1. Trends in math motivation and math self-concept: gender differences
NRU HSE
Kuzmina yulia

2. 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 ( ) from physics and math departments - 20%, from biology and chemistry – 60%, IT – 20% (Russian State Statistics Service)

3. 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

4. Math instrumental motivation
Two way to conceptualize instrumental motivation:
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)*.
Expectancy value theory. Task value:
Perceived importance of being good at this activity
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)

5. 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)

6. 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)

7. Data
TREC: 1st wave: TIMSS (2011) 8th grade. Variables: math self-concept, math intrinsic motivation, math instrumental motivation, TIMSS math scores. N= nd wave: PISA (2012) 9th grade. Variables: math self-concept, math intrinsic motivation, math instrumental motivation, PISA math scores N=2927 (due to rotational design)

8. Method
Multi-group CFA: measurement invariance, estimation of gender differences for each wave
Testing path models with 2-wave data (auto-regressive cross-lagged models) (all sample, by gender)
Testing model with data from 3rd way (probability to choose STEM career – self-report)

9. 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)

10. Math self-concept, motivation and math achievements
Female
TIMSS M1
0.20
-0.04
0.54
-0.10
Math self-concept
Instrumental motiv.
0.45
0.59
0.39
Math interest
Затруднения: тип взаимосвязи между самооценкой и инструментальной мотивацией? Возможно, инструментальная мотивация модератор в связи между достижениями и самооценкой?
0.13
0.08
0.61
PISA

11. Math self-concept, motivation and math achievements(girls/boys)
TIMSS M1
0.20/0.20
0.51/0.57
Math self-concept
Instrumental motivation
0.45/0.47
0.63/0.54
0.35/0.42
Math interest
0.13/0.13
0.13/0
0.61/0.59
PISA

12. Auto-regressive cross-lagged model
Math self-concept1
Instrumental motivation1
TIMSS
Math interest1 т
0.09
0.08
0.10
0.47
0.61
0.15
0.69
0.35
0.13
0.08
Корреляции между конструктами в одной волне учитывались, но не отображены на диаграмме
Math interest2
Instrumental motiv.2
PISA
Math self-concept2

13. Auto-regressive cross-lagged model (girls/boys)
С.оценка1
Инстр.мотив1
ИНТЕРЕС1
TIMSS т
0.12/0
0.12/0
0.12/0
0.59/0.63
0.47/0.47
0.20/0
0.80/0.55
0.37/0.30
0.12/0.14
0/0.12
Корреляции между конструктами в одной волне учитывались, но не отображены на диаграмме
Интерес2
Инстр.мотив2
PISA
С.оценка2

14. Probability to choose STEM career ()
Math interest
-.07
Female
-.08
Math self-concept
-.20
-.14
.21
Probability to choose STEM career
Math instrumental motiv.
.17
-.03
.05
PISA math scores

15. 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)

16. Probability to choose STEM career (girls/boys)
Math interest
Math self-concept
.15/.32
Probability to choose STEM career
Math instrumental motiv.
.21/.13
.00/.07
PISA math scores

17. 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?

18. 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 self- concept and math instrumental motivation but increase for math interest
There are gender differences in relationships between constructs. Math self- concept 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