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