinking Grit Pandemic

References

  • Aho, A. V. (2012). Computation and computational thinking. The Computer Journal, 55(7), 832–835.
  • Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community? ACM Inroads, 2(1), 48-54. https://doi.org/10.1145/1929887.1929905
  • Bono, G., Reil, K., & Hescox, J. (2020). Stress and wellbeing in urban college students in the U.S. during the COVID-19 pandemic: Can grit and gratitude help?. International Journal of Wellbeing, 10(3), 39-57. https://doi.org/10.5502/ijw.v10i3.1331
  • Borghans, L., Duckworth, A. L., Heckman, J. J., & Ter Weel, B. (2008). The economics and psychology of personality traits. Journal of Human Resources, 43(4), 972-1059.
  • Bowman, A. N., Hill, P. L., Denson, N., & Bronkema, R. (2015). Keep on truckin’ or stay the course? exploring grit dimensions as differential predictors of educational achievement, satisfaction, and intentions. Social Psychological and Personality Science, 6(6), 639-645. https://dx.doi.org/10.1177/1948550615574300
  • Bozkurt, A., Karakaya, K., Turk, M. et al. (2022) The Impact of COVID-19 on Education: A Meta-Narrative Review. TechTrends 66, 883–896. https://doi.org/10.1007/s11528-022-00759-0
  • Caron, E. E., Drodt, A. C., Hicks, L. J., & Smilek, D. (2022). The impact of a global pandemic on undergraduate learning experiences: One year later. Trends in Neuroscience and Education, 28, 100184. https://doi.org/10.1016/j.tine.2022.100184
  • Cao, W., Fang, Z., Hou, G., Han, M., Xu, X., Dong, J., & Zheng, J. (2020). The psychological impact of the COVID-19 epidemic on college students in China. Psychiatry Research, 287, 112934. https://doi.org/10.1016/j.psychres.2020.112934
  • Ching, Y., Hsu, Y., & Baldwin, S. (2018). Developing computational thinking with educational technologies for young learners. TechTrends, 62, 563–573. https://doi.org/10.1007/s11528-018-0292-7
  • Christopoulou, M., Lakioti, A., Pezirkianidis, C., Karakasidou, E., & Stalikas, A. (2018). The Role of Grit in Education: A Systematic Review. Psychology, 9, 2951-2971. https://doi.org/ 10.4236/psych.2018.915171
  • Computational Thinking Teacher Resources https://www.csteachers.org/page/CompThinking
  • Corpus, J. H., Robinson, K. A., & Liu, Z. (2022). Comparing College Students’ Motivation Trajectories Before and During COVID-19:A Self-Determination Theory Approach. Frontiers in Education, 7, 848643.. https://doi.org/ 10.3389/feduc.2022.848643
  • Credé, M., Tynan, M. C., & Harms, P. D. (2017). Much ado about grit: A meta-analytic synthesis of the grit literature. Journal of Personality and Social Psychology, 113(3), 492–511. https://doi.org/10.1037/pspp0000102
  • Credé, M. (2018). What shall we do about grit? A critical review of what we know and what we don’t know. Educational Researcher, 47(9), 606–611. https://doi.org/10.3102/0013189X18801322
  • Denning, P. (2017) Remaining Trouble Spots with Computational Thinking. Comm. of the ACM, 60(6); 33-39. http://dx.doi.org/10.1145/2998438
  • Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92(6), 1087–1101. https://doi.org/10.1037/0022-3514.92.6.1087
  • Duckworth, A. L., & Quinn, P. D. (2009). Development and validation of the Short Grit Scale (Grit S). Journal of Personality Assessment, 91(2), 166-174.
  • Durak, H. Y., & Guyer, T. (2019). Programming with Scratch in primary school, indicators related to effectiveness of education process and analysis of these indicators in terms of various variables Gifted Education International. 35(3), 237-258. DOI: 10.1177/0261429419854223
  • Education in a Pandemic: The Disparate Impacts of COVID-19 on America’s Students. Accesssed October 25 2022. https://www2.ed.gov/about/offices/list/ocr/docs/20210608-impacts-of-covid19.pdf
  • Fong, C. J. (2022). Academic motivation in a pandemic context: a conceptual review of prominent theories and an integrative model. Educational Psychology. https://doi.org/10.1080/01443410.2022.2026891
  • Gabriele, L., Bertacchini, F., Tevernise, A., Vaca-Cardenas, L., Pantano, P., & Bilotta, E. (2019). Lesson Planning by Computational Thinking Skills in Italian Pre-service Teachers. Informatics in Education, 18(1), 69–104. https://doi.org/ 10.15388/infedu.2019.04
  • Glewwe, P., Huang, Q., & Park, A. (2017). Cognitive skills, noncognitive skills, and school-to-work transitions in rural China. Journal of Economic Behavior and Organization, 134, 141-164. https://doi.org/10.1016/j.jebo.2016.12.009
  • Hu, K., Godfrey, K., Ren, Q., Wang, S.,Yang, X., & Li, Q. (2022). The impact of the COVID-19 pandemic on college students in USA: Two years later. Psychiatry Research, 315, 114685. https://doi.org/10.1016/j.psychres.2022.114685
  • Hwang, M. H., Lim, H. J., & Ha, H. S. (2018). Efects of grit on the academic success of adult femalae students at Korean open university. Psychological Reports, 121(4), 705–725. https://doi.org/ 10.1177/0033294117734834
  • İlic, U. (2021). The impact of Scratch-assisted instruction on computational thinking (CT) skills of pre-service teachers. International Journal of Research in Education and Science (IJRES), 7(2), 426-444. https://doi.org/10.46328/ijres.1075
  • Jereb, E., Jerebic, J., and Uhu, M. (2022). Studying Habits in Higher Education Before and After the Outbreak of the COVID-19 Pandemic. Athens Journal of Education, 10 (1): 67–84. https://doi.org/10.30958/aje.10-1-4
  • Kerres, M., and Buchner, J. (2017). Education after the Pandemic: What We Have (Not) Learned about Learning. Education Sciences, 12, 315. https://doi.org/10.3390/educsci12050315
  • Korkmaz, Ö., Çakır, R., & Özden, M. Y. (2017). A validity and reliability study of the computational thinking scales(CTS). Computers in Human Behavior, 721, 558–569. https://doi.org/10.1016/j.chb.2017.01.005
  • Lazarinis, F., Karachristos, C. V., Stavropoulos, E., & Verykios, V. S. (2018). A blended learning course for playfully teaching programming concepts to school teachers. Education and Information Technologies. https://doi.org/ 10.1007/s10639-018-9823-2
  • Marcelino, M. J., Pessoa, T., Vieira, C., Salvador, T., & Mendes, A. J. (2018). Learning Computational Thinking and scratch at distance. Computers in Human Behavior, 80, 470–477. https://doi.org/10.1016/j.chb.2017.09.025
  • Mishra, P., & Yadav, A. (2013). Rethinking Technology & Creativity in the 21st Century. TechTrends, 57, 10–14.
  • Neroni, J., Meijs, C., Kirschner, P. A., Xu, K. M., & De Groot, R. (2022). Academic self‑efficacy, self‑esteem, and grit in higher online education: Consistency of interests predicts academic success. Social Psychology of Education, 25, 951-975. https://doi.org/ 10.1007/s11218-022-09696-5
  • Nichols, M. (2017) Can I choose to have grit? Non-cognitiveskills, behavior, and school choice. Journal of School Choice, 11(4), 622-641, https://doi.org/10.1080/15582159.2017.1395636
  • Nouri, J., Zhang, L., Mannila, L. and Noren, E. (2020), “Development of computational thinking, digital competence and 21st century skills when learning programming in K-9”, Education Inquiry, 11(1), 1-17. https://doi.org/10.1080/20004508.2019.1627844
  • Papert, S. (1980). Mindstorms: Children, computers and powerful ideas. New York: Basic Books.
  • Sarıçam, H., Çelik, İ., & Oğuz, A. (2016). Kısa Azim (Sebat) Ölçeğinin Türkçeye Uyarlanması- Geçerlik ve Güvenirlik Çalışması. Uluslararası Türkçe Edebiyat Kültür Eğitim Dergisi, 5(2), 927-935. Doi Number :http://dx.doi.org/10.7884/teke.622
  • Shirvsn, M. E., & Alamer, A. (2022). Modeling the interplay of EFL learners’ basic psychological needs, grit and L2 achievement. Journal of Multilingual and Multicultural Development. https://doi.org/ 10.1080/01434632.2022.2075002
  • Voogt, J., Fisser, P., Good, J., Mishra, P., & Yadav, A. (2015). Computational thinking in compulsory education: Towards an agenda for research and practice. Educ Inf Technol, 20, 715–728. https://doi.org/10.1007/s10639-015-9412-6
  • Weisskirch, R. S. (2018). Grit, self-esteem, learning strategies and attitudes and estimated and achieved course grades among college students. Curr Psychol, 37, 21–27. https://doi.org/ 10.1007/s12144-016-9485-4
  • Wing, J. M. (2006). Computational thinking. Communications of The ACM, 49(3), 33-35.
  • Wing, J. (2008). Computational thinking and thinking about computing. Phil. Trans. R. Soc. A, 366, 3717-3725. https://doi.org/ 10.1098/rsta.2008.0118.
  • Wolters, A. C., & Hussain, M. (2015). Investigating grit and its relations with college students’ self-regulated learning and academic achievement. Metacognition Learning, 10, 293–311. https://doi.org/ 10.1007/s11409-014-9128-9
  • Yadav, A., Stephenson, C., & Hong, H. (2017). Computational Thinking for Teacher Education. Communıcatıons Of The Acm, 60(4), 55-62. https://doi.org/ 10.1145/2994591
  • Zhao, X., Zhang, J., Li, W., Khan, K., Lu, Y., and Winters, N. 2021. Learners’ non-cognitive skills and behavioral patterns of programming: A sequential analysis. 2021 International Conference on Advanced Learning Technologies (ICALT), Tartu, July 12–15.
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Primary Language English
Subjects Other Fields of Education (Other)
Journal Section Research Articles
Authors

Taner Arabacıoglu 0000-0003-1116-1777

Early Pub Date April 25, 2024
Publication Date
Submission Date November 15, 2023
Acceptance Date January 8, 2024
Published in Issue Year 2024 Volume: 5 Issue: 1

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APA Arabacıoglu, T. (2024). Scratch, computational thinking, and grit: At the beginning, during, and after the COVID-19 Pandemic. Instructional Technology and Lifelong Learning, 5(1), 1-20. https://doi.org/10.52911/itall.1391292
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