#### Transcript Trends in math motivation and math self

Trends in math motivation and math self-concept: gender differences NRU HSE Kuzmina yulia Gender and STEM STEM – Science, Technology, Engineering, Math Policymaker from different countries try to encourage girls to participate in STEM-related fields Gender disproportion in STEM exists in many countries including Russia Proportion of females in STEM related fields: Specialists with high education in science and technology – 29% (2015) Among graduates (2012-2014) from physics and math departments - 20%, from biology and chemistry – 60%, IT – 20% (Russian State Statistics Service) Gender gap in math self-concept and motivation is the important factor of gender disproportion in STEM Math self-concept and math intrinsic motivation are an important factors of participation in STEM activity (e.g. Heilbronner, 2011; Marsh & Yeung, 1997) These factors can have a different effect on career choice and academic achievements for boys and girls (e.g. Watt et al., 2012) The role of instrumental motivation in some previous studies was underestimated. Although many math teachers consider that focusing on importance and value math for future life can involve students in math activity and math lessons. Some studies show that in high school instrumental motivation becomes more important than intrinsic motivation Some studies show that math instrumental motivation is more important factor for choice of STEM career for girls than for boys Math instrumental motivation Two way to conceptualize instrumental motivation: 1) External motivation (it is the desire to obtain something practical or rewarding, such as improving future career opportunities (Hudson, 2000). Some prior researches tend to downplay the role of instrumental motivation in learning and found no significant correlations (Gardner, 1983; Gardner, Lalonde, Moorcroft, & Evers, 1985)*. 2) Expectancy value theory. Task value: 1) Perceived importance of being good at this activity 2) Perceived usefulness of the activity for obtaining short- or long-term goals Task value has the positive correlation with cognitive engagement and achievements. In high school task value in math is higher for boys than for girls (Wigfield, Eccles, MacIver, 1991). Beliefs in value in math declines more rapidly in high school (Jacobs et al., 2002) Inconsistent findings about trends in gender differences. Rate of decreasing is higher for girls (Eccles, 1987; Hyde и др., 1990). Rate of decreasing is the same for boys and girls or higher for boys ( Chouinard, Roy, 2008; Xin, & Cartwright, 2003; Watt, 2004; Gasco, 2004). Even though math achievement explained career differences between men and women, math task value partially explained the gender differences in STEM career attainment that were attributed to math achievement (Wang, Degol, Ye, 2015) AIMS To estimate gender differences in math instrumental motivation and math self-concept, math interest in the 8th and 9th grades To estimate the effect of instrumental motivation in math on math achievements, math interest and math self-concept To estimate gender differences in the correlation between math instrumental motivation and other math constructs Data: TIMSS-PISA data (later in more detail) Gender differences Girls have a lower level of math self-concept (Ma, X, 1995; Preckel, Goetz, Pekrun, 2008); math intrinsic motivation (Ackerman et al., 2001; Kovas и др., 2015) and instrumental motivation (Fredricks, Eccles, 2002) Self-concept and motivation have a different correlation with achievements for boys and girls (Vecchione, Alessandri, & Marsicano, 2014; Watt et al., 2012) Data TREC: https://trec.hse.ru/en/ 1st wave: TIMSS (2011) 8th grade. Variables: math self-concept, math intrinsic motivation, math instrumental motivation, TIMSS math scores. N= 4636 2nd wave: PISA (2012) 9th grade. Variables: math self-concept, math intrinsic motivation, math instrumental motivation, PISA math scores N=2927 (due to rotational design) Method 1) Multi-group CFA: measurement invariance, estimation of gender differences for each wave 2) Testing path models with 2-wave data (auto-regressive cross-lagged models) (all sample, by gender) 3) Testing model with data from 3rd way (probability to choose STEM career – self-report) Comparison mean latent variables After confirmation of measurement invariance we compare means of latent constructs Difference between boys and girls (std.) Wave 1 (8th grade) Wave2 (9th grade) Math self-concept -.10*** (.03) -.16*** (.04) Math intrinsic motivation -.05 (.03) -.14*** (.04) Math instrumental motivation -.21*** (.03) -.31*** (.04) Math self-concept, motivation and math achievements Female TIMSS M1 -0.10 Math selfconcept 0.45 Math interest PISA Instrumental motiv. Math self-concept, motivation and math achievements(girls/boys) TIMSS M1 Math selfconcept 0.45/0.47 Math interest PISA Instrumental motivation Auto-regressive cross-lagged model т Math interest1 Math selfconcept1 Instrumental motivation1 TIMSS 0.47 Math interest2 Math selfconcept2 Instrumental motiv.2 PISA Auto-regressive cross-lagged model (girls/boys) т ИНТЕРЕС1 С.оценка1 Инстр.мотив 1 TIMSS 0.47/0.47 Интерес2 С.оценка2 Инстр.мотив 2 PISA Probability to choose STEM career () m Math interest -.07 -.08 Female Math selfconcept Math instrumental motiv. PISA math scores Probability to choose STEM career Instrumental motivation can reduce gender gap in “STEM intention” Probability to choose STEM Female -.38*** (.03) Instrumental motivation .15** (.06) Female* Instr.mot .23*** (.07) Probability to choose STEM career (girls/boys) m Math interest Math selfconcept Math instrumental motiv. PISA math scores .15/.32 Probability to choose STEM career Problems and questions Technical: factorial invariance across time??? Interest – how it can be measured? Intrinsic motivation is not always and fully intrinsic Math interest and math lessons: is it possible to keep interest after math class in ordinary school? Are schools and teachers ready to really intrinsically motivated students? Results Math self-concept is more stable than instrumental and intrinsic motivation Girls have a lower level of math self-concept and math instrumental motivation in 8th grade. There is no significant gender difference in math interest in 8th grade. In 9th grade girls have a lower level of math interest. Self-concept and instrumental motivation. Gender differences remain the same from 8th to 9th grade for math selfconcept and math instrumental motivation but increase for math interest There are gender differences in relationships between constructs. Math selfconcept is the significant predictor of subsequent interest for girls only. Math instrumental motivation is significant predictor of math achievements and interest for girls not for boys. At the same time previous achievement correlated with subsequent math SC for boys only. Math self-concept has a larger correlation with probability to choose STEM career for boys than for girls Math instrumental motivation has a larger correlation with probability to choose STEM career for girls than for boys