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Cross-Country Variation in (Binary) Gender Differences in Secondary School Students’ CS Attitudes: Re-Validating and Generalizing a CS Attitudes Scale

Published:11 December 2023Publication History
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Abstract

The current study compared American, Korean, and Indonesian middle and high school students’ CS attitudes. Concurrently, this study also examined whether the items in the CS attitudes scale exhibit country and gender measurement biases. We gathered data on CS attitudes from middle and high school students in the US, Korea, and Indonesia. The participating students took the same (translated) previously validated CS attitudes scale. We ran a unidimensional IRT, differential item functioning (DIF), a two-way ANOVA, and the Kruskal-Wallis H test. Despite the valid instrument, we found it inappropriate as is for international comparison studies because students from different countries interpreted some items differently. We then compared gender-based differences in CS attitudes across countries. The results revealed no significant differences between males and females in the Indonesian middle school data, whereas male students had significantly higher CS attitudes than female students in both American and Korean student data. Furthermore, we found the same pattern in gender differences in Korean and Indonesian high school students’ CS attitudes scores as in the middle school study. These findings underscore the importance of a country’s sociocultural context in influencing gap and diversity in secondary school students’ CS attitudes.

REFERENCES

  1. [1] Accenture and Code Girls Who. 2020. Cracking the gender code: Get 3X more women in computing. https://www.accenture.com/_acnmedia/PDF-150/Accenture-Cracking-The-Gender-Code-Report.pdf#zoom=50Google ScholarGoogle Scholar
  2. [2] Association American Educational Research, Association American Psychological, Education National Council on Measurement in, et al. 2014. Standards for Educational and Psychological Testing. American Educational Research Association, Washington D.C.Google ScholarGoogle Scholar
  3. [3] Association Computer Science Teacher. 2016. CSTA CS Standards. https://csteachers.org/page/standardsGoogle ScholarGoogle Scholar
  4. [4] Benton Laura, Saunders Piers, Kalas Ivan, Hoyles Celia, and Noss Richard. 2018. Designing for learning mathematics through programming: A case study of pupils engaging with place value. International Journal of Child-computer Interaction 16 (2018), 6876.Google ScholarGoogle ScholarCross RefCross Ref
  5. [5] Boone William J., Staver John R., and Yale Melissa S.. 2013. Rasch Analysis in the Human Sciences. Springer, Dordrecht.Google ScholarGoogle Scholar
  6. [6] Budiansyah Arif. 2020. Nadiem Usung Computational Thinking Jadi Kurikulum, Apa Itu? [Nadiem stretches computational thinking to become a curriculum, what is it?]. https://www.cnbcindonesia.com/tech/20200218151009-37-138726/nadiem-usung-computational-thinking-jadi-kurikulum-apa-ituGoogle ScholarGoogle Scholar
  7. [7] Campbell D., Brislin R., Stewart V., and Werner O.. 1970. Back-translation and other translation techniques in cross-cultural research. International Journal of Psychology 30 (1970), 681692.Google ScholarGoogle Scholar
  8. [8] Catsambis Sophia. 2005. The Gender Gap in Mathematics: Merely a Step Function?Cambridge University Press, Cambridge.Google ScholarGoogle Scholar
  9. [9] Chen Dandan. 2009. Vocational Schooling, Labor Market Outcomes, and College Entry. Technical Report 4814. The World Bank.Google ScholarGoogle ScholarCross RefCross Ref
  10. [10] Cheryan Sapna and Markus Hazel Rose. 2020. Masculine defaults: Identifying and mitigating hidden cultural biases. Psychological Review 127, 6 (2020), 1022.Google ScholarGoogle ScholarCross RefCross Ref
  11. [11] Cheryan Sapna, Master Allison, and Meltzoff Andrew N. 2015. Cultural stereotypes as gatekeepers: Increasing girls’ interest in computer science and engineering by diversifying stereotypes. Frontiers in Psychology 49, 1 (2015), 49.Google ScholarGoogle Scholar
  12. [12] Cho Jungrae and Lim Sukja. 2015. The scheme of education for gender diversity in computer engineering education. The Journal of Korean Association of Computer Education 18, 1 (2015), 1320.Google ScholarGoogle Scholar
  13. [13] Choi Hyojeong and Kim Minso. 2022. I can’t get a job without a double major in computer science”... Humanities students learning coding to find a job. https://biz.chosun.com/topics/topics_social/2022/06/14/DWYTDAJSEVEDPLAA72IIFQ25AM/Google ScholarGoogle Scholar
  14. [14] Choi Seung W, Gibbons Laura E, and Crane Paul K. 2011. Lordif: An R package for detecting differential item functioning using iterative hybrid ordinal logistic regression/item response theory and Monte Carlo simulations. Journal of Statistical Software 39, 8 (2011), 1.Google ScholarGoogle ScholarCross RefCross Ref
  15. [15] Code.org. 2022. 2022 State of Computer Science Education: Understanding Our National Imperative. https://advocacy.code.org/2022_state_of_cs.pdfGoogle ScholarGoogle Scholar
  16. [16] Council National Research. 2014. Building Infrastructure for International Collaborative Research in the Social and Behavioral Sciences: Summary of a Workshop. Washington, DC: The National Academies Press. Google ScholarGoogle ScholarCross RefCross Ref
  17. [17] DeVellis Robert F.. 2016. Scale Development: Theory and Applications. Vol. 26. Sage Publications, Thousand Oaks, California.Google ScholarGoogle Scholar
  18. [18] Feist Gregory J.. 1998. A meta-analysis of personality in scientific and artistic creativity. Personality and Social Psychology Review 2, 4 (1998), 290309.Google ScholarGoogle ScholarCross RefCross Ref
  19. [19] Co-operation Organisation for Economic and Development. 2021. Education at a Glance Database. http://stats.oecd.orgGoogle ScholarGoogle Scholar
  20. Education] Lembaga Pengelola Dana Pendidikan[Indonesia Endowment Fund for. 2022. About Beasiswa LPDP. https://lpdp.kemenkeu.go.id/en/Google ScholarGoogle Scholar
  21. [21] Framework K–12 Computer Science. 2016. K–12 Computer Science Framework. http://www.k12cs.orgGoogle ScholarGoogle Scholar
  22. [22] Gallup and Google Code with. 2020. Current Perspectives and Continuing Challenges in Computer Science Education in U.S. K-12 Schools. https://services.google.com/fh/files/misc/computer-science-education-in-us-k12schools-2020-report.pdfGoogle ScholarGoogle Scholar
  23. [23] Hampson Sasha. 2020. Rhetoric or reality? Contesting definitions of women in Korea. In Women in Asia. Routledge, England, 170187.Google ScholarGoogle Scholar
  24. [24] Heine Steven J., Lehman Darrin R., Peng Kaiping, and Greenholtz Joe. 2002. What’s wrong with cross-cultural comparisons of subjective Likert scales?: The reference-group effect. Journal of Personality and Social Psychology 82, 6 (2002), 903.Google ScholarGoogle ScholarCross RefCross Ref
  25. [25] Henrich Joseph, Heine Steven J., and Norenzayan Ara. 2010. Most people are not WEIRD. Nature 466, 7302 (2010), 2929.Google ScholarGoogle ScholarCross RefCross Ref
  26. [26] Hinckle Madeline, Rachmatullah Arif, Mott Bradford, Boyer Kristy Elizabeth, Lester James, and Wiebe Eric. 2020. The relationship of gender, experiential, and psychological factors to achievement in computer science. In Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education. ACM, Trondheim, Norway, 225231.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. [27] Holland Paul W. and Wainer Howard. 2012. Differential Item Functioning. Routledge, New York.Google ScholarGoogle ScholarCross RefCross Ref
  28. [28] Kim Hyomin, Cho Youngju, Kim Sungeun, and Kim Hye-Suk. 2018. Women and men in computer science: Geeky proclivities, college rank, and gender in Korea. East Asian Science, Technology and Society: An International Journal 12, 1 (2018), 3356.Google ScholarGoogle ScholarCross RefCross Ref
  29. [29] Koo Y. Y., Park D. H., Kim J. J., Park Y. H., K.Ko C., and Lee B. K.. 2019. Meta-analysis of course selection data of the university graduates revealed the problems of course structures. Korean J. Gen. Educ. 13 (2019), 369396.Google ScholarGoogle Scholar
  30. [30] Kukul Volkan, Gökçearslan Şahin, and Günbatar Mustafa Serkan. 2017. Computer programming self-efficacy scale (CPSES) for secondary school students: Development, validation and reliability. Eğitim Teknolojisi Kuram ve Uygulama 7, 1 (2017), 158179.Google ScholarGoogle ScholarCross RefCross Ref
  31. [31] Lee ChangKwon, Jo Jaechoon, and Kim HyeonCheol. 2019. A study on gender difference of SW recognition by middle school students. The Journal of Korean Association of Computer Education 22, 1 (2019), 1120.Google ScholarGoogle Scholar
  32. [32] Lee Eunsang. 2017. Effects of South Korean high school students’ motivation to learn science and technology on their concern related to engineering. Educational Sciences: Theory & Practice 17, 2 (2017), 549571.Google ScholarGoogle Scholar
  33. [33] Lee Hyonyong, Ham Hyungin, and Kwon Hyuksoo. 2020. Research trends of integrative technology education in South Korea: A literature review of journal papers. International Journal of Technology and Design Education 32, 1 (2020), 114.Google ScholarGoogle Scholar
  34. [34] Lee Jeong-Kyu. 2006. Educational fever and South Korean higher education. Revista electrónica de investigación educativa 8, 1 (2006), 114.Google ScholarGoogle Scholar
  35. [35] LLC Google and Gallup Inc.. 2016. Trends in the State of Computer Science in U.S. K-12 Schools. http://services.google.com/fh/files/misc/trends-in-the-state-of-computer-science-report.pdfGoogle ScholarGoogle Scholar
  36. [36] Margulieux Lauren, Ketenci Tuba Ayer, and Decker Adrienne. 2019. Review of measurements used in computing education research and suggestions for increasing standardization. Computer Science Education 29, 1 (2019), 4978.Google ScholarGoogle ScholarCross RefCross Ref
  37. [37] Martin Michael, Mullis Ina, Foy Pierre, and Hooper Martin. 2016. TIMSS 2015 Internation Results in Science. https://timssandpirls.bc.edu/timss2015/international-results/wp-content/uploads/filebase/full%20pdfs/T15-International-Results-in-Science.pdfGoogle ScholarGoogle Scholar
  38. [38] McCartney Robert, Boustedt Jonas, Eckerdal Anna, Sanders Kate, and Zander Carol. 2017. Folk pedagogy and the geek gene: Geekiness quotient. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education. ACM, New York City, 405410.Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. [39] Messick Samuel. 1995. Validity of psychological assessment: Validation of inferences from persons’ responses and performances as scientific inquiry into score meaning. American Psychologist 50, 9 (1995), 741.Google ScholarGoogle ScholarCross RefCross Ref
  40. [40] Miller Joan G., Das Rekha, and Chakravarthy Sharmista. 2011. Culture and the role of choice in agency. Journal of Personality and Social Psychology 101, 1 (2011), 46.Google ScholarGoogle ScholarCross RefCross Ref
  41. [41] Mirjana Ivanović, Zoran Putnik, Anja Šišarica, and Zoran Budimac. 2010. A note on performance and satisfaction of female students studying computer science. Innovation in Teaching and Learning in Information and Computer Sciences 9, 1 (2010), 3241.Google ScholarGoogle ScholarCross RefCross Ref
  42. [42] Nisbett Richard E. and Miyamoto Yuri. 2005. The influence of culture: Holistic versus analytic perception. Trends in Cognitive Sciences 9, 10 (2005), 467473.Google ScholarGoogle ScholarCross RefCross Ref
  43. [43] Education Ministry of and Institute Korean Educational Development. 2021. Statistical Yearbook of Education. Ministry of Education. https://kess.kedi.re.kr/eng/publ/publFile/pdfjs?survSeq=2021&menuSeq=3894&publSeq=2&menuCd=90153&itemCode=02&menuId=2_16_4&language=enGoogle ScholarGoogle Scholar
  44. [44] Statistics U.S. Bureau of Labor. 2022. Occupational Outlook Handbook: Computer and Information Technology Occupations. https://www.bls.gov/ooh/computer-and-information-technology/home.htmGoogle ScholarGoogle Scholar
  45. [45] Science Committee on STEM Education of the National and Council Technogy. 2018. Charting a Course for Succcess: America’s Strategy for STEM Education. https://www.energy.gov/sites/default/files/2019/05/f62/STEM-Education-Strategic-Plan-2018.pdfGoogle ScholarGoogle Scholar
  46. [46] Osterlind Steven J. and Everson Howard T.. 2009. Differential Item Functioning. Sage Publications, Thousand Oaks, California.Google ScholarGoogle ScholarCross RefCross Ref
  47. [47] Papastergiou Marina. 2008. Are computer science and information technology still masculine fields? High school students’ perceptions and career choices. Computers & Education 51, 2 (2008), 594608.Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. [48] Pektas Emrah and Sullivan Florence. 2021. Storytelling through programming in Scratch: Interdisciplinary integration in the elementary English language arts classroom. In Proceedings of the 5th Asia Pacific Society for Computers in Education International Conference on Computational Thinking and STEM Education,. APSCE, Taiwan, 15.Google ScholarGoogle Scholar
  49. [49] Rachmatullah Arif, Roshayanti Fenny, Shin Sein, Lee Jun-Ki, and Ha Minsu. 2018. The secondary-student science learning motivation in Korea and Indonesia. EURASIA Journal of Mathematics, Science and Technology Education 14, 7 (2018), 31233141.Google ScholarGoogle Scholar
  50. [50] Rachmatullah Arif, Vandenberg Jessica, and Wiebe Eric. 2022. Toward more generalizable CS and CT instruments: Examining the interaction of country and gender at the middle grades level. In Proceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 1. ACM, New York City, 179185.Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. [51] Rachmatullah Arif, Wiebe Eric, Boulden Danielle, Mott Bradford, Boyer Kristy, and Lester James. 2020. Development and validation of the Computer Science Attitudes Scale for middle school students (MG-CS attitudes). Computers in Human Behavior Reports 2 (2020), 100018.Google ScholarGoogle ScholarCross RefCross Ref
  52. [52] Rachmatullah Arif and Wiebe Eric N.. 2022. Building a computational model of food webs: Impacts on middle school students’ computational and systems thinking skills. Journal of Research in Science Teaching 59, 4 (2022), 585618.Google ScholarGoogle ScholarCross RefCross Ref
  53. [53] Rachmatullah Arif and Wiebe Eric N.. 2022. Exploring middle school students’ interests in computationally intensive science careers: The CISCI instrument validation and intervention. Science Education 107, 2 (2022), 333367.Google ScholarGoogle ScholarCross RefCross Ref
  54. [54] Riemer Hila, Shavitt Sharon, Koo Minkyung, and Markus Hazel Rose. 2014. Preferences don’t have to be personal: Expanding attitude theorizing with a cross-cultural perspective. Psychological Review 121, 4 (2014), 619.Google ScholarGoogle ScholarCross RefCross Ref
  55. [55] Robitzsch Alexander and Steinfeld Jan. 2018. Item response models for human ratings: Overview, estimation methods, and implementation in R. Psychological Test and Assessment Modeling 60, 1 (2018), 101138.Google ScholarGoogle Scholar
  56. [56] Ryu Kiung and Cervero Ronald M.. 2011. The role of Confucian cultural values and politics in planning educational programs for adults in Korea. Adult Education Quarterly 61, 2 (2011), 139160.Google ScholarGoogle ScholarCross RefCross Ref
  57. [57] Salmon Aliénor. 2015. A Complex Formula: Girls and Women in Science, Technology, Engineering and Mathematics in Asia.UNESCO, Paris.Google ScholarGoogle Scholar
  58. [58] Sheffield Rachel Sarah, Koul Rekha, Blackley Susan, Fitriani Ella, Rahmawati Yuli, and Resek Diane. 2018. Transnational examination of STEM education. International Journal of Innovation in Science and Mathematics Education 26, 8 (2018), 6780.Google ScholarGoogle Scholar
  59. [59] Shin Jongho, Lee Hyunjoo, McCarthy-Donovan Alexander, Hwang Hyeyoung, Yim Sonyoung, and Seo EunJin. 2015. Home and motivational factors related to science-career pursuit: Gender differences and gender similarities. International Journal of Science Education 37, 9 (2015), 14781503.Google ScholarGoogle ScholarCross RefCross Ref
  60. [60] Shin Sein, Rachmatullah Arif, Roshayanti Fenny, Ha Minsu, and Lee Jun-Ki. 2018. Career motivation of secondary students in STEM: A cross-cultural study between Korea and Indonesia. International Journal for Educational and Vocational Guidance 18, 2 (2018), 203231.Google ScholarGoogle ScholarCross RefCross Ref
  61. [61] Yalcinkaya Nur Soylu and Adams Glenn. 2020. A cultural psychological model of cross-national variation in gender gaps in STEM participation. Personality and Social Psychology Review 24, 4 (2020), 345370.Google ScholarGoogle ScholarCross RefCross Ref
  62. [62] Unfried Alana, Faber Malinda, Stanhope Daniel S., and Wiebe Eric. 2015. The development and validation of a measure of student attitudes toward science, technology, engineering, and math (S-STEM). Journal of Psychoeducational Assessment 33, 7 (2015), 622639.Google ScholarGoogle ScholarCross RefCross Ref
  63. [63] Wagner Isabel. 2016. Gender and performance in computer science. ACM Transactions on Computing Education (TOCE) 16, 3 (2016), 116.Google ScholarGoogle ScholarDigital LibraryDigital Library
  64. [64] Webb David C. and Miller Susan B.. 2015. Gender analysis of a large scale survey of middle grades students’ conceptions of computer science education. In Proceedings of the 3rd Conference on GenderIT. ACM, Philadelphia, PA, 18.Google ScholarGoogle ScholarDigital LibraryDigital Library
  65. [65] Week EU Code. 2022. Europe Code Week. https://codeweek.eu/Google ScholarGoogle Scholar
  66. [66] Wiebe Eric, Unfried Alana, and Faber Malinda. 2018. The relationship of STEM attitudes and career interest. EURASIA Journal of Mathematics, Science and Technology Education 14, 10 (2018), 117.Google ScholarGoogle ScholarCross RefCross Ref
  67. [67] Wigfield Allan and Eccles Jacquelynne S. 2000. Expectancy–value theory of achievement motivation. Contemporary Educational Psychology 25, 1 (2000), 6881.Google ScholarGoogle ScholarCross RefCross Ref
  68. [68] Wright Benjamin and Linacre J. M.. 1994. Reasonable mean-square fit values. Rasch Meas Trans 8 (1994), 370.Google ScholarGoogle Scholar
  69. [69] Zilberman Alan and Ice Lindsey. 2021. Why computer occupations are behind strong STEM employment growth in the 2019–29 decade. https://www.bls.gov/opub/btn/volume-10/why-computer-occupations-are-behind-strong-stem-employment-growth.htmlGoogle ScholarGoogle Scholar

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  1. Cross-Country Variation in (Binary) Gender Differences in Secondary School Students’ CS Attitudes: Re-Validating and Generalizing a CS Attitudes Scale

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          cover image ACM Transactions on Computing Education
          ACM Transactions on Computing Education  Volume 23, Issue 4
          December 2023
          213 pages
          EISSN:1946-6226
          DOI:10.1145/3631944
          • Editor:
          • Amy J. Ko
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          Publication History

          • Published: 11 December 2023
          • Online AM: 23 October 2023
          • Accepted: 12 October 2023
          • Received: 9 June 2023
          Published in toce Volume 23, Issue 4

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