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Journal of Experimental Child Psychology 160 (2017) 92–106
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Journal of Experimental Child

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Programming experience promotes higher STEM
motivation among first-grade girls
0022-0965/� 2017 Elsevier Inc. All rights reserved.

⇑ Corresponding author.
E-mail address: [email protected] (A. Master).
Allison Master a,⇑, Sapna Cheryan a, Adriana Moscatelli b, Andrew N. Meltzoff a
a University of Washington, Seattle, WA 98195, USA
b Play Works Studio, Seattle, WA 98166, USA

a r t i c l e i n f o
Article history:
Received 4 May 2016
Revised 21 March 2017

Social cognition
a b s t r a c t

The gender gap in science, technology, engineering, and math
(STEM) engagement is large and persistent. This gap is significantly
larger in technological fields such as computer science and engi-
neering than in math and science. Gender gaps begin early; young
girls report less interest and self-efficacy in technology compared
with boys in elementary school. In the current study (N = 96), we
assessed 6-year-old children’s stereotypes about STEM fields and
tested an intervention to develop girls’ STEM motivation despite
these stereotypes. First-grade children held stereotypes that boys
were better than girls at robotics and programming but did not hold
these stereotypes about math and science. Girls with stronger
stereotypes about robotics and programming reported lower inter-
est and self-efficacy in these domains. We experimentally tested
whether positive experience with programming robots would lead
to greater interest and self-efficacy among girls despite these
stereotypes. Children were randomly assigned either to a treatment
group that was given experience in programming a robot using a
smartphone or to control groups (no activity or other activity).
Girls given programming experience reported higher technology
interest and self-efficacy compared with girls without this experi-
ence and did not exhibit a significant gender gap relative to boys’
interest and self-efficacy. These findings show that children’s views
mirror current American cultural messages about who excels at
computer science and engineering and show the benefit of provid-
ing young girls with chances to experience technological activities.

� 2017 Elsevier Inc. All rights reserved.

mailto:[email protected]

A. Master et al. / Journal of Experimental Child Psychology 160 (2017) 92–106 93

Women’s underrepresentation in science, technology, engineering, and math (STEM) is a complex
issue. There are large variations in women’s underrepresentation among STEM fields. In 2012, women
earned 59% of bachelor’s degrees in biological sciences, 43% in math and statistics, and 41% in physical
sciences (National Science Foundation, 2015). In contrast, women’s representation was much lower in
technological fields such as computer science (18%) and engineering (19%). This means that many
young women have fewer opportunities to contribute to and benefit from careers in computer science
and engineering. Although many interconnected factors influence the gender gap in participation,
research points to a gender difference in interest that begins early in elementary school (Ceci &
Williams, 2010). Theory-based interventions that increase young girls’ interest and self-efficacy in
technology-related activities have the potential to reduce the gender gap in participation (Cheryan,
Ziegler, Montoya, & Jiang, 2017; Master, Cheryan, & Meltzoff, 2016).

The current study had two aims. First, we examined whether 6-year-old girls and boys have stron-
ger gender stereotypes about computer science and engineering compared with other STEM fields
such as math and science. We examined children’s stereotypes about computer science and engineer-
ing to address two questions: (a) whether 6-year-olds have stereotypes that boys are better than girls
at computer science and engineering (i.e., programming and robotics) and (b) whether 6-year-olds’
gender stereotypes about computer science and engineering are stronger than their gender stereo-
types about math and science. We then examined possible consequences and correlates of gender
stereotypes by assessing the relation between girls’ stereotypes and their motivation in computer
science and engineering.

Second, we examined as the central aim of this study an intervention that targeted girls’ interest
and self-efficacy in computer science and engineering in the face of potential negative stereotypes
about their abilities. We tested whether providing 6-year-old girls and boys a brief experience in pro-
gramming robots can affect girls’ immediate interest and self-efficacy in computer science and
Gender gaps in technology motivation

Gender gaps in older children and adults exist in both STEM interest and self-efficacy, which are
two different but related aspects of motivation (Eccles, 2011; Mantzicopoulos, Patrick, &
Samarapungavan, 2008; Weisgram & Bigler, 2006). There are two types of interest that are relevant
to this study. Situational interest is interest that is triggered within an immediate experience and
may or may not last over time. Individual interest is a persistent inclination to engage with particular
activities over time. The gender difference in individual interest begins by early elementary school,
with girls reporting less interest in and liking for computers compared with boys (Cooper, 2006;
McKenney & Voogt, 2010; Patrick, Mantzicopoulos, & Samarapungavan, 2009). Self-efficacy refers to
confidence in one’s ability to succeed on a specific task (Britner & Pajares, 2006). Girls report less con-
fidence than boys about their science and computing abilities in elementary and middle school
(Beghetto, 2007; Mumtaz, 2001).

Interests in science and technology are largely established by the end of elementary school
(Maltese & Tai, 2010), suggesting the value of intervening at even earlier ages to foster emergence
of these interests. It has been theorized that interest can develop from situational interest to individ-
ual interest (Hidi & Renninger, 2006). We argue that a first step toward increasing women’s individual
interest in computer science and engineering is to trigger young girls’ situational interest in topics
such as robotics. Many types of experiences in formal and informal learning environments, such as
summer camps and conversations with parents in museums, can help to trigger children’s situational
interest in science and technology (Haden, 2010). Efforts by teachers and parents can develop stu-
dents’ interest from situational to individual, for example, by offering new challenges or opportunities.
Once situational interest is triggered with an appropriate task, girls have the opportunity to build this
situational interest into a more durable and strong individual interest (Crowley, Barron, Knutson, &

94 A. Master et al. / Journal of Experimental Child Psychology 160 (2017) 92–106
Martin, 2015). Without this first step of triggered situational interest, girls may be hesitant to begin to
explore this field. Providing new STEM experiences to young girls can also create more opportunities
for them to build self-efficacy in computer science and engineering.

Conceptual framework: Sources of gender gaps in motivation

Why are there early gender gaps in motivation to pursue computer science and engineering? In our
theoretical model, we posit that two interacting sociocultural factors are particularly important in
generating and maintaining the gender gap in technology motivation in young children: (a) cultural
stereotypes and (b) gender differences in experiences (see Fig. 1). (See also Eccles, 2011 for a related
model; for a review of possible biological factors, see Ceci & Williams, 2010; Halpern et al., 2007.)

How stereotypes contribute to the gender gap in motivation.
Girls may be affected by stereotypes about intellectual ability as early as 6 years of age, when they

become less likely than boys to assume that someone who is ‘‘really, really smart” is their own gender
and also start to avoid difficult tasks (Bian, Leslie, & Cimpian, 2017). Do children report gender-related
stereotypes about math and science? From the youngest ages so far tested (kindergarten to second
grade), North American and European children tend to report either that the genders are equal in abil-
ity (Steele, 2003) or that their own-gender group is better at math and science (Galdi, Cadinu, &
Tomasetto, 2014; Heyman & Legare, 2004; Kurtz-Costes, Rowley, Harris-Britt, & Woods, 2008). (For
work on the development of implicit gender stereotypes about STEM and how these relate to explicit
measures, see Cvencek, Meltzoff, & Greenwald, 2011; Cvencek, Meltzoff, & Kapur, 2014.) Explicit
stereotypes about math and science appear to emerge later in development. It is not clear from pre-
vious research precisely when girls explicitly endorse the stereotype that boys are better than girls at
math, with some of the discrepancy in age estimates perhaps due to different methods of measuring
stereotypes. Some research indicates that European girls explicitly endorse the stereotype that boys
are better at math by fourth grade (Muzzatti & Agnoli, 2007), although Latin American girls seem to
attribute less ability in math to girls compared with boys at 6 years of age (del Río & Strasser,
2013). However, other research using different methods suggests that European girls do not explicitly
endorse this stereotype until adolescence (Martinot & Désert, 2007; Passolunghi et al., 2014).

What about gender stereotypes in STEM fields such as computer science and engineering? No study
yet has systematically measured young children’s stereotypes across a variety of STEM fields; that is
one of the aims and novel contributions of the current research. This question is of particular relevance
because variations in adults’ masculine stereotypes about STEM fields correspond to women’s actual
representation in those fields (Cheryan et al., 2017; Leslie et al., 2015). Do girls as young as 6 years
differentiate among different STEM fields as adults do? Do they show gender stereotypes favoring
boys over girls for the most highly stereotyped fields (programming and robotics)?

Gender stereotypes have negative consequences for girls’ performance in STEM, a phenomenon
known as ‘‘stereotype threat” (Flore & Wicherts, 2015; Régner et al., 2014), and for adults’ motivation
Fig. 1. Cultural stereotypes and gender differences in early experiences contribute to gender differences in motivation in
computer science and engineering. These compound over time to lead to a participation gap in computer science and
engineering as boys gain more experience, interest, and self-efficacy than girls in technological fields.

A. Master et al. / Journal of Experimental Child Psychology 160 (2017) 92–106 95
in STEM (Thoman, Smith, Brown, Chase, & Lee, 2013). The prevalence of STEM–gender stereotypes
may be an important social factor influencing girls’ interest in STEM (Kessels, 2015; Master et al.,
2016). Stereotypes about STEM may act as ‘‘gatekeepers” and deter girls from pursuing interests in
computer science and engineering (Cheryan, Master, & Meltzoff, 2015). If children hold stereotypes
that boys are better than girls at computer science and engineering, girls may anticipate doing poorly
and be deterred from related activities.

How experiences contribute to the gender gap in motivation.
Another possible reason why girls may show lower motivation than boys for computer science and

engineering is because they have fewer experiences with technology to generate their interest and
build self-efficacy (Barker & Aspray, 2006; Martin & Dinella, 2002). As early as elementary school, girls
spend less time playing with computer games and technological toys (Cherney & London, 2006) and
are less likely to play with spatial and science-related games and toys than boys (Jirout & Newcombe,
2015). By sixth grade, boys spend more time than girls playing with electric toys and fuses outside of
school (Jones, Howe, & Rua, 2000). Young boys spend more time interacting with age-appropriate
technology activities, which could give them more opportunities to gain self-efficacy (Nugent et al.,
2010; Terlecki & Newcombe, 2005).

Girls’ insufficient early experience with computer science and engineering may contribute to gen-
der gaps in later participation (Cheryan et al., 2017). States and countries that require both girls and
boys to take more STEM coursework have lower gender gaps in STEM participation in college (Charles
& Bradley, 2009; see also Federman, 2007). Correlational research with older students shows that
stronger math and science curricula are correlated with high school girls’ intentions to major in STEM
fields (Legewie & DiPrete, 2014).

Goals of the current research

The current work investigated three interrelated questions: (a) whether 6-year-old children hold
stronger gender stereotypes about computer science and engineering (programming and robotics)
compared with math and science, (b) whether girls who believe that boys are better than girls at com-
puter science and engineering report lower motivation for these subjects, and (c) whether girls in a
treatment group who experience a child-friendly robot programming activity show higher technology
motivation than girls in control groups.

We hypothesized that 6-year-old children would hold stereotypes that boys are better at robotics
and programming and that these stereotypes would be stronger than stereotypes about math and
science. We also predicted that girls’ stereotypes that boys are better at robotics and programming
would correlate with lower motivation for these domains. Finally, and most importantly, we predicted
that girls who were randomly assigned to the treatment group would report significantly higher moti-
vation than girls in the control groups. We also predicted there would be fewer gender differences in
motivation for children in the treatment group than children in the control groups. We did not expect
that our specific treatment would influence the cultural stereotypes that children held because it was
not designed to do so; rather, we expected that the treatment would result in higher technology inter-
est and self-efficacy for girls in the treatment group compared with the control groups.


Participants were 96 6-year-old children (48 girls and 48 boys; Mage = 6 years 10 months, range = 6
years 8 months to 6 years 11 months; 79% White, 3% Asian American, 1% Black, 1% Latino, 1% other,
and 15% multiple ethnicities). Most were middle or upper-middle class (93% of mothers were college
graduates). No participants were excluded from analyses. Conditions were balanced across child gen-
der and experimenter gender using stratified random sampling to condition (the experimenter was
male for half of the participants and female for the other half). Preliminary analyses confirmed that

96 A. Master et al. / Journal of Experimental Child Psychology 160 (2017) 92–106
the random assignment worked as expected and that conditions did not differ in age, family income,
mother’s education level, or minutes per day that children spent using devices such as smartphones,
computers, and video games.


Children were tested individually in the laboratory. Children were randomly assigned to one of
three independent groups: (a) the ‘‘robot” experimental treatment group, (b) a control group that
completed a parallel ‘‘storytelling” activity not involving technology, and (c) a ‘‘no-activity” control
group. All children then responded to measures of technology motivation and STEM–gender

Robot treatment group
Children who were randomly assigned to this group spent 20 min playing a game in which they

chose a specially designed ‘‘pet” robot and used a smartphone to program the robot. Smartphones
are mobile devices that include all the features of a phone as well as features like touch-screen capa-
bilities. Past research indicates that even very young children can learn to program (Kazakoff & Bers,
2014; Wyeth, 2008) and use robots (Bers et al., 2014; Mioduser & Levy, 2010). The goal was to make
the robot navigate an experimentally specified spatial path made out of hexagonal tiles that could be
laid out in different spatial designs (see Fig. 2). Children used drag-and-drop visual programming to
program the robot to move forward, turn left or right, and create loops to repeat instructions. The
researcher demonstrated how to program the robot to navigate four different spatial paths, and then
children programmed the robot to navigate up to eight additional paths. The eight additional paths
Fig. 2. Six-year-old girls in the robot treatment group: (A and C) programming a robot animal using a smartphone to move
along different spatial paths; (B and D) watching the robot execute the programmed commands. (The authors received signed
consent for the experimenter and children’s likenesses to be published in this article.) Links to movies are available here: and

A. Master et al. / Journal of Experimental Child Psychology 160 (2017) 92–106 97
required children to generalize what they had learned during the practice paths. The mean number of
additional paths children completed within 20 min was 6.41 (SD = 1.34).

Children had no difficulty with the phone itself, which is unsurprising given that children’s toys
involving mobile apps and technology are becoming increasingly common (Montgomery, 2015). In
a parental survey (completed by 80% of participating families), 100% reported owning either a smart-
phone or tablet computer. Other research indicates that 75% of families with children age 8 years and
under have access to a smart device (including smartphones and tablet computers) at home and that
83% of 5- to 8-year-olds have used a mobile device at some point (Rideout, 2013). The researcher pro-
vided assistance as needed (see online supplementary material for more details).

Two control groups
Two control groups were used: ‘‘storytelling” and ‘‘no activity.” In the storytelling control group,

children spent 20 min playing a storytelling card game (adapted from the card game ‘‘Once Upon a
Time”) where they were given a series of cards with a person, an object, or an idea and were asked
to tell a brief story involving those cards. This group helped to control for the experience of playing
a sequential game with the researcher. The researcher demonstrated how to tell a story using four sets
of picture cards that were arranged on the table, and then children told their own stories for eight
additional sets of cards (all children completed all eight sets within 20 min). In the no-activity control
group, children did not play any games. We did not expect any differences between control groups on
outcome measures, but combining both controls provides the most rigorous comparison against the
experimental treatment.

Dependent measures
Practice items. To help children get used to the scales assessing interest and self-efficacy, children first
responded to two practice items. These items were designed to introduce children to the positive and
negative dimensions of the scale. Each item was asked in two steps (known as ‘‘branching”; Krosnick &
Presser, 2010) to keep the number of choices simple and age appropriate (Master et al., 2017, 2012). In
the first step, to familiarize children with the positive side of the scale, we asked children whether
playing outside is fun or not fun, accompanied by a card with one smiling face and one frowning face.
Depending on their choice, in a second step they were asked how much it was fun or how much it was
not fun—a little, medium, or a lot—with a second card showing faces with three sizes of smiles (or
frowns). To familiarize children with the negative side of the scale, we asked children whether getting
hurt is fun or not fun and then how much it was or was not fun. The steps were combined to create a
6-point scale with three positive values and three negative values.

Technology motivation. We measured technology motivation with three items assessing interest in
programming (how fun is programming), interest in robots (how fun are robots), and self-efficacy with
robots (how good are you with robots), all measured in two steps to create a scale from 1 to 6, as
described above. Items were adapted from other scales assessing young children’s interest and liking
for math and science (Arnold et al., 2002; Mantzicopoulos et al., 2008). We defined programming for
all children by saying, ‘‘Programming is when you tell a computer or a robot or a phone what to do.”

STEM–gender stereotypes. We measured explicit stereotypes about whether boys or girls are ‘‘better”
at robots and programming (as well as science and math for comparison)—for example, ‘‘Who is better
at programming, girls or boys? Are girls/boys a little better or a lot better?” We coded each of these on
a 4-point scale so that higher numbers indicated belief that boys are better and a score of 0 reflected
chance responses if children were equally likely to choose boys and girls as better. We purposely asked
this question comparatively, rather than asking children to evaluate girls and boys separately, to help
highlight any contrasts between the genders (Heyman & Legare, 2004; Kurtz-Costes et al., 2014). We
did not offer children a neutral option because children may default to neutral response options with-
out fully considering their answer, leading to less reliable responses (Borgers, Hox, & Sikkel, 2004).1
1 We also measured children’s spatial cognition (see supplementary material for details).

98 A. Master et al. / Journal of Experimental Child Psychology 160 (2017) 92–106

Preliminary analyses

Table 1 provides the correlations across measures. We focus on the programming–gender and

robotics–gender stereotypes to highlight the stereotypes most relevant to technology motivation.
(For means and standard deviations for all four stereotypes, see Table 3 below.)
Control groups
As expected, results showed that the two control groups did not differ for any of the technology

motivation items, ts < 1.23, ps > .22, ds < .31, so we collapsed across control groups for analyses. Effect of treatment on technology motivation Because the three technology motivation items were moderately correlated (average inter-item correlation = .20), we analyzed them using a multivariate analysis of variance (MANOVA). A 2 � 2 (Gender � Group [robot treatment or controls]) MANOVA on technology motivation revealed signifi- cant main effects of both gender, Pillai’s trace = .19, F(3,88) = 6.82, p < .001, gp 2 = .19, and group, Pillai’s Table 1 Correlations among dependent measures by gender. Measure 1 2 3 4 5 1. Programming interest – .12 .23 .32* .27 2. Robot interest �.23 – .15 �.05 .15 3. Robot self-efficacy .05 .51*** – .15 .36* 4. Programming–gender stereotype �.37* �.03 �.29* – .26 5. Robotics–gender stereotype .12 �.37** �.32* .02 – Note. Correlations for girls (n = 48) are presented below the diagonal, and correlations for boys (n = 48) are presented above the diagonal. Stereotypes were scored such that positive scores indicated the stereotype that boys were better and negative scores indicated that girls were better. * p � .05. ** p � .01. *** p � .001. Table 2 Technology motivation by experimental condition and gender. Controls Measure Robot treatment Combined Storytelling No activity d Programming interest Girls 5.00 (1.41) 3.88 (1.66) 3.69 (1.82) 4.06 (1.53) .73 Boys 5.60 (0.63) 5.00 (1.24) 5.07 (1.03) 4.94 (1.44) .61 Overall 5.29 (1.13) 4.43 (1.56) 4.35 (1.62) 4.50 (1.52) .63 Robot interest Girls 5.06 (1.24) 4.44 (1.70) 4.87 (1.54) 4.00 (1.79) .42 Boys 5.87 (0.35) 5.19 (1.17) 5.07 (1.34) 5.31 (1.01) .78 Overall 5.45 (0.99) 4.81 (1.50) 4.97 (1.43) 4.66 (1.58) .50 Robot self-efficacy Girls 4.88 (0.96) 3.59 (1.97) 3.88 (1.86) 3.31 (2.09) .83 Boys 5.13 (0.74) 4.81 (1.25) 5.07 (0.88) 4.56 (1.50) .32 Overall 5.00 (0.86) 4.19 (1.75) 4.45 (1.57) 3.94 (1.90) .59 Note. Means (and standard deviations) on a scale from 1 (really not) to 6 (really) are shown. Effect sizes correspond to the difference between the robot treatment and the combined control groups. Table 3 Descriptive statistics for STEM-Gender stereotypes. Overall Girls Boys Field M SD M SD M SD Robots .73*** .88 .81a .90 .65a .85 Programming .24* .96 .04b .97 .46c .92 Science .08 .88 �.16d .87 .33e .82 Math .07 .93 �.38f .73 .54 g .89 Note. Range = �1.5 (girls a lot better) to 1.5 (boys a lot better). Means for each stereotype sharing a common subscript are not statistically different at p � .05. Overall significantly different from 0: * p < .05; *** p < .001. A. Master et al. / Journal of Experimental Child Psychology 160 (2017) 92–106 99 trace = .18, F(3,88) = 6.22, p = .001, gp 2 = .18, and a nonsignificant interaction, Pillai’s trace = .04, F (3,88) = 1.06, p = .37, gp 2 = .035; see Table 2 for means and standard deviations and Fig. 3. Boys reported significantly higher motivation than girls for all three items: (a) programming interest, p = .005, d = .67, (b) robot interest, p = .008, d = .58, and (c) robot self-efficacy, p = .023, d = .60. Children who experienced the robot treatment reported significantly higher motivation than children in the controls for all three items: (a) programming interest, p = .005, d = .63, (b) robot interest, p = .027, d = .50, and (c) robot self-efficacy, p = .013, d = .59. The lack of interaction indicates that the size of the treatment effect was not significantly different for boys and girls for any item, ps = .38, .93, and .14 and gp 2s = .008, < .001, and .025, respectively. Because there were a priori hypotheses, we also examined the simple effects as a function of gen- der. We expected that (a) girls who experienced the robot treatment would report significantly higher motivation than girls in the control conditions and (b) the robot experience would eliminate signifi- cant gender differences in motivation. As predicted, we found that girls who experienced the robot treatment had significantly higher technology motivation compared with girls in the controls, Pillai’s trace = .16, F(3,88) = 5.47, p = .002, gp 2 = .16. Girls who experienced the robot treatment reported significantly higher motivation than girls in the controls for two of the three items: programming interest, p = .008, d = .73, and robot self-efficacy, p = .005, d = .83, but not robot interest, p = .12, d = .42. The robot treatment did not signif- icantly affect boys’ motivation, Pillai’s trace = .06, F(3,88) = 1.90, p = .14, gp 2 = .06, which appeared to be close to ceiling on this scale (� 4.8 on the 6-point scale) and was nonsignificant for all three items, ps = .17, .11, and .47 and ds = .61, .78, and .32, respectively. Fig. 3. Technology motivation: (A) programming interest; (B) robot interest; (C) robot self-efficacy. All were measured on a scale from 1 (really not fun/good) to 6 (really fun/good). Error bars show standard errors. Brackets show pairwise comparisons. Girls in the control groups showed particularly low technology motivation compared with boys and with girls in the robot treatment group. *p < .05; **p < .01; ***p < .001. 100 A. Master et al. / Journal of Experimental Child Psychology 160 (2017) 92–106 Also as predicted, the gender difference (with boys showing more technology motivation than girls) was significant in the controls, Pillai’s trace = .22, F(3,88) = 8.23, p < .001, gp 2 = .22, but not in the robot treatment group, Pillai’s trace = .06, F(3,88) = 1.84, p = .15, gp 2 = .06. For children who expe- rienced the robot treatment, the gender difference was not significant for any of the three items, ps = .22, .09, and .62 and ds = .30, .82, and .30, respectively. In the control groups, boys reported signif- icantly higher motivation than girls for all three items: (a) programming interest, p = .001, d = .79, (b) robot interest, p = .025, d = .52, and (c) robot self-efficacy, p = .001, d = .74. For simple effects using univariate tests, see Table 2 and Fig. 3.2 We also repeated these analyses controlling for children’s spatial …

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