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.
- [1] . 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 Scholar
- [2] . 2014. Standards for Educational and Psychological Testing. American Educational Research Association, Washington D.C.Google Scholar
- [3] . 2016. CSTA CS Standards. https://csteachers.org/page/standardsGoogle Scholar
- [4] . 2018. Designing for learning mathematics through programming: A case study of pupils engaging with place value. International Journal of Child-computer Interaction 16 (2018), 68–76.Google ScholarCross Ref
- [5] . 2013. Rasch Analysis in the Human Sciences. Springer, Dordrecht.Google Scholar
- [6] . 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 Scholar
- [7] . 1970. Back-translation and other translation techniques in cross-cultural research. International Journal of Psychology 30 (1970), 681–692.Google Scholar
- [8] . 2005. The Gender Gap in Mathematics: Merely a Step Function?Cambridge University Press, Cambridge.Google Scholar
- [9] . 2009. Vocational Schooling, Labor Market Outcomes, and College Entry.
Technical Report 4814. The World Bank.Google ScholarCross Ref - [10] . 2020. Masculine defaults: Identifying and mitigating hidden cultural biases. Psychological Review 127, 6 (2020), 1022.Google ScholarCross Ref
- [11] . 2015. Cultural stereotypes as gatekeepers: Increasing girls’ interest in computer science and engineering by diversifying stereotypes. Frontiers in Psychology 49, 1 (2015), 49.Google Scholar
- [12] . 2015. The scheme of education for gender diversity in computer engineering education. The Journal of Korean Association of Computer Education 18, 1 (2015), 13–20.Google Scholar
- [13] . 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 Scholar
- [14] . 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 ScholarCross Ref
- [15] . 2022. 2022 State of Computer Science Education: Understanding Our National Imperative. https://advocacy.code.org/2022_state_of_cs.pdfGoogle Scholar
- [16] . 2014. Building Infrastructure for International Collaborative Research in the Social and Behavioral Sciences: Summary of a Workshop. Washington, DC: The National Academies Press. Google ScholarCross Ref
- [17] . 2016. Scale Development: Theory and Applications. Vol. 26. Sage Publications, Thousand Oaks, California.Google Scholar
- [18] . 1998. A meta-analysis of personality in scientific and artistic creativity. Personality and Social Psychology Review 2, 4 (1998), 290–309.Google ScholarCross Ref
- [19] . 2021. Education at a Glance Database. http://stats.oecd.orgGoogle Scholar
- 2022. About Beasiswa LPDP. https://lpdp.kemenkeu.go.id/en/Google Scholar .
- [21] . 2016. K–12 Computer Science Framework. http://www.k12cs.orgGoogle Scholar
- [22] . 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 Scholar
- [23] . 2020. Rhetoric or reality? Contesting definitions of women in Korea. In Women in Asia. Routledge, England, 170–187.Google Scholar
- [24] . 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 ScholarCross Ref
- [25] . 2010. Most people are not WEIRD. Nature 466, 7302 (2010), 29–29.Google ScholarCross Ref
- [26] . 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, 225–231.Google ScholarDigital Library
- [27] . 2012. Differential Item Functioning. Routledge, New York.Google ScholarCross Ref
- [28] . 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), 33–56.Google ScholarCross Ref
- [29] . 2019. Meta-analysis of course selection data of the university graduates revealed the problems of course structures. Korean J. Gen. Educ. 13 (2019), 369–396.Google Scholar
- [30] . 2017. Computer programming self-efficacy scale (CPSES) for secondary school students: Development, validation and reliability. Eğitim Teknolojisi Kuram ve Uygulama 7, 1 (2017), 158–179.Google ScholarCross Ref
- [31] . 2019. A study on gender difference of SW recognition by middle school students. The Journal of Korean Association of Computer Education 22, 1 (2019), 11–20.Google Scholar
- [32] . 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), 549–571.Google Scholar
- [33] . 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), 1–14.Google Scholar
- [34] . 2006. Educational fever and South Korean higher education. Revista electrónica de investigación educativa 8, 1 (2006), 1–14.Google Scholar
- [35] . 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 Scholar
- [36] . 2019. Review of measurements used in computing education research and suggestions for increasing standardization. Computer Science Education 29, 1 (2019), 49–78.Google ScholarCross Ref
- [37] . 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 Scholar
- [38] . 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, 405–410.Google ScholarDigital Library
- [39] . 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 ScholarCross Ref
- [40] . 2011. Culture and the role of choice in agency. Journal of Personality and Social Psychology 101, 1 (2011), 46.Google ScholarCross Ref
- [41] . 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), 32–41.Google ScholarCross Ref
- [42] . 2005. The influence of culture: Holistic versus analytic perception. Trends in Cognitive Sciences 9, 10 (2005), 467–473.Google ScholarCross Ref
- [43] . 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 Scholar
- [44] . 2022. Occupational Outlook Handbook: Computer and Information Technology Occupations. https://www.bls.gov/ooh/computer-and-information-technology/home.htmGoogle Scholar
- [45] . 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 Scholar
- [46] . 2009. Differential Item Functioning. Sage Publications, Thousand Oaks, California.Google ScholarCross Ref
- [47] . 2008. Are computer science and information technology still masculine fields? High school students’ perceptions and career choices. Computers & Education 51, 2 (2008), 594–608.Google ScholarDigital Library
- [48] . 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, 1–5.Google Scholar
- [49] . 2018. The secondary-student science learning motivation in Korea and Indonesia. EURASIA Journal of Mathematics, Science and Technology Education 14, 7 (2018), 3123–3141.Google Scholar
- [50] . 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, 179–185.Google ScholarDigital Library
- [51] . 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 ScholarCross Ref
- [52] . 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), 585–618.Google ScholarCross Ref
- [53] . 2022. Exploring middle school students’ interests in computationally intensive science careers: The CISCI instrument validation and intervention. Science Education 107, 2 (2022), 333–367.Google ScholarCross Ref
- [54] . 2014. Preferences don’t have to be personal: Expanding attitude theorizing with a cross-cultural perspective. Psychological Review 121, 4 (2014), 619.Google ScholarCross Ref
- [55] . 2018. Item response models for human ratings: Overview, estimation methods, and implementation in R. Psychological Test and Assessment Modeling 60, 1 (2018), 101–138.Google Scholar
- [56] . 2011. The role of Confucian cultural values and politics in planning educational programs for adults in Korea. Adult Education Quarterly 61, 2 (2011), 139–160.Google ScholarCross Ref
- [57] . 2015. A Complex Formula: Girls and Women in Science, Technology, Engineering and Mathematics in Asia.UNESCO, Paris.Google Scholar
- [58] . 2018. Transnational examination of STEM education. International Journal of Innovation in Science and Mathematics Education 26, 8 (2018), 67–80.Google Scholar
- [59] . 2015. Home and motivational factors related to science-career pursuit: Gender differences and gender similarities. International Journal of Science Education 37, 9 (2015), 1478–1503.Google ScholarCross Ref
- [60] . 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), 203–231.Google ScholarCross Ref
- [61] . 2020. A cultural psychological model of cross-national variation in gender gaps in STEM participation. Personality and Social Psychology Review 24, 4 (2020), 345–370.Google ScholarCross Ref
- [62] . 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), 622–639.Google ScholarCross Ref
- [63] . 2016. Gender and performance in computer science. ACM Transactions on Computing Education (TOCE) 16, 3 (2016), 1–16.Google ScholarDigital Library
- [64] . 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, 1–8.Google ScholarDigital Library
- [65] . 2022. Europe Code Week. https://codeweek.eu/Google Scholar
- [66] . 2018. The relationship of STEM attitudes and career interest. EURASIA Journal of Mathematics, Science and Technology Education 14, 10 (2018), 1–17.Google ScholarCross Ref
- [67] . 2000. Expectancy–value theory of achievement motivation. Contemporary Educational Psychology 25, 1 (2000), 68–81.Google ScholarCross Ref
- [68] . 1994. Reasonable mean-square fit values. Rasch Meas Trans 8 (1994), 370.Google Scholar
- [69] . 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 Scholar
Index Terms
- Cross-Country Variation in (Binary) Gender Differences in Secondary School Students’ CS Attitudes: Re-Validating and Generalizing a CS Attitudes Scale
Recommendations
CS Unplugged and Middle-School Students’ Views, Attitudes, and Intentions Regarding CS
Many students hold incorrect ideas and negative attitudes about computer science (CS). In order to address these difficulties, a series of learning activities called Computer Science Unplugged was developed by Tim Bell and his colleagues. These ...
Gender Differences in Taiwan University Students' Attitudes toward the Web-based Learning
Proceedings of the 2005 conference on Towards Sustainable and Scalable Educational Innovations Informed by the Learning Sciences: Sharing Good Practices of Research, Experimentation and InnovationThis paper explored gender differences on university students' attitudes toward the web-based learning in Taiwan. Researchers used an on-line questionnaire to survey students' attitudes about the web-based learning. The initial responses in this study ...
Computer attitudes of primary and secondary students in South Africa
This study investigated computer attitudes of 240 students from eight primary and secondary schools in South Africa. The student population of six of the eight schools that participated in the study can be characterised as middle or upper class. Two ...
Comments