Research Article
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Year 2019, Volume: 6 Issue: 1, 10 - 26, 01.06.2019
https://doi.org/10.17275/per.19.2.6.1

Abstract

References

  • Aggarwal A., Gardner -Mc Cune C. & Touretzky, D. S. (2017). Evaluating the Effect of Using Physical Manipulatives to Foster Computational Thinking in Elementary School, The 2017 ACM SIGCSE Technical Symposium on Computer Science Education. ACM,9-14.
  • Aksoy, B. (2004). Coğrafya öğretiminde probleme dayalı öğrenme yaklaşımı[The Problem-Based Learning Approach in Geography Teaching ]. (Unpublished master’s thesis). Gazi University, Institute of Education Sciences, Ankara.
  • Anderson, J. C. & Gerbing, D. W. (1984). The effect of sampling error on convergence, improper solutions, and goodness-offit indices for maximum likelihood confirmatory factor analysis. Psychometrika, 49, 155-173.
  • Balcı, A. (2009). Sosyal bilimlerde araştırma: Yöntem, teknik ve ilkeler [Research in social science: Methods, techniques and principles]. Ankara: PegemA Pub.
  • Barr, D., Harrison, J. & Conery, L (2011). Computational Thinking: A Dijital Age Skill for Everyone, Available at: http://files.eric.ed.gov/fulltext/EJ918910.pdf
  • Brennan K. & Resnick M. (2012). New Frameworks for Studying and Assessing the Development of Computational Thinking. The 2012Annual Meeting of the American Educational Research Association,1-25.
  • Brown, W. (2015). Introduction to Algorithmic Thinking. Available at: www.cs4fn.com/algoritmicthinking.php
  • Büyüköztürk, Ş. (2002). Sosyal bilimler için veri analizi el kitabı [Data analysis for social sciences hand book]. Ankara: PegemA Pub.
  • Carmines, E.G. & Zeller, R.A. (1982). Reliability and validity assessment. 5th ed. Beverly Hills: Sage Publications Inc.
  • Chen, G., Shen, J., Barth-Cohe, L., Jiang, S., Huang, X. & Eltoukhy, M. (2017). Assessing Elementary Students’ Computational Thinking in Everyday Reasoning and Robotics Programming. Computers & Education, 109: 162-175.
  • CSTA (2016). K-12 Computer Science Standards. [2018-06-26]. https//c.ymcdn.com/sites/www.csteachers.org/resource/resmgr/Docs/Standards/2016Standards Revision/INTERIM_Standards FINAL_07222.pdf.
  • CSTA and ISTE (2011). Computational Thinking in K-12 Education Lead-ership Toolkit. [2018-06-26].http ://csta.acm.org/Curriculum/sub/Curr Files/471.11CTLeadershipt Toolkit-SP-v F.pdf.
  • Deniz, K.Z. (2007). The adaptation of psychological scales. Ankara University, Journal of Faculty of Educational Sciences, 40(1), 1-16.
  • Eroğlu, A. (2008). Faktör analizi [Factor analyses]. In: Kalaycı, Ş. (ed), SPSS Uygulamalı Çok Değişkenli İstatistik Teknikleri [Statistics Techniques with Multi Variable in SPSS Applications], Ankara: Asil Publishers, 321-331.
  • Esteves, M., Fonseca B., Morgado L & et al. (2011). Improving Teaching and Learning of Computer Programming through the Use of the SecondLife Virtual World. British Journal of Educational Technology, 42(4): 624-637.
  • Field, A. (2000). Discovering Statistics using SPSS for Windows. London: Thousand Oaks- New Delhi: Sage Pub.
  • Gorsuch, R. L. (1983). Factor analysis. Hillsdale: Lawrence Erlbaum Associates.
  • Grover S. & Pea R. (2013). Computational thinking in K-12: A review of the state of the field. Educational Researcher, 42 (2), 59–69.
  • Gülbahar, Y. & Büyüköztürk, Ş. (2008). Adaptation of Assessment Preferences Inverntory to Turkish. H. U. Journal of Education, 35, 148-161.
  • Günüç, S. Odabaşı, F. & Kuzu A. (2013). 21. yüzyıl öğrenci özelliklerinin öğretmen adayları tarafından tanımlanması: Bir twitter uygulaması. The defining characteristics of students of the 21st century by student teachers: A twitter activity. journal of theory and practice in education, 9(4): 436-455.
  • Hambleton, R.K. & Patsula, L. (1999). Increasing the validity of adapted tests: myths to be avoided an guidelines for improving test adaptation practices. Journal of Applied Testing Technology, August Issue. Online: http://data.memberclicks.com/site/atpu/volume%201%20issue%201Increasing%20validity.pdf
  • Iste. (2015). CT Leadership toolkit. Available at http://www.iste.org/docs/ct-documents/ct-leadershipt-toolkit.pdf?sfvrsn=4.
  • Kline, P. (1994). An easy guide to factor analysis. London and New York: Routledge.
  • Kline, R.B. (2005). Principles and practice of structural equation modeling, 2nded, New York: Guilford Press.
  • Koh, K.H, Basawapatna, A., Bennett, V. & Repenning, A (2010). Towards the Automatic Recognition of Computational Thinking for Adaptive Visual Language Learning. Visual Languages and Human -Centric Computational thinking, 59-66.
  • Korkmaz, Ö. (2009). The influence of education faculties on students’ critical thinking level and disposition. Turkish Journal of Educationa Science 7(4):879-902.
  • Korkmaz, Ö., Çakır, R. & Özden, M. Y. (2015). Computational thinking levels scale (CTLS) adaptation for secondary school level. Gazi journal of education sciences, 1(2): 67-86.
  • Korkmaz, Ö., Çakır, R. & Özden, M.Y. (2017). A validity and reliability study of the Computational Thinking Scales (CTS). Computers in Human Behaviours. 72:558-569. (SSCI)
  • Korkmaz, Ö., Çakır, R., Özden, M. Y, Oluk & A., Sarıoğlu, S. (2015). Investigation of Individuals’ Computational Thinking Skills in terms of Different Variables, Ondokuz Mayis University Journal of Faculty of Education, 34(2): 68-87
  • Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12?. Computers in Human Behavior, 41, 51-61
  • MacCallum, R.C., Browne, M.W., & Sugawara, H.M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1, 130-149.
  • Marsh, H. W., Balla, J. R. & McDonald, R. P. (1988). Goodness-of-fit indexes in confirmatory factor analysis: The effect of sample size. Psychological Bulletin, 103, 391-410.
  • Ozden, M. Y. (2015). Computational thinking. http://myozden.blogspot.com.tr/2015/06/computational-thinking-bilgisayarca.html.
  • Pohlmann, J.T. (2004). Use and Interpretation of Factor Analysis in The Journal of Educational Research: 1992-2002. The Journal of Educational Research, 98(1), 14-23
  • Román -González, M., Pérez -González, J.C. & Jiménez -Fernández, C. (2016). Which Cognitive Abilities Underlie Computational Thinking? Criterion Validity of the Computational Thinking Test. Computers in Human Behavior, 72: 678-691.
  • Russell, D. W. (2002). In search of underlying dimensions: The use (and abuse) of factor analysis. Personality and Social Psychology Bulletin, 28, 1629–1646.
  • Scherer, R.F., Wiebe F.A., Luther, D. C. & Adams J. S. (1988). Dimensionality of coping: Factor stability using the ways of coping questionnaire, Psychological Reports 62(3), 763-770. PubMed PMID: 3406294.
  • Şimşek, Ö.F. (2007). Yapısal eşitlik modellemesine giriş [Introduction to structural equation modeling]. Ankara: Ekinoks Pub., 18-71.
  • Soylu, Y. & Soylu, C. (2006). The Role of Problem Solving In Mathematics Lessons For Success. Inönü University Educational Journal, 7(11), 97-111.
  • Sümer, N. (2000). Structural equation models: Basic concepts and sample applications. Turkish Psychology Articles, 3(6), 49-74.
  • Tabachnick, B. G. & Fidell, L.S. (2001). Using multivariate statistics (4th edition). Boston: Allyn and Bacon.
  • Tatlidil, H. (2002). Uygulamalı çok değişkenli istatistiksel analiz [Applied multivariate statistical analysis], Akademi Pub, Ankara
  • Voogt, J., Fisser, P., Good, J., Mishra, P., & Yadav, A. (2015). Computational thinking incompulsory education: Towards an agenda for research and practice. Education and Information Technologies, 20(4), 715-728.
  • Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49, 33-35.
  • Yukselturk, E. & Bulut, S. (2009). Gender differences in self-regulated online learning environment. Educational Technology & Society, 12(3), 12–22.

Adapting Computational Thinking Scale (CTS) for Chinese High School Students and Their Thinking Scale Skills Level

Year 2019, Volume: 6 Issue: 1, 10 - 26, 01.06.2019
https://doi.org/10.17275/per.19.2.6.1

Abstract

The
purpose of this study is to adapt the computational thinking scale to Chinese.
The study group consists of 1015 students. The study was performed in the descriptive
scanning model. The final version of the scale was corrected in line with the
opinions of the language experts who received the items translated from Turkish
to Chinese. Exploratory and confirmatory factor analyses were calculated to
determine the validity of the scale. Later, the distinctiveness forces were
calculated. To determine the reliability of the scale internal consistency and
stability levels were calculated. It has been concluded that the computational
thinking scale is a valid and reliable tool in Chinese culture that can be used
to determine high school students' computational thinking skills. In addition,
it was concluded that the students' computational thinking skills were quite
high. In terms of factors, the students' highest level skills are “Creativity”
and the lowest ones are “Problem Solving” and “Algorithmic Thinking”. In terms
of total scores and factors, computational thinking skills of male students are
higher than female students. But problem solving skills are similar. It was
concluded that k10 students' computational thinking skills were higher than k11
students in terms of “Problem Solving”, “Critical Thinking” and total scores.
Based on the results obtained from this research and the literature, it is
recommended that students frequently take part in activities that aim to
improve their Problem Solving and Algorithmic Thinking skills, especially
within the context of different courses.

References

  • Aggarwal A., Gardner -Mc Cune C. & Touretzky, D. S. (2017). Evaluating the Effect of Using Physical Manipulatives to Foster Computational Thinking in Elementary School, The 2017 ACM SIGCSE Technical Symposium on Computer Science Education. ACM,9-14.
  • Aksoy, B. (2004). Coğrafya öğretiminde probleme dayalı öğrenme yaklaşımı[The Problem-Based Learning Approach in Geography Teaching ]. (Unpublished master’s thesis). Gazi University, Institute of Education Sciences, Ankara.
  • Anderson, J. C. & Gerbing, D. W. (1984). The effect of sampling error on convergence, improper solutions, and goodness-offit indices for maximum likelihood confirmatory factor analysis. Psychometrika, 49, 155-173.
  • Balcı, A. (2009). Sosyal bilimlerde araştırma: Yöntem, teknik ve ilkeler [Research in social science: Methods, techniques and principles]. Ankara: PegemA Pub.
  • Barr, D., Harrison, J. & Conery, L (2011). Computational Thinking: A Dijital Age Skill for Everyone, Available at: http://files.eric.ed.gov/fulltext/EJ918910.pdf
  • Brennan K. & Resnick M. (2012). New Frameworks for Studying and Assessing the Development of Computational Thinking. The 2012Annual Meeting of the American Educational Research Association,1-25.
  • Brown, W. (2015). Introduction to Algorithmic Thinking. Available at: www.cs4fn.com/algoritmicthinking.php
  • Büyüköztürk, Ş. (2002). Sosyal bilimler için veri analizi el kitabı [Data analysis for social sciences hand book]. Ankara: PegemA Pub.
  • Carmines, E.G. & Zeller, R.A. (1982). Reliability and validity assessment. 5th ed. Beverly Hills: Sage Publications Inc.
  • Chen, G., Shen, J., Barth-Cohe, L., Jiang, S., Huang, X. & Eltoukhy, M. (2017). Assessing Elementary Students’ Computational Thinking in Everyday Reasoning and Robotics Programming. Computers & Education, 109: 162-175.
  • CSTA (2016). K-12 Computer Science Standards. [2018-06-26]. https//c.ymcdn.com/sites/www.csteachers.org/resource/resmgr/Docs/Standards/2016Standards Revision/INTERIM_Standards FINAL_07222.pdf.
  • CSTA and ISTE (2011). Computational Thinking in K-12 Education Lead-ership Toolkit. [2018-06-26].http ://csta.acm.org/Curriculum/sub/Curr Files/471.11CTLeadershipt Toolkit-SP-v F.pdf.
  • Deniz, K.Z. (2007). The adaptation of psychological scales. Ankara University, Journal of Faculty of Educational Sciences, 40(1), 1-16.
  • Eroğlu, A. (2008). Faktör analizi [Factor analyses]. In: Kalaycı, Ş. (ed), SPSS Uygulamalı Çok Değişkenli İstatistik Teknikleri [Statistics Techniques with Multi Variable in SPSS Applications], Ankara: Asil Publishers, 321-331.
  • Esteves, M., Fonseca B., Morgado L & et al. (2011). Improving Teaching and Learning of Computer Programming through the Use of the SecondLife Virtual World. British Journal of Educational Technology, 42(4): 624-637.
  • Field, A. (2000). Discovering Statistics using SPSS for Windows. London: Thousand Oaks- New Delhi: Sage Pub.
  • Gorsuch, R. L. (1983). Factor analysis. Hillsdale: Lawrence Erlbaum Associates.
  • Grover S. & Pea R. (2013). Computational thinking in K-12: A review of the state of the field. Educational Researcher, 42 (2), 59–69.
  • Gülbahar, Y. & Büyüköztürk, Ş. (2008). Adaptation of Assessment Preferences Inverntory to Turkish. H. U. Journal of Education, 35, 148-161.
  • Günüç, S. Odabaşı, F. & Kuzu A. (2013). 21. yüzyıl öğrenci özelliklerinin öğretmen adayları tarafından tanımlanması: Bir twitter uygulaması. The defining characteristics of students of the 21st century by student teachers: A twitter activity. journal of theory and practice in education, 9(4): 436-455.
  • Hambleton, R.K. & Patsula, L. (1999). Increasing the validity of adapted tests: myths to be avoided an guidelines for improving test adaptation practices. Journal of Applied Testing Technology, August Issue. Online: http://data.memberclicks.com/site/atpu/volume%201%20issue%201Increasing%20validity.pdf
  • Iste. (2015). CT Leadership toolkit. Available at http://www.iste.org/docs/ct-documents/ct-leadershipt-toolkit.pdf?sfvrsn=4.
  • Kline, P. (1994). An easy guide to factor analysis. London and New York: Routledge.
  • Kline, R.B. (2005). Principles and practice of structural equation modeling, 2nded, New York: Guilford Press.
  • Koh, K.H, Basawapatna, A., Bennett, V. & Repenning, A (2010). Towards the Automatic Recognition of Computational Thinking for Adaptive Visual Language Learning. Visual Languages and Human -Centric Computational thinking, 59-66.
  • Korkmaz, Ö. (2009). The influence of education faculties on students’ critical thinking level and disposition. Turkish Journal of Educationa Science 7(4):879-902.
  • Korkmaz, Ö., Çakır, R. & Özden, M. Y. (2015). Computational thinking levels scale (CTLS) adaptation for secondary school level. Gazi journal of education sciences, 1(2): 67-86.
  • Korkmaz, Ö., Çakır, R. & Özden, M.Y. (2017). A validity and reliability study of the Computational Thinking Scales (CTS). Computers in Human Behaviours. 72:558-569. (SSCI)
  • Korkmaz, Ö., Çakır, R., Özden, M. Y, Oluk & A., Sarıoğlu, S. (2015). Investigation of Individuals’ Computational Thinking Skills in terms of Different Variables, Ondokuz Mayis University Journal of Faculty of Education, 34(2): 68-87
  • Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12?. Computers in Human Behavior, 41, 51-61
  • MacCallum, R.C., Browne, M.W., & Sugawara, H.M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1, 130-149.
  • Marsh, H. W., Balla, J. R. & McDonald, R. P. (1988). Goodness-of-fit indexes in confirmatory factor analysis: The effect of sample size. Psychological Bulletin, 103, 391-410.
  • Ozden, M. Y. (2015). Computational thinking. http://myozden.blogspot.com.tr/2015/06/computational-thinking-bilgisayarca.html.
  • Pohlmann, J.T. (2004). Use and Interpretation of Factor Analysis in The Journal of Educational Research: 1992-2002. The Journal of Educational Research, 98(1), 14-23
  • Román -González, M., Pérez -González, J.C. & Jiménez -Fernández, C. (2016). Which Cognitive Abilities Underlie Computational Thinking? Criterion Validity of the Computational Thinking Test. Computers in Human Behavior, 72: 678-691.
  • Russell, D. W. (2002). In search of underlying dimensions: The use (and abuse) of factor analysis. Personality and Social Psychology Bulletin, 28, 1629–1646.
  • Scherer, R.F., Wiebe F.A., Luther, D. C. & Adams J. S. (1988). Dimensionality of coping: Factor stability using the ways of coping questionnaire, Psychological Reports 62(3), 763-770. PubMed PMID: 3406294.
  • Şimşek, Ö.F. (2007). Yapısal eşitlik modellemesine giriş [Introduction to structural equation modeling]. Ankara: Ekinoks Pub., 18-71.
  • Soylu, Y. & Soylu, C. (2006). The Role of Problem Solving In Mathematics Lessons For Success. Inönü University Educational Journal, 7(11), 97-111.
  • Sümer, N. (2000). Structural equation models: Basic concepts and sample applications. Turkish Psychology Articles, 3(6), 49-74.
  • Tabachnick, B. G. & Fidell, L.S. (2001). Using multivariate statistics (4th edition). Boston: Allyn and Bacon.
  • Tatlidil, H. (2002). Uygulamalı çok değişkenli istatistiksel analiz [Applied multivariate statistical analysis], Akademi Pub, Ankara
  • Voogt, J., Fisser, P., Good, J., Mishra, P., & Yadav, A. (2015). Computational thinking incompulsory education: Towards an agenda for research and practice. Education and Information Technologies, 20(4), 715-728.
  • Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49, 33-35.
  • Yukselturk, E. & Bulut, S. (2009). Gender differences in self-regulated online learning environment. Educational Technology & Society, 12(3), 12–22.
There are 45 citations in total.

Details

Primary Language English
Subjects Studies on Education
Journal Section Research Articles
Authors

Özgen Korkmaz 0000-0003-4359-5692

Xuemei Bai This is me

Publication Date June 1, 2019
Acceptance Date March 7, 2019
Published in Issue Year 2019 Volume: 6 Issue: 1

Cite

APA Korkmaz, Ö., & Bai, X. (2019). Adapting Computational Thinking Scale (CTS) for Chinese High School Students and Their Thinking Scale Skills Level. Participatory Educational Research, 6(1), 10-26. https://doi.org/10.17275/per.19.2.6.1

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