學生在數學與自然科學上的兩性差異對未來勞動市場的影響一直是一個重要的議題，有鑑於此，本文使用臺灣學生學習成就評量資料庫 (Taiwan Assessment of Student Achievement, 簡稱TASA) 的資料檢視2010年與2013年國二學生數學與自然學習成效、態度、學習模式等三方面的決定因素，並探究男女差異在不同世代間的變化趨勢與縮小男女差異的管道。
實證方法採用一般迴歸分析 (ordinary least squares, 簡稱OLS) 比較男女學生在數學與自然表現的差異，透過女性與年虛擬變數的交乘項，檢視男女差異的變化趨勢。實證結果顯示：(1) 數學成績的男女差異有些微擴大趨勢，自然成績的男女差異呈縮小趨勢。(2) 在學習態度方面，數學態度與自然態度的男女差異均有縮小趨勢。(3) 在數學學習模式中，碰到不會的問題會請教老師或同學，以及考試時遇到沒看過的數學題目會努力分析問題，男女差異均呈擴大趨勢。然而，會將數學應用到其他學科則是呈現男女差異縮小趨勢。(4) 自然學習模式中，會將自然應用到其他學科也呈現男女差異趨勢縮小的狀況，閱讀自然課外讀物則沒有發現男女差異趨勢變化。(5) 學習積極度對男女學生在數學與自然學科的學習成效、態度、學習模式等方面大多有正向顯著的影響。
其次，本文依照男女同學樣本分析男同學和女同學的數學與自然表現的因素，並整理對女性有正向關係且對男性沒有顯著關係的因素作為縮小男女學生在數學、自然學習表現差異的可能管道。結果顯示，如果兩個月後要再做一次類似的測驗，因為希望能進步而願意準備下次測驗、母親教育程度以及師生關係，是縮小男女在數學和自然表現差異的可能因素。

The gender differences in performance of mathematics and natural sciences are important issues to understand the gender difference in the labor market performance. The thesis uses data from the Taiwan Assessment of Student Achievement (TASA) to study the determinants of mathematics as well as natural sciences learning performance, attitudes, and learning modes, respectively, for the 8th grade students in 2010 and 2013. The analyses will focus on the trend of gender differences in learning performance, attitudes, and learning modes between the two 8th grade students, and the likely potential factors for narrowing the gap between boys and girls.
The ordinary least squares (OLS) method is used to compare differences in mathematics and natural sciences outcomes between boys and girls. The trend of gender differences in the outcomes is examined by the intersection term between female and year. The results show that: (1) The gender differences in mathematics achievement is slightly widening while the gender gap in natural sciences achievement is narrowing. (2) For the learning attitudes, gender differences in attitudes towards mathematics and natural sciences tend to shrink. (3) In the analyses of mathematics learning modes, “asking teachers or classmates when they had difficulty in solving questions” as well as “trying to analyze problems when facing unfamiliar questions”, the gender differences in these two modes are expanding. Nevertheless, applying the math knowledge to other subjects reveals the reverse pattern. The gender difference in this mode is narrowing. (4) In the analyses of natural sciences learning modes, the gender difference in the mode of “applying natural sciences to other disciplines” is shrinking whereas the gender difference in the mode of “reading natural sciences related extracurricular books” remains the same. (5) Enthusiasm to learning mostly is positively related to learning performance, attitudes, and learning modes of mathematics and natural sciences for boys and girls.
In addition, this thesis re-examines the analyses by both genders, and sorts out the factors that have a positive relationship with girls’ outcome but no such significant relationship with boys’ as a likely factor that reduced the gender gap performance in mathematics and natural sciences. Results indicate that willingness to prepare for the next similar test in the hope of improving after two months, mother's education level, and relationship between teachers and students are likely factors to narrow the gender gap performance in mathematics and natural sciences.

1前言	1
2文獻回顧	5
2.1 STEM領域的重要性	5
2.2男女投入在STEM領域的差異及STEM領域的決定因素	6
2.3兩性在數學、自然科學上的學業表現差異	9
2.4影響學習成就的因素	11
3資料	15
3.1資料來源和抽樣方式	15
3.2變數定義與變數敘述統計	16
4.實證結果	27
4.1實證策略	27
4.2全部數學成績的樣本	28
4.3全部自然成績的樣本	36
4.4同時有數學成績、自然成績的樣本	41
4.5 比較不同樣本群在數學成果和自然成果變數的差異	51
4.6子樣本分析--依照男女學生檢視在數學和自然成果的差異	52
5結論	82
參考文獻	85

 
表目錄
表 1：全部數學成績樣本的敘述統計表	98
表 2：全部自然成績樣本的敘述統計表	99
表 3：同時有數學、自然成績樣本的敘述統計表	100
表 4：國二學生數學成績的決定因素--使用全部數學成績樣本	101
表 5：國二學生花在數學的時間及自評數學測驗難度的決定因素--使用全部數學成績樣本	102
表 6：國二學生數學態度的決定因素--使用全部數學成績樣本	103
表 7：國二學生數學學習模式的決定因素--使用全部數學成績樣本	104
表 8：國二學生自然成績的決定因素--使用全部自然成績樣本	105
表 9：國二學生花在自然的時間的決定因素--使用全部自然成績樣本	106
表 10：國二學生自然態度的決定因素--使用全部自然成績樣本	107
表 11：國二學生自然學習模式的決定因素--使用全部自然成績樣本	108
表 12：國二學生數學成效的決定因素--使用同時有數學、自然成績樣本	109
表 13：國二學生數學態度、數學學習模式的決定因素--使用同時有數學、自然成績樣本	110
表 14：國二學生自然成效的決定因素--使用同時有數學、自然成績樣本	111
表 15：國二學生自然態度、自然學習模式的決定因素--使用同時有數學、自然成績樣本	112
表 16：比較不同樣本群在數學成果和自然成果變數的差異	113
表 17：依男女樣本分析：影響國二男女學生數學成績的因素--使用全部數學成績樣本	114
表 18：依男女樣本分析：影響國二男女學生花在數學的時間及自評數學測驗難度的因素--使用全部數學成績樣本	115
表 19：依男女樣本分析：國二男女學生數學態度的決定因素--使用全部數學成績樣本	116
表 20：依男女樣本分析：國二男女學生數學學習模式的決定因素--使用全部數學成績樣本	117
表 21：依男女樣本分析：國二男女學生在自然成效的決定因素--使用全部自然成績樣本	118
表 22：依男女樣本分析：國二男女學生自然態度的決定因素--使用全部自然成績樣本	119
表 23：依男女樣本分析：國二男女學生自然學習模式的決定因素--使用全部自然成績樣本	120
表 24：依男女樣本分析：影響國二男女學生數學成績的因素--使用同時有數學、自然成績樣本	121
表 25：依男女樣本分析：影響國二男女學生花在數學的時間及自評數學測驗難度的因素--使用同時有數學、自然成績樣本	122
表 26：依男女樣本分析：國二男女學生數學態度的決定因素--使用同時有數學、自然成績樣本	123
表 27：依男女樣本分析：國二男女學生數學學習模式的決定因素--使用同時有數學、自然成績樣本	124
表 28：依男女樣本分析：影響國二男女學生自然成效的因素--使用同時有數學、自然成績樣本	125
表 29：依男女樣本分析：國二男女學生自然態度的決定因素--使用同時有數學、自然成績樣本	126
表 30：依男女樣本分析：國二男女學生自然學習模式的決定因素--使用同時有數學、自然成績樣本	127
表 31：比較不同樣本群在數學成果和自然成果變數的差異	128
表 32：比較不同樣本群在數學成果和自然成果變數的差異	129
附錄A   130

丁學勤與曾智豐 (2013)，「影響國中階段貧窮學生學業表現之因素探析-以臺灣兒童暨家庭扶助基金會扶助對象為例」，《台灣教育社會學研究》，13(1)，1-42。
余民寧、翁雅芸與張靜軒 (2018)，「數理科學的學習動機有性別差異嗎? 一個來自後設分析的證據」， 《當代教育季刊》，26(1)，45-75。
余鴻穎 (2006)，《高職學生數學學習困擾與學習態度之研究》，臺北科技大學技術及職業教育研究所碩士論文。
吳幼妃 (1982)，「父母對子女教育期待有性別差異，對男孩的期待高於對女孩的期待」，《教育學刊》，4。
吳武典 (1997)，「國中偏差學習模式學生學校生活適應之探討」，《教育心理學報》，29，25-49。
巫有鎰 (1999)，「影響國小學生學業成就的因果機制－以台北市和台東縣作比較」，《教育研究集刊》，43，213-242。
李浩然與柳賢 (2012)，「國三學生數學觀念之研究」，《科學教育學刊》， 20(3)， 267-294。
周新富與賴鑫城 (2004)，「父母教育期望的理論與影響因素之探討」，《正修通識教育學報》，1，301-326。
柯慶輝 (2006)，「以「小組加速教學」 改進數學學習之行動研究」，《國民教育研究學報》，17，55-81。
郎亞琴與陳彩卿 (2008)，「國民中學學生之性別， 性別角色， 數學自我效能與數學成就之研究-以彰化縣國民中學三年級學生為例」，《立德學報》， 6(1)， 44-58。
柴昌維與趙增偉 (2019)，「運用新興科技為教學輔具以提升偏鄉中小學女學生對STEM 領域學習之興趣」，《科技部補助專題研究計畫報告》。
張玉茹與江芳盛 (2013)，「師生關係、學習動機與數學學業成就模式之驗證──以PISA_2003資料庫為例」，《測驗統計年刊》，(21)。
曹博盛 (2015)，「八年級學生數學成就及相關因素探討」，《國際數學與科學教育成就趨勢調查》。
郭靜姿、林慶波、張馨仁、周坤賢、曾琦芬、張玉佩與林燁虹 (2012)，「高中數理能力優異班學生與普通班學生大腦結構及性別差異之研究」，《教育科學研究期刊 》， 57(2)， 25-64。
陳怡君 (2016)，「淺談空間能力的性別差異與科學、科技、工程及數學類型的職業選擇」，《科學教育月刊》，392，47-55。
陳嵐瑩與李維鈞 (2018)，「轉型式教師領導風格，師生關係與學生學習動機關係之研究：以新北市 C 中學職業科為例」，《雙溪教育論壇》， (7)，197-213。
陳義汶與呂佳陵 (2012)，「國中生數學成績與性別差異之相關研究」，《國民教育學報》， (9)， 121-145。
劉家樺 (2018)，「2001 年高中職和五專多元入學方案與入學後學校滿意度，學業壓力及在校表現之關係」，《經濟論文叢刊》，46(4)，569-618。
劉家樺與駱明慶 (2021)，「父母的性別偏好對國中子女升學和高中職主修領域選擇的影響－以「台灣青少年計畫」樣本為例」，《經濟論文》，49(2)，163-200。
歐仁榮 (2004)，《家庭環境、家庭結構對高職生學業成就影響之研究---以台南市高級職業學校為例》，國立中正大學犯罪防治研究所碩士論文。
盧雪梅與毛國楠 (2008)，「國中基本學力測驗數學科之性別差異與差別試題功能 (DIF) 分析」， 《教育實踐與研究》， 21(2)， 95-126。
賴佩祺 (2005)，《家庭結構對學習成就的影響》，台灣大學經濟研究所碩士論文。
駱明慶 (2001)，「教育成就的省籍與性別差異」，《經濟論文叢刊》，29(2)，117-152。
駱明慶 (2004)，「升學機會與家庭背景」，《經濟論文叢刊》， 32(4)，417-445。
謝小芩 (1998)，「性別與教育期望」，《婦女與兩性學刊》，9，205-231。
Aiken Jr, L. R. (1976), “Update on attitudes and other affective variables in learning mathematics,” Review of Educational Research, 46(2), 293-311.
Aiken Jr, L. R., and Dreger, R. M. (1961), “The effect of attitudes on performance in mathematics,” Journal of Educational Psychology, 52(1), 19.
Akınoğlu, O., and Tandoğan, R. Ö. (2007), “The effects of problem-based active learning in science education on students’ academic achievement, attitude and concept learning,” Eurasia Journal of Mathematics, Science and Technology Education, 3(1), 71-81.
Amelink, C. T. (2009), “Literature overview: Gender differences in math performance,” SWEAWE CASEE Overviews. 
Arthur, Y. D., Dogbe, C. S. K., and Asiedu-Addo, S. K. (2022), “Enhancing performance in mathematics through motivation, peer assisted learning, and teaching quality: The mediating role of student interest,” EURASIA Journal of Mathematics, Science and Technology Education, 18(2), em2072.
Baker, D. P., and Jones, D. P. (1993), “Creating gender equality: Cross-national gender stratification and mathematical performance,” Sociology of Education, 91-103.
Barile, J. P., Donohue, D. K., Anthony, E. R., Baker, A. M., Weaver, S. R., and Henrich, C. C. (2012), “Teacher–student relationship climate and school outcomes: Implications for educational policy initiatives,” Journal of Youth and Adolescence, 41(3), 256-267.
Barth, J. M., and Masters, S. (2020), “Changes in math and science interest over school transitions: relations to classroom quality, gender stereotypes, and efficacy,” International Journal of Gender, Science and Technology, 12(1).
Beede, D. N., Julian, T. A., Langdon, D., McKittrick, G., Khan, B., and Doms, M. E. (2011), “Women in STEM: A gender gap to innovation,” Economics and Statistics Administration Issue Brief, (04-11).
Beier, M., and Rittmayer, A. (2008), “Literature overview: Motivational factors in STEM: Interest and self-concept,” Assessing Women and Men in Engineering.
Bench, S. W., Lench, H. C., Liew, J., Miner, K., and Flores, S. A. (2015), “Gender gaps in overestimation of math performance,” Sex Roles, 72(11), 536-546.
Bishop, J. L. (2008), “A comparative analysis of self-esteem, school involvement, family cohesiveness and academic achievement in seventh and eighth graders dependent upon their relative age,” City University of New York.
Blackwell, L. S., Trzesniewski, K. H., and Dweck, C. S. (2007), “Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention,” Child Development, 78(1), 246-263.
Brecko, B. N. (2004), “How family background influences student achievement,” In Proceedings of the IRC-2004 TIMSS ,1(1),191-205.
Browne, K. R. (2006), “Evolved sex differences and occupational segregation,” Journal of Organizational Behavior: The International Journal of Industrial, Occupational and Organizational Psychology and Behavior, 27(2), 143-162.
Bryan, T., and Bryan, J. (1991), “Positive mood and math performance,” Journal of Learning Disabilities, 24(8), 490-494.
Buchmann, C. (2002), “Measuring family background in international studies of education: Conceptual issues and methodological challenges,” Methodological Advances in Cross-National Surveys of Educational Achievement, 150, 197.
Buchmann, C., and DiPrete, T. A. (2006), “The growing female advantage in college completion: The role of family background and academic achievement,” American Sociological Review, 71(4), 515-541.
Burstein, L., Fischer, K. B., and Miller, M. D. (1980), “The multilevel effects of background on science achievement: A cross-national comparison,” Sociology of Education, 215-225.
Butler, R. (1998), “Determinants of help seeking: Relations between perceived reasons for classroom help-avoidance and help-seeking behaviors in an experimental context,” Journal of Educational Psychology, 90(4), 630.
Byrnes, J. P., and Takahira, S. (1993), “Explaining gender differences on SAT-math items,” Developmental Psychology, 29(5), 805.
Byrnes, J. P., Hong, L., and Xing, S. (1997), “Gender differences on the math subtest of the Scholastic Aptitude Test may be culture-specific,” Educational Studies in Mathematics, 34(1), 49-66.
Ceci, S. J., Ginther, D. K., Kahn, S., and Williams, W. M. (2014), “Women in academic science: A changing landscape,” Psychological Science in the Public Interest, 15(3), 75-141.
Ceci, S. J., Williams, W. M., and Barnett, S. M. (2009), “Women's underrepresentation in science: sociocultural and biological considerations,” Psychological Bulletin, 135(2), 218-261.
Chapman, E., Baron-Cohen, S., Auyeung, B., Knickmeyer, R., Taylor, K., and Hackett, G. (2006), “Fetal testosterone and empathy: evidence from the empathy quotient (EQ) and the “reading the mind in the eyes” test,” Social Neuroscience, 1(2), 135-148.
Chen, L., Bae, S. R., Battista, C., Qin, S., Chen, T., Evans, T. M., and Menon, V. (2018), “Positive attitude toward math supports early academic success: Behavioral evidence and neurocognitive mechanisms,” Psychological Science, 29(3), 390-402.
Cheng, A., Kopotic, K., and Zamarro, G. (2017), “Can parents’ growth mindset and role modelling address STEM gender gaps?,” EDRE Working Paper (No. 2017-07), 41.
Clark Blickenstaff*, J. (2005), “Women and science careers: leaky pipeline or gender filter?,” Gender and Education, 17(4), 369-386.
Covarrubias, R., Laiduc, G., and Valle, I. (2019), “Growth messages increase help-seeking and performance for women in STEM,” Group Processes and Intergroup Relations, 22(3), 434-451.
Crosnoe, R., Morrison, F., Burchinal, M., Pianta, R., Keating, D., Friedman, S. L., and Clarke-Stewart, K. A. (2010), “Instruction, teacher–student relations, and math achievement trajectories in elementary school,” Journal of Educational Psychology, 102(2), 407.
Croson, R., and Gneezy, U. (2009), “Gender differences in preferences,” Journal of Economic Literature, 47(2), 448-74.
Cvencek, D., Meltzoff, A. N., and Greenwald, A. G. (2011), “Math–gender stereotypes in elementary school children,” Child Development, 82(3), 766-779.
Datta Gupta, N., Poulsen, A., and Villeval, M. C. (2005), “Male and Female Competitive Behavior-Experimental Evidence,” GATE Working Paper ( No. W.P.05-12), 38.
Dickhauser, O., and Meyer, W. J. P. S. (2006), “Gender differences in young children's math ability attributions,” Psychology Science, 48(1), 3.
Eccles, J. S. (2007), “Where Are All the Women? Gender Differences in Participation in Physical Science and Engineering,” American Psychological Association.
Eccles, J. S., and Jacobs, J. E. (1986), “Social forces shape math attitudes and performance,” Signs: Journal of Women in Culture and Society, 11(2), 367-380.
Ellison, G., and Swanson, A. (2018), “Dynamics of the gender gap in high math achievement (No. w24910),” National Bureau of Economic Research.
Entwisle, D. R., Alexander, K. L., and Olson, L. S. (1994), “The gender gap in math: Its possible origins in neighborhood effects,” American Sociological Review, 822-838.
Fennema, E., and Sherman, J. (1977), “Sex-related differences in mathematics achievement, spatial visualization and affective factors,” American Educational Research Journal, 14(1), 51-71.
Frost, L. A., Hyde, J. S., and Fennema, E. (1994), “Gender, mathematics performance, and mathematics-related attitudes and affect: A meta-analytic synthesis,” International Journal of Educational Research, 21(4), 373-385.
Fyans Jr, L. J., and Maehr, M. L. (1987), “Sources of Student Achievement: Student Motivation, School Context and Family Background,” Retrieved from  https://eric.ed.gov/?id=ED290997 
Good, C., Rattan, A., and Dweck, C. S. (2012), “Why do women opt out? Sense of belonging and women's representation in mathematics,” Journal of Personality and Social Psychology, 102(4), 700.
Guiso, L., Monte, F., Sapienza, P., and Zingales, L. (2008), “Culture, Gender, and Math,” Science, 320(5880), 1164-1165.
Gunderson, E. A., Ramirez, G., Levine, S. C., and Beilock, S. L. (2012), “The role of parents and teachers in the development of gender-related math attitudes,” Sex Roles, 66(3), 153-166. 
Haile, G. A., and Nguyen, A. N. (2008), “Determinants of academic attainment in the United States: A quantile regression analysis of test scores,” Education Economics, 16(1), 29-57.
Halpern, D. F., Benbow, C. P., Geary, D. C., Gur, R. C., Hyde, J. S., and Gernsbacher, M. A. (2007), “The science of sex differences in science and mathematics,” Psychological Science in the Public Interest, 8(1), 1-51.
Hedges, L. V., and Nowell, A. (1995), “Sex differences in mental test scores, variability, and numbers of high-scoring individuals,” Science, 269(5220), 41-45.
Hong, L., and Page, S. E. (2004), “Groups of diverse problem solvers can outperform groups of high-ability problem solvers,” Proceedings of the National Academy of Sciences, 101(46), 16385-16389.
Hyde, J. S., and Mertz, J. E. (2009), “Gender, culture, and mathematics performance,” Proceedings of the National Academy of Sciences, 106(22), 8801-8807.
Hyde, J. S., Fennema, E., and Lamon, S. J. (1990a), “Gender differences in mathematics performance: a meta-analysis,” Psychological Bulletin, 107(2), 139.
Hyde, J. S., Fennema, E., Ryan, M., Frost, L. A., and Hopp, C. (1990b), “Gender comparisons of mathematics attitudes and affect: A meta-analysis,” Psychology of Women Quarterly, 14(3), 299-324.
Hyde, J. S., Lindberg, S. M., Linn, M. C., Ellis, A. B., and Williams, C. C. (2008), “Gender similarities characterize math performance,” Science, 321(5888), 494-495.
Iben, M. F. (1991), “Attitudes and mathematics,” Comparative Education, 27(2), 135-151.
Jacobs, J. E., and Bleeker, M. M. (2004), “Girls' and boys' developing interests in math and science: Do parents matter?,” New Directions for Child and Adolescent Development, (106), 5-21.
Killewald, A. A. (2012), “Is American science in decline?,” Harvard University Press.
Krapp, A., and Prenzel, M. (2011), “Research on interest in science: Theories, methods, and findings,” International Journal of Science Education, 33(1), 27-50.
Lavy, V., & Sand, E. (2015), “On the origins of gender human capital gaps: Short and long term consequences of teachers’ stereotypical biases,” National Bureau of Economic Research.
Leahey, E., and Guo, G. (2001), “Gender differences in mathematical trajectories,” Social Forces, 80(2), 713-732.
Lee, V. E., and Burkam, D. T. (2002), “Inequality at the starting gate: Social background differences in achievement as children begin school,” Economic Policy Institute, 1660 L Street, NW, Suite 1200, Washington, DC 20036.
Legewie, J., and DiPrete, T. A. (2014), “Pathways to science and engineering bachelor’s degrees for men and women,” Sociological Science, 1, 41-48.
Liu, O. L., and Wilson, M. (2009), “Gender differences in large-scale math assessments: PISA trend 2000 and 2003,” Applied Measurement in Education, 22(2), 164-184.
Lordan, G., and Pischke, J. S. (2022), “Does Rosie like riveting? Male and female occupational choices,” Economica, 89(353), 110-130.
Macaluso, B., Larivière, V., Sugimoto, T., and Sugimoto, C. R. (2016), “Is science built on the shoulders of women? A study of gender differences in contributorship,” Academic Medicine, 91(8), 1136-1142.
Maurin, E., Ly, S. T., and Landaud, F. (2016), “Competitive Schools and the Gender Gap in the Choice of Field of Study,” Retrieved from  https://cepr.org/active/publications/discussion_papers/dp.php?dpno=11411 
McClure, I. (2003), “The essential difference: men, women and the extreme male brain,” BMJ, 327(7405), 57.
Miller, J. L., Ward, R. B., Sienkiewicz, F., and Antonucci, P. (2011), “ITEAMS: An out-of-school time project to promote gain in fundamental science content and enhance interest in STEM careers for middle school students,” Journal of Systemics, Cybernetics and Informatics, 9(4), 76-80.
Miller, P. H., Slawinski Blessing, J., and Schwartz, S. (2006), “Gender differences in high‐school students’ views about science,” International Journal of Science Education, 28(4), 363-381.
Mullis, I. V., Martin, M. O., Smith, T. A., Garden, R. A., Gregory, K. D., Gonzalez, E. J., ... and O’Connor, K. M. (2001), “Assessment frameworks and specifications 2003,” International Study Center, Lynch School of Education, Boston College.
National Academy of Sciences, National Academy of Engineering, and Institute of Medicine. (2007), “Rising Above the Gathering Storm: Energizing and Employing America for a Brighter Economic Future,” Washington, DC: The National Academies Press.
Nelson-Le Gall, S., and Glor-Scheib, S. (1985), “Help seeking in elementary classrooms: An observational study,” Contemporary Educational Psychology, 10(1), 58-71.
Niederle, M., and Vesterlund, L. (2007), “Do women shy away from competition? Do men compete too much?,” The Quarterly Journal of Economics, 122(3), 1067-1101.
Niederle, M., and Vesterlund, L. (2010), “Explaining the gender gap in math test scores: The role of competition,” Journal of Economic Perspectives, 24(2), 129-44.
Nix, S., Perez-Felkner, L., and Thomas, K. (2015), “Perceived mathematical ability under challenge: A longitudinal perspective on sex segregation among STEM degree fields,” Frontiers in Psychology, 530.
Nollenberger, N., Rodríguez-Planas, N., and Sevilla, A. (2016), “The math gender gap: The role of culture,” American Economic Review, 106(5), 257-61.
Nonoyama-Tarumi, Y., Hughes, K., and Willms, J. D. (2015), “The role of family background and school resources on elementary school students’ mathematics achievement,” Prospects, 45(3), 305-324.
Nosek, B. A., Smyth, F. L., Sriram, N., Lindner, N. M., Devos, T., Ayala, A., ... and Greenwald, A. G. (2009), “National differences in gender–science stereotypes predict national sex differences in science and math achievement,” Proceedings of the National Academy of Sciences, 106(26), 10593-10597.
Oberle, E., and Schonert-Reichl, K. A. (2013), “Relations among peer acceptance, inhibitory control, and math achievement in early adolescence,” Journal of Applied Developmental Psychology, 34(1), 45-51.
Pajares, F., and Miller, M. D. (1994), “Role of self-efficacy and self-concept beliefs in mathematical problem solving: A path analysis,” Journal of educational Psychology, 86(2), 193.
Park, H., Behrman, J. R., and Choi, J. (2018), “Do single-sex schools enhance students’ STEM (science, technology, engineering, and mathematics) outcomes?,” Economics of Education Review, 62, 35-47.
Pavitt, K. (1996), “National policies for technical change: Where are the increasing returns to economic research?,” Proceedings of the National Academy of Sciences, 93(23), 12693-12700.
Perl, T. H. (1982), “Discriminating factors and sex differences in electing mathematics,” Journal for Research in Mathematics Education, 13(1), 66-74.
Pinxten, M., Marsh, H. W., De Fraine, B., Van Den Noortgate, W., and Van Damme, J. (2014), “Enjoying mathematics or feeling competent in mathematics? Reciprocal effects on mathematics achievement and perceived math effort expenditure,” British Journal of Educational Psychology, 84(1), 152-174.
Price, D. J. (1986), “Little science, big science... and beyond,” New York: Columbia University Press, 480.
Reis, S. M., and Park, S. (2001), “Gender differences in high-achieving students in math and science,” Journal for the Education of the Gifted, 25(1), 52-73.
Sax, L. J., Kanny, M. A., Riggers-Piehl, T. A., Whang, H., and Paulson, L. N. (2015), ““But I’m not good at math”: The changing salience of mathematical self-concept in shaping women’s and men’s STEM aspirations,” Research in Higher Education, 56(8), 813-842.
Smetackova, I. (2015), “Gender stereotypes, performance and identification with math,” Procedia-Social and Behavioral Sciences, 190, 211-219.
Tian, M. (2006), “A quantile regression analysis of family background factor effects on mathematical achievement,” Journal of Data Science, 4(4), 461-478.
Van Voorhis, F. L. (2003), “Interactive homework in middle school: Effects on family involvement and science achievement,” The Journal of Educational Research, 96(6), 323-338.
Vanderbergue, V. (2002), “Evaluating the magnitude and the stakes of peer effects analysing science and math achievement across OECD,” Applied Economics, 34(10), 1283-1290.
Wang, D. B. (2004), “Family background factors and mathematics success: A comparison of Chinese and US students,” International Journal of Educational Research, 41(1), 40-54.
Weinburgh, M. (1995), “Gender differences in student attitudes toward science: A meta‐analysis of the literature from 1970 to 1991,” Journal of Research in Science Teaching, 32(4), 387-398.
Weiser, D. A., and Riggio, H. R. (2010), “Family background and academic achievement: Does self-efficacy mediate outcomes?,” Social Psychology of Education, 13(3), 367-383.
Whalen, D. F., and Shelley, M. C. (2010), “Academic success for STEM and non-STEM majors,” Journal of STEM Education: Innovations and Research, 11(1).
Wößmann, L. (2005), “Educational production in East Asia: The impact of family background and schooling policies on student performance,” German Economic Review, 6(3), 331-353.
Xie, F., Xin, Z., Chen, X., and Zhang, L. (2019), “Gender difference of Chinese high school students’ math anxiety: The effects of self-esteem, test anxiety and general anxiety,” Sex Roles, 81(3), 235-244.
Xie, Y., and Shauman, K. A. (2003), “Women in science. Harvard University Press,.
Xie, Y., Fang, M., and Shauman, K. (2015) “STEM education,” Annual Review of Sociology, 41, 331.
Zhu, Z. (2007), “Gender differences in mathematical problem solving patterns: A review of literature,” International Education Journal, 8(2), 187-203.
Zimmerman, B. J., Bandura, A., and Martinez-Pons, M. (1992), “Self-motivation for academic attainment: The role of self-efficacy beliefs and personal goal setting,” American Educational Research Journal, 29(3), 663-676.