269
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Why female students leave science classes for social science classes in Cambodia: A two-level hierarchical linear model

ORCID Icon
Received 27 May 2023, Accepted 30 Nov 2023, Published online: 13 Dec 2023

References

  • Adamuti-Trache, M., & Sweet, R. (2014). Science, technology, engineering, and math readiness: Ethno-linguistic and gender differences in high-school course selection patterns. International Journal of Science Education, 36(4), 610–634. https://doi.org/10.1080/09500693.2013.819453
  • ADB. (2020). Upper secondary education sector development program and PATA 9178-CAM: Accelerating policy reforms in secondary education. Asian Development Bank.
  • Ainley, J., Kos, J., & Nicholas, M. (2008). Participation in science, mathematics, and technology in Australian education. Australian Council for Educational Research.
  • Bergeron, L., & Gordon, M. (2017). Establishing a STEM pipeline: Trends in male and female enrollment and performance in higher level secondary STEM courses. International Journal of Science and Mathematics Education, 15(3), 433–450. https://doi.org/10.1007/s10763-015-9693-7
  • Bhanot, R. T., & Jovanovic, J. (2009). The links between parent behaviors and boys’ and girls’ science achievement beliefs. Applied Developmental Science, 13(1), 42–59. https://doi.org/10.1080/10888690802606784
  • Bieri Buschor, C., Berweger, S., Keck Frei, A., & Kappler, C. (2014). Majoring in STEM—what accounts for women's career decision making? A mixed methods study. The Journal of Educational Research, 107(3), 167–176. https://doi.org/10.1080/00220671.2013.788989
  • Blackburn, H. (2017). The status of women in STEM in higher education: A review of the literature 2007–2017. Science & Technology Libraries, 36(3), 235–273. https://doi.org/10.1080/0194262X.2017.1371658
  • Bray, M. (2006). Private supplementary tutoring: Comparative perspectives on patterns and implications. Compare: A Journal of Comparative and International Education, 36(4), 515–530. https://doi.org/10.1080/03057920601024974
  • Bray, M., Kobakhidze, M. N., Liu, J., & Zhang, W. (2016). The internal dynamics of privatised public education: Fee-charging supplementary tutoring provided by teachers in Cambodia. International Journal of Educational Development, 49, 291–299. https://doi.org/10.1016/j.ijedudev.2016.04.003
  • Bray, M., & Lykins, C. (2012). Shadow education: Private supplementary tutoring and its implications for policy makers in Asia. Asian Development Bank.
  • Brehm, W. C., & Silova, I. (2014). Hidden privatization of public education in Cambodia: Equity implications of private tutoring. Journal for Educational Research Online, 6(1), 94–116.
  • Castro, S. L. (2002). Data analytic methods for the analysis of multilevel questions: A comparison of intraclass correlation coefficients, rwg (j), hierarchical linear modeling, within-and between-analysis, and random group resampling. The Leadership Quarterly, 13(1), 69–93. https://doi.org/10.1016/S1048-9843(01)00105-9
  • Cho, D. (2007). The role of high school performance in explaining women's rising college enrollment. Economics of Education Review, 26(4), 450–462. https://doi.org/10.1016/j.econedurev.2006.03.001
  • Clark Blickenstaff, J. (2005). Women and science careers: Leaky pipeline or gender filter? Gender and Education, 17(4), 369–386. https://doi.org/10.1080/09540250500145072
  • Dawson, W. (2010). Private tutoring and mass schooling in east Asia: Reflections of inequality in Japan, South Korea, and Cambodia. Asia Pacific Education Review, 11(1), 14–24. https://doi.org/10.1007/s12564-009-9058-4
  • Eam, P., Keo, B., Leng, P., Song, S., & Khieng, S. (2021). Correlates of STEM major choice: A quantitative look at Cambodian university freshmen. Research in Science & Technological Education, 39(2), 206–224. https://doi.org/10.1080/02635143.2019.1682987
  • Eam, P., Leng, P., Khieng, S., & Song, S. (2022). Cambodian post-secondary education and training in the global knowledge societies. CDRI, Cambodia Development Research Institute.
  • Edwards, D. B., Zimmermann, T., Sitha, C., Williams, J. H., & Kitamura, Y. (2014). Student transition from primary to lower secondary school in Cambodia: Narrative insights into complex systems. Prospects, 44(3), 367–380. https://doi.org/10.1007/s11125-014-9318-x
  • Elgar, A. G. (2004). Science textbooks for lower secondary schools in Brunei: Issues of gender equity. International Journal of Science Education, 26(7), 875–894. https://doi.org/10.1080/0950069032000138888
  • Finkel, L. (2017). Walking the path together from high school to STEM majors and careers: Utilizing community engagement and a focus on teaching to increase opportunities for URM students. Journal of Science Education and Technology, 26(1), 116–126. https://doi.org/10.1007/s10956-016-9656-y
  • Fowler, F. J. (2009). Survey research methods (4th). SAGE Publications, Inc.
  • Garson, G. D. (2013). Introductory guide to HLM with HLM 7 software. Hierarchical Linear Modeling: Guide and Applications, 55–96. https://doi.org/10.4135/9781483384450.n3
  • Griffith, A. L. (2010). Persistence of women and minorities in STEM field majors: Is it the school that matters? Economics of Education Review, 29(6), 911–922. https://doi.org/10.1016/j.econedurev.2010.06.010
  • Hill, C., Corbett, C., & St Rose, A. (2010). Why so few? Women in science, technology, engineering, and mathematics. ERIC.
  • Hoffmann, L. (2002). Promoting girls’ interest and achievement in physics classes for beginners. Learning and Instruction, 12(4), 447–465. https://doi.org/10.1016/S0959-4752(01)00010-X
  • Kao, S. (2019). Family socioeconomic status and students’ choice of STEM majors. International Journal of Comparative Education and Development, 22(1), 49–65. https://doi.org/10.1108/ijced-03-2019-0025
  • Kao, S. (2020). Cambodian upper secondary school students’ attitudes towards science: Trends and patterns. Journal of International Development and Cooperation, 26(1), 15–27.
  • Kao, S., Chea, P., & Song, S. (2022). Upper secondary school tracking and major choices in higher education: To switch or not to switch. CDRI, Cambodia Development Research Institute.
  • Kao, S., & Kinya, S. (2020). Factors affecting Cambodian upper secondary school students’ choice of science track. International Journal of Sociology of Education, 9(3), 262–292. https://doi.org/10.17583/rise.2020.4823
  • Kind, P., Jones, K., & Barmby, P. (2007). Developing attitudes towards science measures. International Journal of Science Education, 29(7), 871–893. https://doi.org/10.1080/09500690600909091
  • Ma, Y. (2022). Profiles of student science attitudes and its associations with gender and science achievement. International Journal of Science Education, 44(11), 1876–1895. https://doi.org/10.1080/09500693.2022.2101705
  • Mark, B., Magda Nutsa, K., Junyan, L., & Wei, Z. (2016). The internal dynamics of privatised public education: Fee-charging supplementary tutoring provided by teachers in Cambodia. International Journal of Educational Development, 49, 291–299. https://doi.org/10.1016/j.ijedudev.2016.04.003
  • Ministry of Planning. (2019). General population census of the kingdom of Cambodia 2019: Provisional population totals. The National Institute of Statistics.
  • MoEYS. (2010). Guidelines on the practice of general education curriculum at upper secondary schools. The Ministry of Education, Youth and Sport.
  • MoEYS. (2016). Policy on science, technology, engineering, and mathematics (STEM) education. The Ministry of Education, Youth and Sport.
  • MoEYS. (2018). Education in Cambodia: Findings from Cambodia's experience in PISA for Development. The Ministry of Education, Youth and Sport.
  • MoEYS. (2020). The enrollment statistics of science and social science streams at upper secondary education. The Ministry of Education, Youth and Sport.
  • Mujtaba, T., & Reiss, M. J. (2013). What sort of girl wants to study physics after the age of 16? Findings from a large-scale UK survey. International Journal of Science Education, 35(17), 2979–2998. https://doi.org/10.1080/09500693.2012.681076
  • OECD. (2016). PISA 2015 results (volume I): Excellence and equity in education, PISA. OECD Publishing. Paris.
  • Oon, P. T., Cheng, M. M. W., & Wong, A. S. L. (2020). Gender differences in attitude towards science: Methodology for prioritising contributing factors. International Journal of Science Education, 42(1), 89–112. https://doi.org/10.1080/09500693.2019.1701217
  • Palmer, T. A. (2020). Student subject choice in the final years of school: Why science is perceived to be of poor value. The Australian Educational Researcher, 47(4), 591–609. https://doi.org/10.1007/s13384-019-00357-9
  • Palmer, T. A., Burke, P. F., & Aubusson, P. (2017). Why school students choose and reject science: A study of the factors that students consider when selecting subjects. International Journal of Science Education, 39(6), 645–662. https://doi.org/10.1080/09500693.2017.1299949
  • Perera, L. D. H. (2014). Parents’ attitudes towards science and their children's science achievement. International Journal of Science Education, 36(18), 3021–3041. https://doi.org/10.1080/09500693.2014.949900
  • Plasman, J., Gottfried, M., Williams, D., Ippolito, M., & Owens, A. (2021). Parents’ occupations and students’ success in stem fields: A systematic review and narrative synthesis. Adolescent Research Review, 6(1), 33–44. https://doi.org/10.1007/s40894-020-00136-z
  • Regan, E., & De Witt, J. (2015). Attitudes, interest, and factors influencing STEM enrolment behavior: An overview of relevant literature. In E. Henriksen, J. Dillon, & J. Ryder (Eds.), Understanding student participation and choice in science and technology education (pp. 63–88). Springer.
  • RGC. (2015). Cambodia industrial development policy 2015-2025: Market orientation and enabling environment for industrial development. The Royal Government of Cambodia.
  • RGC. (2019). National strategic development plan 2019-2023. The Royal Government of Cambodia.
  • Scientific Software International. (2022). HLM Standard (12 months) https://ssilive.com/hlm-standard-12-months.
  • Seaton, G. A. (2011). Belonging uncertainty and psychological capital: An investigation of antecedents of the leaky pipeline in STEM. Purdue University.
  • Silova, I. (2010). Private tutoring in Eastern Europe and Central Asia: Policy choices and implications. Compare: A Journal of Comparative and International Education, 40(3), 327–344. https://doi.org/10.1080/03057920903361926
  • Šimunović, M., & Babarović, T. (2020). The role of parents’ beliefs in students’ motivation, achievement, and choices in the STEM domain: A review and directions for future research. Social Psychology of Education, 23(3), 701–719. https://doi.org/10.1007/s11218-020-09555-1
  • Smith, E., & Gorard, S. (2011). Is there a shortage of scientists? A re-analysis of supply for the UK. British Journal of Educational Studies, 59(2), 159–177. https://doi.org/10.1080/00071005.2011.578567
  • Starr, C. R., & Simpkins, S. D. (2021). High school students’ math and science gender stereotypes: relations with their STEM outcomes and socializers’ stereotypes. Social Psychology of Education, 24(1), 273–298. https://doi.org/10.1007/s11218-021-09611-4
  • Tenenbaum, H. R., & Leaper, C. (2003). Parent-child conversations about science: The socialization of gender inequities? Developmental Psychology, 39(1), 34. https://doi.org/10.1037/0012-1649.39.1.34
  • Thomson, S. (2005). Pathways from school to further education or work: Examining the consequences of year 12 course choices. Australian Council for Educational Research.
  • Todd, B. L., & Zvoch, K. (2019). The effect of an informal science intervention on middle school girls’ science affinities. International Journal of Science Education, 41(1), 102–122. https://doi.org/10.1080/09500693.2018.1534022
  • Walton, G. M., Logel, C., Peach, J. M., Spencer, S. J., & Zanna, M. P. (2015). Two brief interventions to mitigate a “chilly climate” transform women's experience, relationships, and achievement in engineering. Journal of Educational Psychology, 107(2), 468. https://doi.org/10.1037/a0037461
  • Woltman, H., Feldstain, A., MacKay, J. C., & Rocchi, M. (2012). An introduction to hierarchical linear modeling. Tutorials in Quantitative Methods for Psychology, 8(1), 52–69. https://doi.org/10.20982/tqmp.08.1.p052
  • Wu, S., Crespi, C. M., & Wong, W. K. (2012). Comparison of methods for estimating the intraclass correlation coefficient for binary responses in cancer prevention cluster randomized trials. Contemporary Clinical Trials, 33(5), 869–880. doi:10.1016/j.cct.2012.05.004

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.