The Summary

Gendered pathways into and away from STEM are mediated through motivation, but there is
paucity of knowledge regarding gendered patterns in high school students' motivation profiles,
especially in transdisciplinary domains like integrated STEM (iSTEM). This study addresses
these gaps by examining the interconnection between patterns in motivation profiles towards
integrated STEM, gender and STEM test scores

Cluster Analysis

 Using cluster analysis in a sample of N = 755 eighth grade students, we established

four distinct motivation profiles
 Although our analysis shows no difference in average test scores, significant gender
differences can be found in and between motivation profiles.
 For instance, girls are more likely to belong to a less favorable profile cluster than
boys.

Conclusion

 The concept of motivational co-expression emphasizes a need for instructors to move

past the simple high or low motivation labels and toward an appraisal that recognizes
how students adopt a complex interplay of motivation types.
 Moreover, the gender analyses raise questions about how we can move towards more
equitable approaches.

There is a growing shortage of STEM professionals

 According to UNESCO, only 35% of STEM students in higher education globally are
women.
 Women leave STEM disciplines in disproportionate numbers during their studies and
even during their careers (Fernández Polcuch et al., 2018).
 This has important economic and societal consequences.

Motivation and motivation profiles

 Several influential theories have been proposed to explain why individuals choose or
persist in a specific course of action
 According to expectancy-value theory, students are more motivated to achieve in
areas in which they expect to succeed and that they value
 Through the lens of self-determination theory (SDT), Ryan and Deci (2020) specify
three types of motivation (i.e., amotivation, extrinsic motivation, and intrinsic
motivation) arranged along a continuum reflecting the degree to which the regulation
of behavior is internalized
 Each motivation type is defined by unique characteristics such as enjoyment,
meaningfulness, ego involvement, external pressures and will have different
consequences
  More autonomous motivation types lead to better academic achievement, effort and
engagement compared to external type of motivation
 The relationship between distinct motivational variables and achievement is not new
and studies often investigate motivational constructs in isolation using a variable-
centered approach
 A more holistic approach (e.g., motivation profiles) is needed

Gender gap

 Abundant research has shown that gendered pathways into and away from STEM are
mediated through motivation
 Some studies have identified gender differences in STEM self-efficacy that favor men
 Others have found no significant gender differences
 Career pathways encompass the ability to pursue a career as well as the motivation to
employ that ability
 This study aims to bridge research regarding gender and motivation profiles and skill

Integrated STEM

 Most studies regarding motivation focus on'segregated' STEM disciplines (e.g.,

Mathematics or Science).
 The current international focus in STEM education moves towards integrating the
separate STEM disciplines through 'integrated STEM' (iSTEM)
 There are five categories of instructional elements essential for teaching integrated
STEM: (1) the explicit assimilation of learning goals, content and practices from
different STEM disciplines; (2) a problem-centered learning environment that
involves students in authentic, open-ended, ill-structured, real-world problems; (3) an
inquiry-based learning environment with students engaged in questioning,
experimental learning and hands-on activities
 (4) design-based, hands- on design challenges; (5) cooperative learning where
students collaborate to communicate and collaborate

Goals of this study

 Despite abundant research on gender, motivation and ability, there is paucity of

knowledge regarding gendered patterns in high school students' motivation profiles.
 To better understand how motivation profiles in iSTEM relate to STEM test scores
and to identify possible gender differences, the following three research questions
were developed to guide this study:
 What student profiles regarding iSTEM motivation can be identified?
 How do these profiles relate toSTEM test scores?
 To what extent can we distinguish gender differences?

Methods
  To measure the effects of the developed iSTEM teaching modules, researchers
adopted a quasi-experimental design
 Student motivation variables were measured using individual self-report and a
cognitive STEM test
 Both were administered through an online questionnaire
 Current data collection is part of the pre-test at the beginning of the school year
before any intervention took place

Participants

 755 grade eight students enrolled in STEM courses across 28 institutions.

 Students who identified as boys provided a total of 538 responses (71%), and girls
provided 217 responses (29%).
 Information regarding gender, institution and motivation was acquired from the self-
reports of students via an online questionnaire.

Motivation

 Twenty items from the Self-Regulation Questionnaire (SRQ) were adjusted to assess
students' motivation for studying iSTEM
 Three constructs were measured (i.e., amotivation, controlled motivation and
autonomous motivation) based on underlying subscales
 Controlled motivation was composed of the subscales of external regulation (e.g.,
"During STEM classes I do my best because I want others to think I'm smart.") and
introjected regulation
 Autonomous motivation was constructed from a two-level model using the three
constructs and five subscales

Cognitive STEM test

 The instrument was constructed based on the curriculum for Physics, Mathematics,
and technological concepts of seventh and eighth grade
 Items from existing iSTEM instruments were selected by pedagogical and subject
matter experts
 A 23-item multiple choice test was used
 Item characteristics (i.e., difficulty and discrimination) were analyzed
 Analysis of variance (ANOVA) showed that the 2-PL model fitted the data best based
on Akaike Information Criterion (AIC), Bayesian Information Criteria (BIC) and
Log-Likelihood
 Discrimination values for all items were above 0.15, which was in line with our pilot
test

Analyses STEM motivation profiles

  Cluster analysis is used to detect groups of students with similar patterns of variation
across sets of variable characteristics of the observations (Bartholomew et al., 2008;
Sarstedt & Mooi, 2014).
 The identification of homogenous students is in essence a taxonomy analysis.
 While several techniques exist to perform cluster analysis, we opted for a TwoStep
Cluster Analysis (CA) because it has proven successful in earlier research on
motivational profiles concerning eighth graders within the context of Trends in
International Mathematics and Science Study (TIMSS)
 Since cluster analysis techniques are explorative in nature, a different number of
clusters may be extracted and interpreted.

Cognitive STEM test

 A general score was calculated for each student (max score = 10)
 The mean score (µ) was 4.84, with a standard deviation (σ) of 3.3
 Analysis indicated no significant score differences, t(753) = 1.29, p = 0.81
 Skewness (0.08) and kurtosis (2.36) were within an acceptable range, so we can
conclude that test scores follow a normal distribution (see Fig. 2).

iSTEM motivation and test scores

 A model with four clusters was considered most suitable given the statistical criteria
that each separate cluster should not contain fewer than 7% of the total number of
respondents
 Students with cluster profile 4 score significantly lower on the STEM test than
students with cluster profiles 1, cluster profile 2 and even cluster profile 3
 Cluster 1 (n = 193, 26%) reported the highest levels of autonomous motivation (i.e.,
3.60) while also showing the lowest amounts of controlled motivation and
amotivation.
 No significant difference in STEM test score was observed between boys and girls in
this cluster (see Table 2).

The main purpose of this study was to identify gendered patterns in motivation
profiles towards integrated STEM (iSTEM) and to examine how these relate to
STEM test scores.

 The study identified discernible patterns of motivational related variables in grade

eight STEM courses students in Flanders. Four clusters of motivational profiles were
identified (see Fig. 2).
 Cluster 3 accounted for the largest percentage of the sample (27%) while cluster 4
was the smallest (23%).
 Higher scores correlate with a higher probability of being placed in a more favorable
profile cluster, which is in line with previous research on motivation and self-
efficacy.

Conclusions
  The results of our study show significant gender differences in motivation profiles
regarding iSTEM and STEM test scores within those profiles.
 Girls in the eighth grade, currently enrolled in STEM courses, have a higher chance
compared to boys with equal ability to have a less favorable iSTEM motivation
profile (i.e., cluster 4, the high amotivation profile cluster).
 These patterns in motivation profile and the correlation with STEM test scored were
detected in grade eight students.

Availability of data and materials

 The datasets generated and/or analyzed during the current study are not publicly
available due to general data protection regulations (EU 2016/679), but are available
from the corresponding author on reasonable request.
 AIC: Akaike Information Criterion
 ANOVA: Analyses of variance
 BIC: Bayesian information criterion
 Cluster analysis
 GDP: Gross domestic product
 ICC: Intra-cluster correlation coefficient
 IRT: Item response theory
 ltm: Latent trait model
 NE: Neutral motivation profile
 SRG: Self-regulation questionnaire

Appendix A: Questionnaire factors structure, subscale, and items characteristics

 Factor, component, and item characteristics: M, SD, Factor loading, Autonomous

motivation, Intrinsic regulation, Identified regulation, External regulation,
Amotivation
 I try to do my best during STEM classes because it is fun. * 3.22 0.87 During STEM
classes I do best because I find learning STEM important.
 Ratio Chi-squared by the degrees of freedom (X2/df) = 3.98; Comparative of FIT
Index (CFI) = 0.94; Root mean square error of approximation (RMSEA) =0.056; α =
standardized Cronbach's alpha; M = average value; SD = standard deviation.

Appendix C: Cognitive test items, item difficulty and discrimination by gender

 Biology: Knowledge question, Photosynthesis

 Physics/Mathematics: Analyzing, and interpreting data, Using mathematics and
computational thinking
 Optics: Analysis, and interpretation of data, Optics (Male/Female)
 Technology/Engineering: Logic-Operators
 Science: Planning and carrying out investigations
 Theoretical modeling
 Biomimetics
 Chemistry: Analysis of data on toxicology
  Toxicology: Analyzed and interpreted data on body mass index
 Information: Information, and interpret data, Schematics
 Infrastructural engineering
 Applied physics
 Application engineering

Appendix D: Impact of DIF items on test characteristic curves

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