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Running head: Technology-Based Activities
and STEM School Achievement 1
Technology-Based Activities at Home and STEM School Achievement: The Moderating
Effects of Student Gender and Parental Education
Josip Burušića*, Mara Šimunovića, and Marija Šakić Velića
aIvo Pilar Institute of Social Sciences, Zagreb, Croatia
*Corresponding author:
Josip Burušić
Ivo Pilar Institute of Social Sciences
Marulićev trg 19/1
10 000 Zagreb, Croatia
Phone: +385 1 4886 832
E-mail: josip.burusic@pilar.hr
ORCID https://orcid.org/0000-0002-0933-2189
Mara Šimunović Marija Šakić Velić
Ivo Pilar Institute of Social Sciences Ivo Pilar Institute of Social Sciences
Marulićev trg 19/1 Marulićev trg 19/1
10 000 Zagreb, Croatia 10 000 Zagreb, Croatia
Phone: +385 1 4886 152 Phone: +385 1 4886 829
E-mail: mara.simunovic@pilar.hr E-mail: marija.sakic@pilar.hr
Running head: Technology-Based Activities
and STEM School Achievement 2
Technology-Based Activities at Home and STEM School Achievement: The Moderating
Effects of Student Gender and Parental Education
Background: STEM school achievement is related to various student, parent and family
characteristics, and experiences in the school setting. Studies also acknowledge the role of
informal learning in STEM achievement, mentioning the importance of student digital
activities at home. However, the existing studies have not examined in detail the relationship
between students’ engagement in specific technology-based activities at home and STEM
achievement, and whether this relationship varies depending on student gender and parental
education.
Purpose: This study explores the relationship between students’ engagement in technology-
based activities at home and STEM school achievement, and the possible moderating role of
gender and parental education in this relationship.
Sample: Participants in the study are 1,205 students (580 girls) attending fifth (n = 567) and
sixth grades (n = 638) in 16 primary schools in Zagreb, Croatia. For each student, one of their
parents also participated in the study, by completing questionnaires for parents.
Methods: Students completed questionnaires containing questions assessing the frequency of
various technology-based activities at home, and their STEM school achievement was
assessed by objective paper-pencil tests.
Results: The frequency of writing posts on social media sites and downloading music, movies,
games or software from the internet negatively predicted STEM achievement, even after
controlling for parental education and student gender. The relationship between frequency of
certain technology-based activities at home and STEM achievement was found to vary
depending on student gender and the combination of student gender and parental education.
Conclusions: Although the frequency of certain activities at home is related to STEM
achievement, the general conclusion is that engagement in technology-based activities at home
does not substantially contribute to STEM achievement. Additionally, the relationship between
technology-based activities and STEM achievement may differ for boys and girls, and for
students with parents of different education levels.
Keywords: STEM; school achievement; technology-based activities; primary school
Running head: Technology-Based Activities
and STEM School Achievement 3
Introduction
The acronym STEM was formulated by the American National Science Foundation in
2001, replacing the previously used term SMET, as a part of efforts aimed at addressing the
diminishing interest among young people in studying subjects and choosing careers in the
fields of science, technology, engineering and mathematics (Breiner et al. 2012; Dugger
2010). In an attempt to identify practices that could foster students’ motivation in this field
and help to increase the supply of STEM-related skills in the labour market, contemporary
research needs to address different social, cultural, economic, psychological and educational
factors that can explain students’ achievement and motivation in the STEM school domain.
This study aims to address two important issues that have not been broadly explored in
the STEM school domain. The first issue is to what extent students’ school achievement in
STEM school subjects is related to their engagement in different technology-based activities
at home; the second is whether these relations differ for students from families of different
socioeconomic status (SES) and whether they differ for boys and girls. The term ´technology-
based activities´ in this study refers to different types of students’ free-time activities at home
that can be supported by different technologies (e.g. mobile phones, laptops, desktop
computers) (Bennett and Maton 2010).
Students' STEM achievement in school is often attributed to the levels of their
engagement, motivation or interest for STEM school subjects (Eccles and Wigfield 2002), the
level of their positive self-competence beliefs in the STEM domain also playing an important
role (Simpkins, Davis-Kean, and Eccles 2006).
Family context plays a part in STEM school achievement as well, whereby students
from families with lower SES are less likely to achieve highly in STEM subjects (OECD
2007) and, in addition, less often pursue pathways to careers in the STEM field (Gorard and
See 2009). Significant SES-related differences in students’ STEM achievement are
Running head: Technology-Based Activities
and STEM School Achievement 4
documented in most countries in the world. For example, using the data from the OECD’s
Programme for International Student Assessment (PISA) in 2003, Martins and Veiga (2010)
found that there was some degree of SES-related inequality in mathematics achievement in all
15 states that were then members of European Union (EU). It was also evident that countries
with higher SES equity tended to show better mathematics achievement on PISA. In
Australia, PISA data from 2003 showed that increases in school SES were associated with
increases in students’ science and mathematics performance (but not with interest in science
and mathematics), regardless of students’ individual SES (Perry and McConney 2010).
Utilizing the data from PISA 2006 in Hong Kong, Sun, Bradley, and Akers (2012) found that
science achievement differences could be explained by both individual and school SES.
Kryst, Kotok, and Bodovski (2015) used the data from the Trends in International
Mathematics and Science Study (TIMSS), collected in five waves between 1995 and 2011 in
five eastern European countries. The results showed that parental education and a composite
of home possessions, as two measures of family SES, were both significantly related to
science achievement across all five countries.
Studies also highlight the issue of gender differences in the STEM domain. For
example, data from the U.S. National Center for Education Statistics showed that girls
achieved higher grades in STEM school subjects (Hill, Corbett, and St. Rose 2010).
Meanwhile, boys tended to outperform girls slightly on standardized mathematics tests
(Halpern et al. 2007). In order to analyse gender differences in mathematics achievement in a
nationally representative sample, Penner and Paret (2008) conducted a study including more
than 18,000 kindergarten students who were enrolled in U.S. public or private schools in
1998–1999. Data were collected at six time points as children progressed from kindergarten to
fifth grade. The results showed that boys were significantly more represented in the upper
extreme of the maths achievement distribution and that this advantage was largest among
Running head: Technology-Based Activities
and STEM School Achievement 5
families with high parental education. It was also suggested that girls were less interested in
STEM careers. A survey of more than 9,000 English pupils aged 10–11 years showed that
even at this young age there were gender differences in children’s identification with science.
Namely, although there was not any difference in the proportion of boys and girls who were
classified as ‘disinterested in science’, there was a greater proportion of boys who reported
strong science aspirations (Archer et al. 2012).
Some authors emphasize the importance of students’ ´parental science capital´ for
success in the STEM school domain (Archer, DeWitt, and Willis 2014). Science capital
encompasses science-related cultural capital, behaviours and practices, and social capital
(Archer et al. 2015). Science-related cultural capital encompasses scientific literacy (i.e.
scientific knowledge, skills and understanding, and the ability to use them in everyday life);
science dispositions/preferences (e.g. the valuing of science in society, attitudes to science and
scientists, perceptions of school science); and symbolic knowledge regarding the
transferability of science qualifications in the labour market (Archer et al. 2015; DeWitt and
Archer 2017). Science-related behaviours and practices include the consumption of science
through different media and participation in different science learning contexts outside school,
while science-related social capital encompasses knowing persons who work in science-
related jobs and/or having parents with science qualifications and having available persons
with whom one may talk about science (Archer et al. 2015). Archer, DeWitt, and Willis
(2014) used both survey and interview data from 37 English boys when they were 10 and 13
years old. They found that a lack of science capital in the family makes it challenging for the
child to sustain and persevere with science aspirations. Furthermore, children with low levels
of science capital showed little awareness of potential careers in the science field. Higher
science capital was more common in middle-class than in working-class families. Another
study including 3,658 English secondary school students aged 11–15 years found a higher
Running head: Technology-Based Activities
and STEM School Achievement 6
level of science capital to be associated with higher levels of cultural capital in the family,
male gender, and having educational and career aspirations related to science. Furthermore,
students with high science capital were shown to be more secure that other people thought of
them as a ‘science person’ and were more confident in their science abilities than students
with medium or low science capital—that is to say, they had higher self-efficacy beliefs
(Archer et al. 2015).
Achievement and aspirations in the STEM school domain are also associated with
different science-related behaviours and practices which occur outside school settings. Today,
informal and non-formal learning are more widely recognized as important for STEM
education and achievement. Meltzoff et al. (2009) point out that children today learn not just
in school, but also at home, in community centres, at museums, zoos and aquariums, as well
as through gaming, digital media and the internet. A recent study which included seventy
families with children aged 0–8 years from six European countries and Russia confirmed that
in contemporary society children are growing up in media-rich homes, being in daily contact
with a variety of digital tools such as smartphones, tablets and computers (Chaudron 2015).
Many characteristics of new media, such as the two-way nature of interactions, opportunities
for multimodal engagement and a networked environment, resonate with the idea of informal
learning (National Research Council 2009). Greenfield (2009) notes that technology-based
informal learning environments can stimulate the development of the visual-spatial skills
important for scientific thinking and discovery. However, because of their nature as real-time
media, these environments can also be counterproductive for higher-order cognitive processes
such as reflection, inductive analysis and critical thinking.
As stated, the first aim of the present study is to examine the relationship between
students’ engagement in different technology-based activities at home and their STEM school
achievement.
Running head: Technology-Based Activities
and STEM School Achievement 7
Many studies have explored the relationship between technology use and general
school achievement (e.g. Jackson et al. 2008; Koivusilta, Lintonen, and Rimpelä 2007) and
performance on cognitive tests (Hofferth and Moon 2012; Subrahmanyam and Greenfield
2008), while studies linking technology use at home with performance in specific domains
such as the STEM domain are sparse (Notten and Kraaykamp 2009). However, engagement in
technology-based activities may have different effects in different achievement domains. For
example, there is some evidence that video-game playing, which is generally linked to poorer
academic performance, may increase spatial abilities. Green and Bavelier (2007) conducted
two experiments with American university students. In the first experiment, on the basis of the
prior interview, twenty students were placed into one of two groups: action video game
players or non-action video game players. Spatial resolution of visual processing was
determined as the smallest distance a distractor could be from a target without compromising
target identification. Compared with non-players, players could tolerate significantly smaller
distances. In the second experiment, similar effects were observed in non-players who were
trained on an action video game. This result confirmed the causative relationship between
video-game playing and enhanced spatial resolution. It is also known that spatial abilities are
important in scientific reasoning (Trickett and Trafton 2007) and for success in STEM-related
careers (Webb, Lubinski, and Benbow 2007). Moreover, the findings on the relationship
between children’s technology use and their general school achievement are mixed. Early
research focused primarily on children’s use of computers and different educational
applications. Some of these studies suggested that computer use at home is associated with
better academic achievement. Attewell, Battle, and Suazo-Garcia (2003) collected time-diary
data on 1,680 children aged 4 to 13. After controlling for family background, children who
used computers at home had higher scores on measures of letter-word recognition, reading
comprehension and mathematics calculation problems than children who did not use
Running head: Technology-Based Activities
and STEM School Achievement 8
computers. Using classroom surveys and telephone interviews, Borzekowski and Robinson
(2005) collected data from 410 third graders and their parents in northern California. It was
shown that home computer access and use were positively associated with students’ scores on
standardized tests of mathematics, reading and language arts. However, other authors arrived
at contradictory conclusions. For example, Vigdor, Ladd, and Martinez (2014) studied
administrative data covering the population of North Carolina public school students between
2000 and 2005. They found that the addition of a computer to the home environment was
associated with negative impacts on students’ mathematics and reading test scores.
Investigations into the use of new media also reported mixed results. Rideout, Foehr, and
Roberts (2010) used a large national sample of more than 2,000 children aged 8 to 18 from
across the United States. They found that children who were heavy media users were more
likely to have fair or poor grades in comparison to children who were moderate or light media
users. A survey study including 515 children in the United States with an average age of 12
years found that the length of time spent using the internet positively predicted academic
achievement (Jackson et al. 2008). Furthermore, there is much controversy on the issue of
school achievement and students’ use of social networking sites such as Facebook or Twitter.
For example, a survey study including 114 undergraduate and graduate students reported
Facebook use was related to lower GPA, probably due to less time dedicated to studying
among students who are heavily engaged with social media (Karpinski and Duberstein 2009).
However, in cross-sectional studies, plausible explanation of these relations may also be in the
opposite direction—that is, it may be that students who are academically struggling choose to
engage more often in interactions on social network sites. On the other hand, some authors
note that these sites may in certain cases have a positive effect on students’ learning. For
example, students may use these outlets to work with their peers on homework and group
projects or to exchange ideas about school assignments (Boyd 2008). Lei (2010) notes that
Running head: Technology-Based Activities
and STEM School Achievement 9
two problems in existing research are contributing to inconsistent findings on the relationship
between technology use and students’ academic outcomes. Firstly, studies do not distinguish
between types of technologies, such as specific hardware and software; and secondly, most
studies focus on quantity of use, while neglecting quality, or how the technology is used. To
obtain an in-depth understanding of how technology is used among students, Lei (2010)
interviewed nine teachers and nine students from a north-western middle school in the U.S.
Based on these data, 28 specific technology uses were listed in a survey and students had to
indicate how often they worked with each of these technology uses. By examining both the
quantity and quality of technology use among 133 students, Lei (2010) found that time spent
on computers was not related to students’ academic achievement, but an association was
identified when specific types of technology use were taken into account: social
communication technology use was positively associated with school achievement, while a
negative association was found for entertainment and exploration technology use. It is clear
that more research is needed in this area, with a focus on specific technology-based activities
children participate in and their relationship with school achievement in specific domains. In
line with this, in the present study, students’ overall quantity of engagement in technology-
based activities at home is considered as well as their frequency of engagement in specific
technology-based activities and their relationship with school achievement in the STEM
domain.
The second aim of this study is to explore the moderating effects of students’ gender
and parental education on the relationship between engagement in different technology-based
activities at home and STEM school achievement. Within this aim, we will also examine the
differences in engagement in technology-based activities at home related to students’ gender
and parental education. In the present study, parental education is used as the indicator of
family SES, since it has been proved that as an indicator of SES it has a direct, positive and
Running head: Technology-Based Activities
and STEM School Achievement 10
long-term influence on a child’s academic achievement (Davis-Kean 2005; Dubow, Boxer,
and Huesmann 2009).
Studies reveal important variations in the nature of technology use based on age,
gender and SES (Jackson et al. 2008; Koivusilta, Lintonen, and Rimpelä 2007). Findings
suggest that school-aged boys and girls utilize technology for somewhat different purposes
(Johnson 2011; Lee and Chae 2007). Previous research has consistently shown that girls
report playing videogames less often than boys and use communication technology more than
boys (Witt, Massman, and Jackson 2011). Moreover, it seems that these gender differences are
dominant in school-aged children, while patterns of technology use among boys and girls
aged six and under are quite similar (Rideout and Hamel 2006).
As for potential different effects of technology use on the academic achievement of
boys and girls, there is little research on whether females and males receive equal academic
benefit from engaging in different technology-based activities at home. An early report from
Attewell and Battle (1999), which used data from the National Educational Longitudinal
Survey, suggested that among eighth grade children, male students showed greater benefit in
school test scores from having a home computer than did girls. On the other hand, if we take
into account newer findings on gender differences in the nature of technology use, such as the
nature of preferred online activities, girls can maybe benefit educationally more than boys,
since their online activities are more oriented towards accomplishment versus recreation
(Johnson 2011).
Studies have also highlighted the role of family SES in the nature of a child’s
technology use. Analyses of a survey of 749 Dutch adolescents aged 13–18 showed that
adolescents with better socio-economic background used the internet more for information
than their peers with fewer socio-economic resources, while the result was the reverse for
entertainment use (Peter and Valkenburg 2006). Koivusilta, Lintonen, and Rimpelä (2007)
Running head: Technology-Based Activities
and STEM School Achievement 11
collected data by mailed survey from a nationally representative sample of 7,292 12 to 18
year olds in Finland. They found that children whose fathers had a high level of education
played video games less often than children whose fathers had a middle level of education.
EU Kids Online, a thematic network funded by the European Commission’s Safer Internet
Programme, examined research carried out in 21 EU states on how children and young people
use the internet and new media. It was found that children from lower SES families are more
exposed to online risks such as violent content, invasion of privacy or cyberbullying
(Hasebrink et al. 2009).
Furthermore, previous findings have suggested that potential educational gains or
negative effects of children’s technology use may vary in terms of family SES. In their study,
Vigdor, Ladd, and Martinez (2014) found that the negative relationship between the increased
availability of high speed internet and students’ school achievement was more pronounced in
socially disadvantaged families. The authors argue that this may be due to less effective
parental monitoring in households with socioeconomic disadvantages, which leads to
children’s less productive use of technology. In line with this assumption, a study of 1,511 UK
children aged 12–17 years and 906 parents found that higher-SES parents implemented more
rules and practices in their mediation of their children’s internet use (Livingstone and Helsper
2008). Moreover, it can be expected that in more socio-economically advantaged families,
parents are more able to model and instruct their children how to use technology and online
resources effectively for educational and school purposes (Vigdor, Ladd, and Martinez 2014).
The potential moderating effects of gender and parental education are important in the
context of STEM school achievement, since these results may suggest whether students’
technology use experiences can exacerbate or reduce the already existing gaps in STEM
school achievement between students from low and high SES families and lead to the insights
needed to understand better the existing gender differences in the STEM domain. Moreover,
Running head: Technology-Based Activities
and STEM School Achievement 12
insight into the diversity of students’ home experience with technology in different sub-groups
based on factors such as gender or family SES may have further implications for the transfer
of everyday technology-based activities in academic contexts (Bennett and Maton 2010).
Method
Participants
Participants in the study are 1,205 students (580 girls) attending fifth (n = 567) and
sixth grades (n = 638) in 16 primary schools in Croatia. The schools are located in Zagreb, the
Croatian capital and largest city, and its surroundings, representing mainly an urban and
suburban population. The mean age of participants is M = 12.15 years (SD = 0.61 years, TR =
10.34–15.42 years). For each student, one of their parents also participated in the study. Data
were obtained from a total of 1,072 parents (818 mothers, 205 fathers and 49 parents with
missing data on their gender). Students’ and parents’ data were collected as a part of a
comprehensive longitudinal and experimental research project aiming to investigate STEM
career aspirations during primary schooling and to relate STEM achievement to self-
competence beliefs and career interests in boys and girls from families with different
behaviours and SES (JOBSTEM project).
Measures
Frequency of students’ technology-based activities at home. Students were asked to
assess how often they engaged in eight different technology-based activities when they were
at home: (1) sending and reading e-mails; (2) communicating through Viber, Instagram,
Snapchat or similar applications; (3) playing online games; (4) using online dictionaries or
encyclopaedias, such as Wikipedia and Wiki; (5) studying by browsing for the necessary
information and knowledge on the internet; (6) watching online video clips—for example, on
YouTube and similar web sites; (7) downloading music, movies, games or software from the
internet; and (8) writing posts on social media sites, such as Facebook or Twitter. Students
Running head: Technology-Based Activities
and STEM School Achievement 13
answered on a seven-point scale that included the following points: 1 = ´never´, 2 = ´almost
never´, 3 = ´less than once a week´, 4 = ´once a week´, 5 = ´a few times a week´, 6 = ´almost
every day´, 7 = ´every day´. In order to assess the overall quantity of students’ engagement in
technology-based activities at home, a composite score was formed by calculating the average
score on all eight items. The Cronbach’s alpha for the complete scale was α = .78, indicating
acceptable reliability.
It is important to note that 119 students (9.9%) reported that there was no computer in
their household which they could use for the school purposes. Only 37 (3.1%) students
reported that they did not have internet connection in their household. However, since some of
the examined technology-based activities are accessible via mobile internet, all the children
who completed the questionnaire were included in the analyses.
STEM school achievement. Objective tests of achievement in the STEM domain were
used as a measure of STEM school achievement. The tests were constructed following a
standard procedure for the construction of objective achievement tests (Glasnović Gracin et
al. 2018). The first step in the development of tests was to obtain catalogues of STEM
learning outcomes for grades four, five and six by analysing the current Croatian primary
school national curriculum. In Croatia, the STEM school domain in the fifth and sixth grades,
attended by the participants in this study, includes the following school subjects: mathematics,
biology, geography and technical culture. Next, the general test structure was delineated by
planning the proportion of items across four categories: type of activity implied by the item
(i.e. presentation, calculation, interpretation, argumentation or fact knowledge); cognitive
level of the task (i.e. reproduction, making connections or reflection); context of the task (i.e.
no context, realistic context or authentic context); and form of the required answer (i.e.
multiple choice or short answer). All the initial items were discussed with teachers from
different STEM subjects in terms of their content and requirements, and appropriate items
Running head: Technology-Based Activities
and STEM School Achievement 14
were selected from the larger pool. First versions of tests were validated through a pilot study
including 118 students; possible problems in items were discussed and resolved, and final test
versions were constructed. Each test contained 20 items. The main study included 586 fourth,
580 fifth and 632 sixth graders. Using confirmatory and exploratory factor analyses, a
unidimensional structure was obtained for each test. Furthermore, all tests showed acceptable
reliability and discriminative power.
For the purposes of this study, students’ total test scores were transformed into z-
values and centred on the corresponding grade mean. Z-scores for fifth and sixth graders were
then combined into a single scale to form a dependent variable of students’ achievement in the
STEM domain.
Parental education. Parental education level was assessed using two sources of
information: students’ reports on both their mother’s and father’s education level, and parents’
reports. Students’ and parents’ reports were collected within questionnaires developed for the
purposes of the JOBSTEM project. Whenever available, we used parents’ reports as the
primary measure of parental education. When parents’ data were not available, we utilized
students’ reports. This procedure led to a reduction in the missing data on the parental
education variable: 94.5 per cent of the total sample had data for both mother’s and father’s
education level. Parental education level was measured on a scale of pre-coded responses that
included the following levels: 1 = ´unfinished or completed primary school´, 2 = ´completed
high school´, 3 = ´university degree´, 4 = ´graduate degree´. Since only one student’s parent
participated in the study, they reported on both their own and the educational level of the non-
participating parent. Even when a student’s parents were divorced or did not share a common
household, the participating parent was asked to indicate the educational level of the non-
participating parent. The composite score for parental education level was created by
averaging reports for mother’s and father’s education level, and used as an interval variable in
Running head: Technology-Based Activities
and STEM School Achievement 15
the later analyses. If it was stated that the other parent was deceased or that the child had only
one caregiver, the parental education variable was based solely on the educational level of the
parent (caregiver) who completed the survey. The composite parental education variable was
not computed for students who had incomplete data on parental education. The mean of the
parental education variable was 2.44 (SD = 0.62).
Procedure
Students completed a paper-and-pencil questionnaire containing the technology usage
measures in small groups during regular school classes in their own schools. Two weeks later,
the same students were tested in STEM achievement by the objective STEM test. This time
delay was necessary since the technology usage measures were administrated as a part of a
larger questionnaire. Therefore, the overall testing would be too draining on students if it took
place on the same occasion. The period of two weeks was necessary to conclude the first part
of testing in all 16 schools and to start the second part. Parental questionnaires were
forwarded to parents via students. Upon completing the questionnaires, parents sent them
back to school in sealed envelopes, again via their children. From the parental questionnaire,
only parents’ reports on their educational level were utilized in this study.
Results
Firstly, to examine the frequency of students’ technology-based activities at home,
descriptive statistics for all the activities are calculated for the total sample and separately for
boys and girls (Table 1). Girls most frequently engaged in communicating via digital
applications (M = 5.95), followed by watching online video clips (M = 5.46). Among boys,
the most frequent activity was watching online video clips (M = 5.96), followed by playing
online games (M = 5.48). The least frequent activity among both boys (M = 3.29) and girls
(M = 2.87) was sending or reading emails. When the overall quantity of students’ engagement
Running head: Technology-Based Activities
and STEM School Achievement 16
in technology-based activities at home is considered, girls on average engage in these
activities once a week (M = 4.35) and boys a few times a week (M = 4.60).
[Insert Table 1 about here]
Secondly, bivariate correlations are calculated to examine the relations between
frequency of technology-based activities at home, gender, parental education and STEM
school achievement (Table 2). Results show that girls communicate via digital applications
more frequently than boys (r = .18, p <.001.), while boys send and read emails (r = -.11, p
<.001), watch online video clips (r = -.15, p <.001) and play online games at home (r = -.35, p
<.001) more frequently than girls. Boys also have higher scores on the composite measure of
overall quantity of engagement in technology based-activities at home (r = -.10; p < .001).
Parental education was positively related to frequency of online dictionary and
encyclopaedia use (r = .10; p < .001) and frequency of studying by browsing for information
online (r = .08; p < .01), and was negatively related to frequency of writing posts on social
media sites (r = -.11; p < .001).
Among the technology-based activities examined, STEM school achievement was
related to frequency of downloading content from the internet and writing posts on social
media sites. Both of these correlations were negative: r = -.11 and r = -.20, respectively (p
<.001).
[Insert Table 2 about here]
Thirdly, we proceeded to predict students’ STEM school achievement based on the
frequency of technology-based activities at home, while including the possible moderating
Running head: Technology-Based Activities
and STEM School Achievement 17
effects of students’ gender and parental education. In order to do so, we assessed separate
moderated moderation models (Hayes 2013). Also known as ‘three-way interaction’, these
models extend the principles of simple moderation and allow the researcher to test higher-
order interactions. We tested separate moderated moderation models that included the
frequency of each of the eight technology-based activities and the overall quantity of
engagement in these activities. In each model, students’ STEM school achievement was the
outcome variable, the frequency of technology-based activity at home served as a predictor
variable, and students’ gender and parental education served as the moderators (Figure 1). In
this part of the analysis, we used PROCESS macro for SPSS (Hayes 2012), which is based on
a series of ordinary least squares regressions and can estimate simple slopes for the three-way
interactions. We firstly examined two two-way interactions between predictor and two
moderators (frequency of activity × gender and frequency of activity × parental education) in
predicting the frequency of technology-based activities. We further tested the three-way
interaction between these variables (frequency of activity × gender × parental education).
Moderated moderation models permit the testing of three-way interaction because they allow
the interaction between the predictor and the primary moderator to depend on a secondary
moderator (Hayes 2013). In our case, this enabled us to answer not only if there is an SES and
gender difference in the link between frequency of technology-based activities and STEM
achievement, but also to explore if gender differences in the link between frequency of
technology-based activities and STEM achievement are SES dependent. The interaction term
between students’ gender and parental education also had to be included into the model in
order for the test of the three-way interaction to be valid (Hayes 2013), but it is not reported
here due to its theoretical insignificance for this paper. Moreover, this interaction was not
shown to be statistically significant in any of the tested models.
Running head: Technology-Based Activities
and STEM School Achievement 18
[Insert Figure 1 about here]
The results of the analyses displayed in Table 3 show that frequency of downloading
content from the internet and writing posts on social media sites negatively predicted students’
STEM school achievement even after the effects of students’ gender and parental education
were controlled for. The model which included the frequency of downloading content from
the internet explained 13.51 per cent of the variance in STEM school achievement (F (7,
1044) = 26.17, p < .001), and the model with frequency of writing posts on social media sites
explained 14.67 per cent of the variance in STEM school achievement (F (7, 1044) = 27.70, p
< .001). Furthermore, three interaction terms were shown to be significant. Firstly, there was a
significant interaction between gender and the frequency of students’ e-mail correspondence
at home in predicting STEM school achievement (B = .06; t = 2.03; p = .042). We further
tested simple slopes of frequency of email correspondence separately for each gender, while
controlling for parental education. It was shown that the frequency of this activity negatively
predicted STEM school achievement among boys (B = - 0.05; SE = .02; β = - 0.09; t = -2.32;
p = .021), while among girls no relationship was established. Secondly, there was a significant
interaction between students’ gender and the frequency of use of online dictionaries or
encyclopaedias in predicting STEM school achievement (B = .07; t = 2.17; p = .030). It was
shown that among girls, even after controlling for parental education, the frequency of this
activity positively predicted STEM school achievement (B = 0.05; SE = .02; β = 0.09; t =
2.11; p = .036), while among boys no relationship was established.
[Insert Table 3 about here]
Running head: Technology-Based Activities
and STEM School Achievement 19
Finally, results indicated a significant three-way interaction between frequency of
communication via digital applications, student gender and parental education (B = -.14; t =
-2.21; p = .027). To inspect the simple slopes in this interaction, we used the Johnson–
Neyman technique that is embedded in the PROCESS macro. This approach derives regions
of significance for a two-way interaction at values of a third continuous variable (Hayes and
Matthes 2009). We used this technique to determine the value of parental education when
interaction between frequency of communication via digital applications and student’s gender
becomes a significant predictor of STEM school achievement. It was shown that the
conditional effect of frequency of communication via digital applications × gender on STEM
school achievement became significant when parental education reached a mean-centred
value of -0.4 (mean-centred parental education: M = 0, SD= 0.62). Specifically, more frequent
communication via digital applications among girls was associated with better STEM school
achievement, but only at the lower values of parental education. This interaction effect is
illustrated in Figure 2.
[Insert Figure 2 about here]
Discussion
The results of this study show that the frequency of students’ engagement in
technology-based activities at home can be both positively and negatively related to their
school achievement in the STEM domain, depending on the specific type of the endorsed
activity. Moreover, for certain activities, this relationship seems to vary significantly across
students’ gender and family SES.
Engagement in Technology-Based Activities at Home and STEM School Achievement
The outcomes of this study show that the overall quantity of students’ engagement in
technology-based activities at home is not significantly related to their STEM school
Running head: Technology-Based Activities
and STEM School Achievement 20
achievement. However, when specific types of technology-based activities are considered,
significant relationships between engagement in certain activities and STEM school
achievement are found. This outcome supports the conclusions of previous studies which
indicate that when considering the relationship between technology use and students’
academic achievement it is necessary to focus on both the quantity and the quality of
technology use (Lei 2010; Lei and Zhao 2007). In an attempt to explain and understand the
effects of engagement in technology-based activities at home in the context of STEM school
achievement, a limited focus on only the quantity of students’ engagement in such activities
could be misleading if the quality or engagement in different types of activities and their
relationships with STEM school achievement were not considered.
When engagement in specific technology-based activities is considered, the results of
the present study show that students who more frequently write posts on social media sites
such as Facebook or Twitter, and more frequently download music, movies, games or
software from the internet, have poorer STEM school achievement. The study revealed
similar insights regarding the role of social networking sites to those obtained in a recent
meta-analysis by Huang (2018), in which a negative mean correlation was found between
social networking site use and academic achievement. Although posting on social media sites
can be considered a form of online communication (Ahn 2011), it includes less direct
engagement with others than two-way communication in format of messaging or chatting.
Hence it probably provides less possibility of socializing that could produce knowledge-
sharing or joint work on school assignments (Junco 2011).
The negative relationship of the frequency of use of social media sites and
downloading music, movies, games or software from the internet with STEM school
achievement can probably be explained by the pronounced entertainment nature of these
technology-based activities, whereby a greater amount of time spent on such activities
Running head: Technology-Based Activities
and STEM School Achievement 21
possibly interferes with time invested in learning and thus impedes STEM school
achievement. Studies show that greater non-academic use of ICT is negatively related to the
academic achievement of high school students (Salomon and Ben-David Kolikant, 2016).
However, greater use of electronic devices for school purposes is found to be related to better
school achievement (Drain, Grier and Sun 2012). As for internet use, using the internet to
search for information is positively, and using the internet for socializing, gaming and
recreation is negatively, related to subsequent school outcomes (Chen and Fu 2009). In the
context of STEM school achievement, an analysis of the relationship between ICT use and
mathematics achievement based on PISA 2006 data showed that internet/entertainment use
(e.g. browsing the internet for information about people, things or ideas; playing games;
downloading software or music from the internet) and program/software use (e.g. writing
documents, using spreadsheets, writing computer programs) are significant negative
predictors of mathematics achievement (Güzeller and Akin 2014).
The results of this study support the conclusion that in the context of STEM school
achievement, the overall quantity of engagement in technology-based activities neither
supports nor impedes STEM school achievement, but greater engagement in activities with
more pronounced entertainment than educational nature and potential can be related to poorer
school achievement in the STEM domain.
The Moderating Effects of Student Gender and Family SES on the Relationship between
Engagement in Technology-Based Activities at Home and STEM School Achievement
This study demonstrates that the relationship between engagement in certain
technology-based activities at home and STEM school achievement depends on students’
gender, and that girls and boys from families of lower and higher SES differ in their use and
in the benefits derived from specific technology-based activities at home. When considered
from the perspective of STEM school achievement, the outcomes of the study imply that boys
Running head: Technology-Based Activities
and STEM School Achievement 22
and girls, especially if they are growing up in families of different SES, differently and more
or less successfully combine and utilize opportunities related to technology use available in
school and out-of-school settings. This is in accordance with previous studies which indicate
differences in technology usage and school outcomes associated with technology usage
related to gender and family SES (e.g. Warschauer and Matuchniak 2010).
The results of this study show significant differences between boys and girls in the
relationship between STEM school achievement and technology-based activities such as
using online dictionaries or encyclopaedias on the one side and communication through
digital applications on the other. Specifically, more frequent use of online dictionaries or
encyclopaedias was related to better school achievement only among girls. Although we
cannot pose a causal relationship, this result may suggest that girls can benefit more than boys
from such educational technology-facilitated activities in terms of their STEM school
achievement. Some recent studies also imply that girls may derive more educational benefit
from certain types of technology use. Arroyo et al. (2013) found that in comparison to male
students, female students gained more educational benefit from an advanced learning
technology for mathematics, primarily because they used the tutoring system more
productively. In a longitudinal study by Hofferth (2010), greater overall computer use was
positively associated with reading and problem-solving achievement five years later, but only
among girls. The positive relationship between girls’ STEM school achievement and the
frequency of use of online resources may also be connected to gender differences in online
learning and information retrieval behaviours. For example, when using the internet, girls
spend more time reading individual web pages than boys (Large, Beheshti, and Rahman 2002)
and browse entire linked documents more often than boys (Roy and Chi 2003). Thus, girls
probably use dictionaries and encyclopaedias more effectively in terms of finding and
employing content relevant for learning in the STEM domain. Additionally, when interpreting
Running head: Technology-Based Activities
and STEM School Achievement 23
this finding, gender patterns in the STEM domain should be considered. There is empirical
evidence that teachers tend to underestimate the mathematical ability of girls in comparison to
boys (Frome and Eccles 1998; Tiedemann 2002) and that girls receive less attention and
feedback from teachers in science classrooms (She 2001). Furthermore, it has been shown that
girls have lower self-competence beliefs in the STEM domain (Bleeker and Jacobs 2004; Watt
2004), and that parents believe that boys have more ability in STEM courses than girls
(Bhanot and Jovanovic 2009) and provide girls with less STEM-related experiences than boys
(Jacobs and Bleeker 2004; Eccles et al. 1993). These differentiations certainly put female
students at a greater academic disadvantage in the STEM domain, and thus they may have
more educational returns from informal learning through the use of online resources.
The study also revealed a positive relationship between the frequency of
communication on digital applications such as Viber and Instagram and STEM school
achievement, but this effect was moderated by a combination of student gender and parental
education. Our results suggest that only girls from families of lower SES benefit from
communication on digital applications in terms of their STEM school achievement. Previous
studies point to some pathways through which parental education indirectly affects children’s
educational outcomes. Parents’ achievement beliefs, such as educational expectations;
stimulating home behaviours, such as literacy-related practices; and the affective relationship
between parents and children are some of the underlying factors that can mediate the positive
association between family SES and children’s achievement (Davis-Kean 2005; Eccles 2005).
Among students from lower SES backgrounds, access to social networks may compensate for
possible lower educational opportunities in the family (Vekiri 2010). Hence, for girls from
lower SES families, their social networks in the digital world may present a source of
information and support when tackling educational challenges in the STEM domain, thus
compensating for lower ‘science capital’ available in their families. Moreover, it may be that
Running head: Technology-Based Activities
and STEM School Achievement 24
because of potentially less support for learning received at home, these girls more often
engage in the type of digital communication which is course- or school-related than girls from
higher SES families. The finding that this relationship is evident only among girls may be
explained by their greater propensity towards communication, cooperation and group work in
learning (Severiens and Ten Dam 1994; Slavin, Lake, and Groff 2009), which can be assisted
and realized through communication applications.
In the present study, interesting outcomes are found regarding the use of email. The
frequency of email use was negatively associated with STEM school achievement only
among boys. Similarly, Hofferth and Moon (2012) found greater email use to be associated
with reduced vocabulary scores only for boys. In our study, boys reported more frequent use
of email than girls. Due to changes in the technology used for communication purposes, it is
likely that children no longer use email for everyday continuous communication. Further
research needs to examine the specific context in which male students use email and whether
this context can explain the negative relationship with STEM school achievement.
Patterns of Technology-Based Activities in Different Groups of Students
In this study, we also found SES- and gender-related differences in technology use
among students. When patterns of technology-based activities related to gender are
considered, girls communicated via digital applications more frequently than boys, while boys
played online games, watched online video clips, and sent or read emails more frequently than
girls. These findings are in accordance with others’ studies which showed that girls more
frequently use digital applications for communicating (e.g. Jackson et al. 2008), and that boys
more often play online games (Gross 2004) and watch online videos (Rideout, Foehr, and
Roberts 2010). There were no gender differences in the two activities that were related to
poorer STEM school achievement – downloading content from the internet and writing posts
on social media. However, bearing in mind that positive correlations between certain
Running head: Technology-Based Activities
and STEM School Achievement 25
technology based-activities and STEM achievement were found only among girls, our results
suggest that boys’ out-of-school technology use can only impede their STEM educational
performance, while among girls, some forms of technology use can be beneficial in terms of
their STEM school achievement.
We also found that students from higher SES families more frequently use online
dictionaries and encyclopaedias and browse and search for information on the internet, while
the frequency of writing posts on social media sites is negatively associated with family SES.
Hence, it appears that higher family SES is related to students’ more frequent engagement in
activities which have stronger educational potential and less frequent engagement in more
entertainment-oriented activities. It is possible that higher-SES parents’ more advanced
technology-related capabilities and their tendency to use more advanced forms of digital
technology for educational, informational, and communicational purposes are conveyed to
their children (Howard, Rainie, and Jones 2001; Peter and Valkenburg 2006). Better educated
parents may also have more insight into how their children can use technology in a way that
fosters learning, tend to encourage and advise their children to look for information and
knowledge online when tackling their academic tasks and they may be more comfortable with
the use of new technologies in learning (Linebarger, Royer, and Ghernin 2004). Less use of
entertainment-oriented technology (e.g. social media sites) in higher SES families may reflect
more monitoring of children’s online activities and time and established parental online rules
(Livingstone and Helsper 2008).
There are several limitations of this study that should be taken into account when
interpreting the findings. Firstly, the cross-sectional and correlational design of the study
prevents us from establishing the causal relationship between students’ engagement in
technology-based activities at home and their STEM school achievement, and longitudinal
studies are needed to determine the mechanisms of causation. Secondly, there are some
Running head: Technology-Based Activities
and STEM School Achievement 26
limitations in the methods of data collection. Namely, items measuring parents' education
were constructed so that they reflect the education of the child’s biological parents, or
caregivers if the child is adopted. In the case of students living in blended families, the
educational level of the stepparent has not been taken into account, which limits a full insight
into the child's family background.
Implications for Future Studies and Practice
Our findings reveal some interesting relationships between different technology-based
activities at home and STEM school achievement, and certain moderators of these
relationships. However, future research needs to include technology-based activities which are
more specific for STEM information retrieval and learning to assess which of these activities
are beneficial for STEM achievement. Additionally, it would be informative to explore the
specific relationships between different technology-based activities and student school
achievement in various school subject domains in order to obtain empirically-based
recommendations regarding the possibility of employing technology-based activities in
promoting student achievement. In this context, use of technology-based activities in a formal
school setting should also be examined. Also, qualitative data from interviews and
behavioural observations and information from parents are needed to acquire in-depth insights
into the basis of the observed differences and relationships across different technology-based
activities. For example, underlying mechanisms—such as differences in the quality of
technology use between girls and boys, and parental behaviours, including parental
monitoring of the child’s technology use, parental educational involvement and interactions
with the child—need to be further investigated in the context of these research findings.
The findings of this study have certain practical implications and can be used to advise
parents and teachers on how to use technology-based activities to improve students’ STEM
school achievement. Firstly, the results of this study point to the conclusion that the overall
Running head: Technology-Based Activities
and STEM School Achievement 27
amount of time children spend engaged in different technology-based activities is not related
to their achievement in STEM school subjects. However, greater engagement in certain more
entertainment-oriented activities (i.e. posting on social media sites and downloading content
from the internet) is negatively related to STEM school achievement. Hence, parents should
be advised to control the amount of time their children spend on such entertainment-oriented
technology-based activities. Since students from lower-SES families are more prone to using
technology for entertainment purposes, parental monitoring of the nature of children’s
technology use is especially important in lower-SES families. Secondly, our results suggest
that female students, in comparison to male, may derive more educational benefits in the
STEM domain from certain technology-based activities at home, specifically from the use of
online learning resources (e.g. encyclopaedias and dictionaries). Since girls continue to have
less confidence in their performance in mathematics and science (Bleeker and Jacobs 2004)
and less frequently choose STEM careers (Jarman, Blackburn, and Racko 2012; Smith 2011),
parents and teachers should encourage their use of technology for educational purposes.
Thirdly, in the case of girls from lower SES families, more frequent communication through
digital applications is positively related to their achievement in the STEM domain. According
to our results, this technology-based activity is generally more frequent among girls. Hence,
communication through digital applications can be used to convey and share information
regarding school subject obligations and content among girls to encourage and support their
interest, competence beliefs, and participation in STEM.
Running head: Technology-Based Activities
and STEM School Achievement 28
Funding
This work was supported by the Croatian Science Foundation under grant “IP-09-
2014-9250 – STEM career aspirations during primary schooling: A cohort-sequential
longitudinal study of relations between achievement, self-competence beliefs, and career
interests (JOBSTEM)”.
Disclosure statement
The authors confirm that there are no known conflicts of interest associated with this
publication.
Running head: Technology-Based Activities
and STEM School Achievement 29
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Running head: Technology-Based Activities
and STEM School Achievement 39
Tables
Table 1. Descriptive statistics for frequency of students’ technology-based activities at home.
Frequency of technology-
based activities at home
Girls Boys Total Theoretical
range
(Observed
range)
M SD M SD M SD
Sending or reading e-
mails 2.87 1.75 3.29 2.00 3.09 1.90 1–7 (1–7)
Communicating on
digital applications 5.95 1.61 5.29 1.95 5.61 1.82 1–7 (1–7)
Playing online games 4.08 2.03 5.48 1.68 4.80 1.20 1–7 (1–7)
Using online dictionaries
or encyclopaedias 4.17 1.81 4.23 1.92 4.20 1.87 1–7 (1–7)
Studying by browsing for
information online 4.31 1.83 4.46 1.89 4.38 1.90 1–7 (1–7)
Watching online video
clips 5.46 1.85 5.96 1.52 5.72 1.70 1–7 (1–7)
Downloading music,
movies, games, software 4.57 2.10 4.78 2.07 4.68 2.09 1–7 (1–7)
Writing posts on social
media 3.42 2.26 3.29 2.30 3.35 2.28 1–7 (1–7)
Overall engagement in
technology-based
activities
4.35 1.19 4.60 1.23 4.48 1.22 1–7 (1–7)
40
Table 2. Bivariate correlations between frequency of students’ technology-based activities at
home, STEM achievement, gender and parental education.
STEM
achievement GenderaParental
education
Sending or reading e-mails -.03 -.11*** .02
Communicating on digital applications .03 .18*** .01
Playing online games .04 -.35*** -.02
Using online dictionaries or encyclopaedias .06 -.02 .10***
Studying by browsing for information online .01 -.04 .08**
Watching online video clips .04 -.15*** .02
Downloading music, movies, games,
software -.11*** -.05 -.04
Writing posts on social media -.20*** .03 -.11***
Overall engagement in technology-based
activities -.04 -.10*** -.01
a Coded as 1 = boys; 2 = girls.
n ranges from 1106 to 1178.
***p < .001; **p < .01
41
Table 3. Regression results for testing the moderated moderation models of students’ STEM
achievement with frequency of technology-based activities as predictor variables and gender
and parental education as moderators.
Tested Models B SE t p
Sending or reading e-mails -.02 .02 -1.00 .318
Interaction 1 .06 .03 2.03 .042
Interaction 2 .01 .02 0.55 .579
Interaction 3 -.02 .05 -0.45 .652
Communicating on digital applications .02 .02 0.94 .350
Interaction 1 .02 .03 0.58 .560
Interaction 2 -.03 .03 -1.08 .280
Interaction 3 -.14 .06 -2.21 .027
Playing online games .01 .02 0.77 .439
Interaction 1 .01 .03 0.29 .775
Interaction 2 .02 .02 0.93 .355
Interaction 3 -.03 .05 -0.57 .570
Using online dictionaries or encyclopaedias .01 .02 0.79 .427
Interaction 1 .07 .03 2.17 .030
Interaction 2 .03 .02 1.35 .178
Interaction 3 .03 .05 0.61 .544
Studying by browsing for information online -.01 .02 -0.47 .642
Interaction 1 .03 .03 1.00 .313
Interaction 2 .03 .02 1.05 .294
Interaction 3 .07 .05 1.39 .164
Watching online video clips .01 .02 0.29 .770
Interaction 1 -.03 .04 -0.93 .351
Interaction 2 -.02 .03 -0.56 .574
Interaction 3 -.01 .06 -0.10 .921
Downloading music, movies, games, software -.06 .01 -4.00 < .001
Interaction 1 .03 .03 1.07 .284
Interaction 2 .03 .02 1.23 .219
Interaction 3 .03 .04 0.76 .446
Writing posts on social media -.07 .01 -5.41 < .001
Interaction 1 .04 .03 1.73 .084
Interaction 2 .00 .02 -0.03 .975
Interaction 3 -.01 .04 -0.29 .769
Overall engagement in technology-based
activities
-.04 .02 -1.80 .072
Interaction 1 .08 .05 1.66 .097
Interaction 2 .03 .04 0.82 .412
Interaction 3 -.01 .08 -0.11 .914
Note. Each model is adjusted for parental education and child’s gender.
Interaction 1 = frequency of activity × gender; Interaction 2 = frequency of activity × parental education; Interaction
3 = frequency of activity × gender × parental education.
42
Figures
43
44
Figure captions
(1) Figure 1. Conceptual model showing the tested relationship between frequency of
students’ technology-based activities at home and STEM achievement in which gender and
parental education are included as moderating variables.
(2) Figure 2. Three-way interaction effect of frequency of students’ communication via digital
applications × student’s gender in predicting STEM achievement as a function of parental
education. Note: Only slope for girls in the condition of low parental education is statistically
significant (p = .015). Values on the Y-axis are z-scores of students’ results in the objective
test of STEM achievement (Mean = 0).
