









































Communication, Society and Media 
ISSN 2576-5388 (Print) ISSN 2576-5396 (Online) 

Vol. 5, No. 2, 2022 
www.scholink.org/ojs/index.php/csm 

37 
 

Original Paper 

Putting the Stars within Reach Using NASA 3D Data-Based 

Models 
Kimberly K. Arcand1* 

1 NASA’s Chandra X-ray Observatory, Center for Astrophysics | Harvard & Smithsonian 60 Garden 

Street | MS 70 | Cambridge, MA 02138, United States 
* Kimberly K. Arcand, NASA’s Chandra X-ray Observatory, Center for Astrophysics | Harvard & 

Smithsonian 60 Garden Street | MS 70 | Cambridge, MA 02138, United States 

 

Received: May 20, 2022         Accepted: May 31, 2022        Online Published: August 22, 2022 

doi:10.22158/csm.v5n2p37                       URL: http://dx.doi.org/10.22158/csm.v5n2p37 

 

Abstract 

This study investigated the quantifiable effects of data-based 3D models and prints on spatial reasoning 

skills and interest in science, technology, engineering, and mathematics (STEM) fields, for n = 100 

youths aged 9-12 (99 female and 1 non-binary), primarily from traditionally underrepresented groups 

in STEM. In a pre-post design, participants engaged in workshops using data-based astrophysical 3D 

models delivered via computer-based interactions, virtual reality, and 3D prints. Multivariate ANOVAs 

yielded significantly increased STEM interest but were not significant for increasing spatial ability. The 

results are discussed in terms of the need to extend exposure and science communications to STEM 

activities to female youths that are younger than middle school aged. 

Keywords 

spatial reasoning, 3D printing, women in STEM, astronomy, VR 

 

Putting the Stars Within Reach Using NASA 3D Data-Based Models 

1. Introduction 

This study examined the effects of data-based astrophysical 3D models delivered via computer based 

interactions, virtual reality, and 3D prints, on spatial reasoning skills and interest in science, technology, 

engineering, and mathematics (STEM) for females aged 9-12, in particular from traditionally 

underrepresented groups in STEM. Underrepresented, or underserved, audiences refer to the 

demographic status of, and the services that are offered or presented to, segments of a community, 

typically not currently being served within a larger population that might benefit from such services 

(Williams et al., 2009). Research to date has not focused extensively on the development of STEM 



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interests and spatial reasoning skills of young females, particularly at the time when such learners are 

forming potential identities in or with STEM and beginning to think about educational and 

career-related options. The literature review provides a brief background on the development of data 

visualizations in astrophysics used in the study, issues of STEM interest and identity, and biases and 

role models, before focusing on spatial skills investigated in the study for the target demographic 

group.  

Literature Review 

3D Data in Science Communication 

Astrophysical data, and the visualizations from such data, are often two-dimensional (2D). Our 

perspective from Earth, including from our farthest-reaching telescopes, primarily offers a flat 

projection on the sky, but spectral information can restore the 3-dimensionality of the Universe 

(Ferrand et al., 2016). Researchers from computer scientists to astrophysicists, engineers, and 

technicians are developing multiple techniques to expand astrophysical data visualization within the 2D 

space and beyond into 3D often for scientific research purposes, but also to help improve 

communications of such research (see e.g., Arcand et al., 2017b, 2018, 2019; Diemer & Facio, 2017; 

Goodman et al., 2009; Madura et al., 2015, 2017; Steffen et al., 2014). This work, however, is still 

relatively nascent, particularly regarding its impact on specific audiences. 

The potential to study celestial objects from multiple viewpoints not only can provide researchers with 

a better understanding of such but also has the potential help open access to and enable science 

communication with non-experts (Arcand et al., 2017b, 2020; Ferrand & Warren, 2018). There are 

data-driven 3D objects in 3D printed or extended reality (XR) forms in space science under 

development that have been or can be used in science communications including, for example, 3D 

printed supernova remnants (Arcand et al., 2017; Arcand & Watzke, 2019), binary stars (two stars 

orbiting each other) (Watzke & Edmonds, 2017), lunar geological maps (Ellison, 2014), Martian craters 

and meteorites (Capraro & NASA JPL, 2014; Gwinner et al., 2014), other local planetary bodies such 

as asteroids (e.g., Kim, 2018), and topics as the Cosmic Web (Diemer & Facio, 2017) and the Cosmic 

Microwave Background (Clements et al., 2016). Astrophysical VR applications for use in science 

communication have been based in simulated worlds (see e.g., Farr et al., 2009) and standalone 

applications (see e.g., Arcand et al., 2018; Ferrand & Warren, 2018; Russell, 2020). Non-experts can, 

for example, travel across parts of the Martian surface (Good, 2017), walk on the Sun (Hinode Science 

Center at NAOJ, 2018), or work with radio data cubes of a spiral galaxy (Ferrand et al., 2016). As 

noted previously, potential impacts of such 3D or XR assets on specific audiences like 

female-identifying youths has not been studied in detail, though there is some indication through 

informal observations and audience demographics of the potential of these mediums using astronomical 

data via XR (Ferrand & Warren, 2018) and 3D printing (Arcand et al., 2017). 



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STEM Interest, Identity & Self-efficacy 

Separately from the growth of technical achievements in processing astronomical data, there are 

numerous reasons why gender parity in astronomy in STEM, is important to achieve. Practical issues 

such as equal opportunity and pay, along with job security for women in STEM pathways, are key, 

especially as career opportunities in STEM fields increase (Langdon et al., 2011). Financial security 

can be important for the economic viability of a family (see Anderson, 2016), particularly as 

single-mother families increase (U.S. Census Bureau, 2016). Importantly, developing strong critical 

thinking skills so vital in today’s world (Duran & Sendag, 2012) are part of STEM subjects. Women 

tend to make many of the decisions on health and wellbeing for themselves or their families (Marincola, 

2006). These personal consumer choices, voting behaviors, investments, and other decisions affect 

wider economics, politics, finance, and other areas. This argues for women to be well versed in skills, 

especially those associated with critical thinking and reasoning, associated with STEM fields. The 

loss—or absence—of women in STEM has quantifiable effects on economies, as well as effects on the 

“systems we create”, from scientific policy to consumer products (Reilly et al., 2019; Wade & 

Zaringhalam, 2019, para.4). 

STEM interest is a critical component of developing a STEM identity and can be intertwined with 

issues of confidence and self-efficacy for young females. Self-efficacy is often lower in females than 

males generally (Gnilka & Novakovic, 2017), and related self-efficacy issues of belonging and 

confidence (Settles, 2014) can therefore be difficult to navigate in formulating the STEM identity. 

Research has shown that by as young as kindergarten age, girls believe boys are more brilliant and 

suited to highly intellectual activities over girls (Bian et al., 2017). Negative stereotypes and implicit 

biases, particularly as unconsciously reinforced by parents, caretakers, and educators, can affect girls’ 

self-perception in STEM topics (Levine, et al., 2015). Lack of confidence in themselves as participants 

in STEM, or more generally feeling inadequate, can lead to feelings associated with what has been 

characterized as imposter syndrome (Heaverlo et al., 2013), which affects adult women (and men) in 

STEM, and particularly affects minoritized populations (Byars-Winston & Dahlberg, 2019). This 

relates to Bandura’s theory of self-efficacy, which concerns one’s belief in one’s capabilities to perform 

tasks in specific domains to bring goals to fruition (Bandura, 1997). Self-efficacy can influence what 

choices and effort an individual will make to reach goals, and also, importantly, if the individual will 

persist through difficulties and challenges to obtain those goals (Bandura, 1997; Rittmayer & Beier, 

2008). Success in STEM areas requires a strong sense of one's ability to succeed, and it is noted that 

females can have lower senses of self-efficacy towards STEM than males (Williams & George-Jackson, 

2014). 

Younger women and underrepresented groups may find that if they are not fitting a more typical STEM 

profile by middle school, then STEM careers might not look viable or feasible (Hill, 2019). 



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Furthermore, there is an often referenced “STEM pipeline” which, perhaps inadvertently, asserts a 

linear path from childhood interest in STEM, straight through to taking STEM courses at university, to 

finding work in a STEM career (The STEM Pipeline, 2015). In fact, this pipeline applies to only about 

50% of STEM workers, and further can serve to discourage diversification of the STEM workforce 

population (Cannady et al., 2014) by unintentionally propounding a one size fits all.  

A personal STEM identity is the belief that one can do well and succeed at STEM subjects (Ayoub, 

2017). The social aspect of the STEM identity is where participants can visualize themselves “accepted 

as a member of a STEM discipline or field” (Kim et al., 2018, p. 3). There may be several factors 

related to STEM identities that make it challenging, particularly for underserved groups like young 

women of color, to adopt such an identity for themselves (Steinke, 2017). Negotiating an identity as a 

girl or woman while also adopting an identity in STEM can be challenging, though also eventually 

beneficial (Settles, 2014).  

The challenges to reaching such an identity, however, may include not being able to picture oneself 

working in STEM, particularly for underrepresented groups (Chee, 2018). Additionally, the 

socio-cultural cues from others, such as those who seem to indicate that STEM is not cool and, 

therefore, is less acceptable among peers (Steinke, 2017), or that women do not belong, particularly 

women of color (Kim et al., 2018), can be challenging. Importantly, recent research has demonstrated 

that through interventions, STEM identity challenges can be positively influenced (Kim et al., 2018).  

Biases, Role Models and Stereotypes 

Multiple studies have shown that STEM skills are learned (Hill et al., 2010) and gender bias still has a 

negative impact on girls’ noted interest in STEM fields from a very early age. Work from Spencer et al. 

(1999), for example, demonstrated the strength of stereotype threat for girls in mathematics testing 

among top math performers, where even the mention of gender bias in a test led to reduced 

performance for female participants. Group stereotypes (whether based on gender or race) “can 

threaten how students evaluate themselves, which then alters academic identity and intellectual 

performance” and can affect “members of any group about whom negative stereotypes exist” 

(American Psychological Association, 2006, p. 1). Gender bias not only supports the perception that 

science and mathematics are for boys, but more importantly, that stereotype threat can lead to girls’ 

performance anxiety and low expectations on academic tests (Doyle, 2016). 

A growing body of research has shown that there are significant sociocultural roadblocks for women in 

both the computer science and engineering fields. These barriers can greatly hinder determination of 

selection and study (Ceci et al., 2009; Cheryan et al., 2015). A meta-analysis conducted by Cheryan et 

al. (2017) of 1,200 papers on gender gaps in computer science, engineering, and physics, pointed to 

three primary issues. These included a masculine culture, which included three sub-issues of 

incompatibility stereotypes, negative stereotypes, and lack of role models, that can lead to women 



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feeling less welcome in those fields; less early exposure or work within those fields; and reduced 

self-efficacy. The researchers noted that STEM programs could benefit by addressing problematic 

cultural issues as well as by building awareness in girls and boys that both are equally capable of 

succeeding in STEM careers.  

Spatial Skills 

Focusing down into specific issues in STEM skills, mental manipulation and understanding of 2D or 

3D objects has been posited as an important STEM skill, helping to indicate future mathematical 

success (Hegarty & Waller, 2005; Verdine et al., 2014), science performance (Uttal et al., 2013), and 

potential pursuit of STEM careers (Uttal & Cohen, 2012). Middle school students with good mental 

rotation skills particularly tend to perform well in their science classes (Ganley et al., 2014). And Rafi 

et al. (2005) noted that early childhood activities involving paper folding, unfolding, and other 

manipulations make important contributions towards enhancing spatial skills. 

Research back over three decades has indicated that differences in spatial reasoning between males and 

females often show reduced or delayed development in females (Linn & Petersen, 1985; Maccoby & 

Jacklin, 1974; Voyer et al., 1995; Yilmaz, 2009). A meta-analysis of gender differences across 14 

studies published between 1975 and 1992 supported that claim, with females shown to be at a 

statistically significant disadvantage in mental rotation skills in each of the studies (Masters & Sanders, 

1993). Recent research has emerged indicating that such spatial skills can be improved through 

interventions (Gold et al., 2018; Hwang et al., 2009; Uttal et al., 2013; Yeh, 2007), and likely through 

interventions in virtual environments (Hwang & Hu, 2013). Additionally, 3D or XR experiences may 

support such learning by providing a hands-on approach and making less abstract such large, abstract 

and highly spatial concepts as astrophysics through embodied cognition (Cole, Cohen, Wilhelm & 

Lindell, 2018; Pomerantz, 2019). Those findings underpinned the motivation for the research 

conducted for this study. 

Some literature has suggested an optimal age for spatial skills development would be in early middle 

school years, that is, ages 11-12 (Ben-Chaim, 1989). Yet, no definitive research was found on the lower 

limits of total time needed for such interventions, nor specific research particularly targeting 

underserved young females. The research in this study set out to explore such variables.  

Conceptual Framework and Research Question 

As this study investigated the quantifiable effects of data-based 3D models and prints on spatial 

reasoning skills and interest in STEM fields for young females, particularly for those from traditionally 

underrepresented groups in STEM, it is important to consider young females’ participation in STEM 

activities, the development of spatial reasoning skills, and the presence of encouraging role models, to 

open potential future pathways in STEM studies. 

 



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This study involved working directly with young females to investigate the quantifiable effects of 

data-based 3D models and prints on improving spatial reasoning skills and interest in STEM fields in 

under-represented groups, particularly of young female learners. The approach used in this research 

was pragmatism (Johnson & Onwuegbuzie, 2004), as it allows that there is a subjective reality or “real 

world,” although within that subjective reality there are different world views or perspectives (Johnson 

& Onwuegbuzie, 2004). Pragmatism, therefore, frames the research question with an eye towards 

“workability,” while understanding that “our values and our politics are always a part of who we are 

and how we act” (Morgan, 2007, p. 70). 

 

2. Methods and Procedures 

2.1 Participants 

In the study, a convenience sample of 117 youths aged 9-12 years from female-supporting 

organizations participated, recruited from local networks known to the researcher: a local girls day 

camp group (Camp Group = 29), a local private all-girls school (Private School = 59), and local girls 

scouting organization (Scout Group = 29) in the northeastern region of the United States. 

2.2 Materials 

There were three instruments used in the study, a demographics questionnaire, a spatial skills 

evaluation, and a STEM interest inventory. The science materials used in the workshops are described 

in the procedure, with links provided to the content. 

2.3 Demographics Inventory 

The demographics inventory developed for study included items for grade, gender, ethnicity, zip code, 

activity location, self-rated knowledge of STEM, and hours of video games played per day. Age, grade, 

gender, ethnicity information, zip code, and location were collected as background, for analyses, and to 

ensure the sample groups represented the targeted population. Self-rating information and estimates of 

how many hours of video games played per day were collected to later assist with the examination of 

the data in the spatial skills and inventory responses. 

2.4 Spatial Skills Inventory 

The spatial skills instrument used was the freely available "Spatial Reasoning Test | 123test.com," 

(123test, 2019). In pilot testing (n = 4) of comparably aged females, it was determined that using all 10 

items from the original instrument was too lengthy for the selected age group, and items 6 through 10 

became more difficult for the participants. The evaluation was reduced, therefore, to the first five items.  

2.5 STEM Interest Inventory 

The STEM interest inventory was the “Instruments for Assessing Interest in STEM Content and 

Careers” (Tyler-Wood et al., 2010), validated with middle school student participants, and used with 

permission of the authors. The inventory comprises five opposite word pairs repeated across each topic 



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of science, math, engineering, technology, and STEM careers, with selections across a 7-point scale.  

Procedure 

The science content used in the study applied information gained from researching the data processing 

and 3D modeling pipeline. The order of the activities was chosen to build participants towards a 

knowledge base of a topic not typical in content for children aged 9-12 in the U.S. Care was taken to 

provide the underpinnings of how the data were collected, what scientists do to the data, and why.  

This study was approved by a university ethics committee. Participation was anonymous and the data 

were aggregated, with no personally identifiable information stored.  

The workshops were conducted between February-October, 2019 in concrete sessions of half (2.5 hours) 

or full (6 hours) days, as determined by the typical meeting time for the group involved. The times 

listed for the workshops included all activities, from start to finish. This study used a controlled within 

groups design. Participants were randomly assigned to group A or group B to counterbalance the order 

for the pre- and post-conditions. Identification numbers were used to ensure that the pre- and 

post-evaluations were matched.  

Group A completed the spatial skills instrument as a pre-evaluation, engaged in the workshop, and then 

completed the STEM interest inventory as a post-evaluation. Group B completed the STEM interest 

inventory as a pre-evaluation, engaged in the workshop, and then completed the spatial skills 

instrument as the post-evaluation. Both Groups A and B completed the demographics inventory as part 

of the pre-evaluations. 

Materials were created and produced by the researcher and her team for NASA’s Chandra X-ray 

Observatory. Materials provided multiple modes of accessibility. VR utilized audio tracks and closed 

captioning, tactile 3D prints of data sets were provided, videos included audio and text transcripts, 

images were tagged for screen readers, and digital files were coded to U.S. accessibility standards. 

Activities were developed to describe and interactively show the kind of work the researcher and others 

do using astronomical data. Materials were developed with funding from NASA and were required to 

pass NASA product review, a rigorous internal quality control system from NASA to ensure accuracy 

of materials.  

The half day event was conducted with the researcher and one additional female facilitator who was 

trained by the researcher. Full-day events were conducted with the researcher and three to four 

additional trained female facilitators or teacher helpers. Facilitators were current workers in STEM 

fields. The Private School group had a surplus of volunteer teachers to help throughout the days, and 

who were trained with a 60-minute webinar prior to the event. The schedule for the workshops follows: 

1) Introduction 

The pre-evaluation was administered to all participants, followed by an introduction for the science 

content. The warm-up activity outlined how controllers talk to spacecraft orbiting beyond Earth. 



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Participants created binary code name tags, translating their names into 1’s and 0’s 

(http://chandra.si.edu/binary/) 

2) Imaging 

In the half days, this covered the basics on how to create an image, followed by questions and answers. 

In the full days, participants completed a computer-based activity using Pencil Code and videos that 

step through the mechanics of compositing/colorizing NASA data to create an image of stars 

(http://chandra.si.edu/code/). 

3) 3D modeling/printing 

In full days, participants used browser-based software Tinkercad to learn 3D modeling before loading 

3D NASA data. Users discussed 3D printing and handled 3D prints. Half days had a discussion of the 

modeling/printing processes in which they handled 3D prints for questions and answers. 

(http://chandra.si.edu/tinkercad/ and http://chandra.si.edu/3dprint).  

4) VR 

This segment began with a presentation on creating NASA data-driven VR and what the use of such 

applications can mean before moving into spatially-aware demonstrations of astronomical VR 

including supernova remnants and the area around the Milky Way’s supermassive black hole 

(http://chandra.si.edu/vr). 

5) Paper activities 

Paper-based activities included origami (http://chandra.si.edu/origami) and paper circuits 

(http://chandra.si.edu/make/) to help communicate science data concepts (e.g., expansion and 

unpacking, or neutron stars).  

6) Wrap-up 

Post-evaluation assessments were presented with a question-and-answer session on science and careers. 

NASA handouts showcasing women in STEM (http://chandra.si.edu/women/) and handouts on each 

activity were given out for reflection and sharing.  

Informal Observations 

Beyond the formal instruments, informal observations were recorded by the researcher in the form of 

field notes. Though not formal analyses, these observations of participant behaviors and comments 

were captured throughout the day on the researcher’s iPad and are threaded into the results as 

supplemental interpretations where applicable. 

 

3. Result 

Workshops 

There were four workshops, with workshop times and lengths considered in the analyses by examining 

results for the different groups. The first workshop was a half-day, special event offered at a local Scout 



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Group headquarters for 4th, 5th, and 6th grade girls (n = 29, aged 9-11). Participants, with their 

caregivers, self-selected to register individually or as a group of Scouts. Fourteen girls had to leave 

early due to poor weather and snow. This reduced the total number of completed pre-post sets of data to 

15.  

The second workshop was conducted as a single full-day workshop hosted at a local college, organized 

with the Camp Group as part of their summer day-camp program for socio-economically disadvantaged 

girls. There were 29 participants aged 10-12. Two participants had to leave the workshop early, 

bringing the total of completed pre-post sets to 27. 

The third and fourth workshops took place at a local private girls’ school with a “STEAM” (science, 

technology, engineering, art, and math) center for 59 girls, comprising their entire 4th, 5th, and 6th 

grade classes (aged 9-12). One 6th grader had to leave early, bringing the total of completed pre-post 

sets to 58. 

Descriptive Data 

The data collected were digitized and entered into SPSS version 26 for analysis (IBM Corp., 2019). 

Results for the full sample (n = 100) and broken down by affiliation are provided in Tables 1 and 2. 

As shown, participants in the full group were evenly spread across age and grade, in a narrow band 

from ages 9 to 12. Participants identified as female with one exception, who self-identified as 

non-binary. In terms of affiliation, the Camp Group was the only group comprised completely of girls 

of color (with one participant who chose not to respond to this item). 

As a result of the poor weather during one workshop (described above), there were slightly more 

participants in the spatial inventory pre-STEM inventory post sample (n = 55) than the STEM 

inventory pre-spatial inventory post sample (n = 45).  

All participants were asked to rate themselves on their overall knowledge in STEM topics. On a scale 

of 1 (low) to 10 (high), the mean ratings indicated a wide range of knowledge for the full sample, with 

the average rating approaching 7 (see Table 2). When examined by affiliation, the Camp Group 

participants were somewhat below the other two groups. A one-way analysis of variance determined 

that these differences were not statistically significant, F (2, 97) = 2.03, ns. Although most participants 

(n = 86; 86%) reported spending 0-2 hours playing video games per day (M = 1.16, SD = 1.68), there 

was some confusion vocalized by the girls in this group and noted by the researcher as to what 

“counted” as playing a video game (e.g., Candy Crush vs. Grand Theft Auto). Thus, this variable was 

only used in a correlational analysis and not in the primary analyses for investigating the research 

question.  

The scores for the spatial and STEM inventories are presented in Table 3. The total score for the spatial 

inventory, based on 0 (lowest possible score) to 5 (highest possible score) indicated that the Private 

School participants had the highest values for the spatial score totals, followed by the Camp Group, and 



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then the Scout Group. For the STEM inventories, the minimum score possible was 0 and the maximum 

score possible was 135. For the STEM inventory score totals, the Private School placed the highest 

again, followed by the Scout Group, and then the Camp Group.  

Principal Component Analysis  

Next, a Principal Component Analysis with a direct oblimin rotation was run on the items of the STEM 

inventory to examine their factor structure (data are available). A direct oblimin rotation was chosen as 

any anticipated underlying factors could be correlated. The structure matched what was expected for 

the inventory, with the items for each subscale loading into an individual factor, aligning with the 

published results (Tyler-Wood et al., 2010). Based on this analysis, the items for each category were 

combined into their expected subscales.  

Each subscale was then examined in terms of reliabilities, using Cronbach’s Alpha. Results are 

provided in Table 4, along with the published reliabilities for the STEM Inventory (Tyler-Wood et al., 

2010). As shown, in each case the data from the participants on the STEM inventory subscales were the 

same or similar to the published coefficient alphas. Reliabilities were calculated for the Spatial 

inventory, also shown below. The value reported is generally considered to be at the minimum level of 

acceptability (Cortina, 1993). As there were no published values for this inventory, no comparison 

could be made to previous data.  

Having established that the reliabilities of the inventories meant that the scales were viable, the 

researcher proceeded with analyses using the total scores for the Spatial and STEM inventories. 

Correlations were computed to examine the relationships of hours playing video games with self-rating 

of STEM knowledge, the Spatial total scores, and the STEM inventory total score, to investigate the 

extent of any relationship among these variables that might help inform STEM identity. The 

correlations were done first for the full sample and then by affiliation (full data are available).  

For the full sample, video hours were negatively correlated with self-rating of STEM knowledge and 

the STEM inventory total score, thus suggesting that more hours playing video games were related to 

lower values for self-rating of STEM knowledge and the STEM inventory total score. Of note is the 

positive correlation between perception of self-rating of STEM knowledge and the STEM inventory 

total score. 

When the correlation analysis was rerun by affiliation (data are available), only the results for Private 

School yielded significant relationships. For the full sample, video hours were negatively correlated 

with self-rating of STEM knowledge, and the STEM inventory total score, suggesting that more hours 

playing video games were related to lower values for self-rating of STEM knowledge and the STEM 

inventory total score. Again, for the full sample there was also a positive correlation between self-rating 

of STEM knowledge and the STEM inventory total score for the Private School sample. 

 



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Multivariate Analysis of Variance (MANOVA) Results 

These analyses laid the groundwork to allow the examination of research question concerning the 

quantifiable effects of databased 3D models of on under-represented populations in STEM, using a 

multivariate analysis of variance (MANOVA). The independent variables in the MANOVA were the 

three affiliations (Scout Group, Camp Group, and Private School) and two levels of the order of the 

presentation of the inventories (Spatial inventory pre and STEM inventory post or STEM inventory pre 

and Spatial inventory post). The two dependent variables were the total scores for the Spatial and 

STEM inventories. Self-rating of STEM knowledge was used as a covariate to eliminate the possibility 

of any potential relationship between perceptions of preexisting knowledge and the dependent variables, 

especially the STEM inventory. Therefore, using this covariate essentially equalized the baseline going 

into the study in terms of where the participants thought they were in terms of ability, so as not to 

obscure any potential effects.  

The results of the MANOVA (see Table 5) yielded two significant findings. Affiliation was significant, 

as the pre-post condition. There was no interaction effect of affiliation by pre-post condition.  

 

Univariate Results 

The univariate results are shown on Table 6. The descriptive statistics for the multivariate test are 

shown previously on Table 5 and are presented graphically at the end of the paper.  

Figure 1 shows the mean pre-post scores for the Spatial inventory, with separate lines for each 

affiliation. All three groups did less well on average in the post-evaluation condition, and the 

participants from the Camp Group were well below the other two groups.  

 

 

 

 

 



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Figure 1. Mean Pre-Post Scores for Spatial Inventory 

 

Figure 2 shows the mean pre-post scores for the STEM inventory, with separate lines for each 

affiliation. The Private School participants did not exhibit much change from pre- to post, but the other 

two groups increased nearly 20 points for the Scout Group and 25 points for the Camp Group 

participants. 



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Figure 2. Mean Pre-Post Scores for STEM Inventory 

 

Informal Observations 

Informal observations included recording general field notes on participation, vocalizations and energy 

levels, comments made by the participants to the researcher throughout the day, questions or concerns 

raised during the activities, as well as summations from the participants volunteered during the wrap up 

sessions. Comments and questions recorded by the researcher in the field notes were tallied and coded 

per group and calculated per session as a percentage over time.  

Comments throughout the workshops included: “I want to do this when I grow up,” “I didn’t know I 

could do this,” “This was the best day ever,” and “I didn’t know I could walk around a black hole.” The 

highest percentage of comments spoken directly to the researcher were from the Camp group; and the 

Scouts group directed the least number of verbal comments to the researcher in total and as a 

percentage of time (note the Scouts group had the least time with the researcher overall).  

Informal end-of-day dialogues resulted in discussions around activities that the participants enjoyed, 

concepts learned, and areas to explore. One common theme expressed to the researcher across all 

groups was enjoyment of the VR and 3D printing (backed up by the recorded observations of the 

individual activities), though the girls spoke positively about each activity. Questions relating to the 

researcher’s career were numerous, but particularly from the Private School. Science-based questions 

from black holes to aliens were another common theme across all groups, with the highest frequency 

from the Private School. 



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Table 1. Demographic Data for the Full Sample and by Affiliation 

___________________________________________________________________________________ 

Item       Group    n    Percent 

___________________________________________________________________________________ 

Affiliation     Scout Group   15     15 

      Camp Group   27     27 

      Private School   58     58 

Pre-Post Group     

   

Full Group    Spatial/Inventory   55     55 

      Inventory/Spatial   45     45 

Scout Group   Spatial/Inventory    7     46.7 

      Inventory/Spatial    8     53.3 

Camp Group   Spatial/Inventory   16     59.3 

      Inventory/Spatial   11     40.7 

Private School   Spatial/Inventory   32     55.2 

      Inventory/Spatial   6     44.8 

Age   

Full Group    9     21     21 

      10     25     25 

      11     38     38 

12     16     16 

Scout Group    9      6     40 

      10      8     53.3 

      11      1     6.7 

      12     --      -- 

Camp Group    9     --      -- 

      10      4     14.8 

      11      9     33.3 

      12     14     51.9 

Private School    9     15     25.9 

      10     13     22.4 

      11     28     38 

  

          12     2     3.4 



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Item      Group    n     Percent 

___________________________________________________________________________________ 

Grade         

Full Group      4     27     27 

      5     25     25 

      6     32     32 

      7     16     16 

Gender       Female     99     99 

     Nonbinary       1     1 

Ethnicity  

   

Full Group   Caucasian     45     45 

     Black/African    11     11 

     Hispanic/Latina    9     9 

     Other      30     30 

     Prefer NtA     5     5 

Scout Group  Caucasian     11     73.3 

     Black/African    --     -- 

     Hispanic/Latina    --     -- 

     Other      2     13.3 

     Prefer NtA     2     13.3 

Camp Group  Caucasian     --     -- 

     Black/African    8     29.6 

     Hispanic/Latina    8     29.6 

     Other      10     37 

     Prefer NtA     1     3.7 

Private School  Caucasian     34     58.6 

     Black/African    3     5.2 

     Hispanic/Latina    1     1.7 

     Other      18     31 

     Prefer NtA     2     3.4 

Self-rating    

Full Group    1     2     2 

      2     --     -- 

      3     1     1 



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      4     6     6 

      5     21     21 

Item        Group     n     Percent 

___________________________________________________________________________________ 

      6     19     19 

      7     20     20 

      8     18     18 

      9     7     7 

      10     6     6 

Scout Group    1     --     -- 

      2     --     -- 

      3     --     -- 

      4     2     13.3 

      5     3     20 

      6     1     6.7 

      7     2     13.3 

      8     2     13.3 

      9     3     20 

      10     2     13.3 

Camp Group    1     --     -- 

      2     --     -- 

      3     1     3.7 

      4     1     3.7 

      5     8     29.6 

      6     7     25.9 

7     7     25.9 

      8     3     11.1 

      9     --     -- 

      10     --     -- 

Private School    1     2     3.4 

      2     --     -- 

      3     --     -- 

      4     3     5.2 

      5     10     17.2 

      6     11     19 



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      7     11     19 

      8     13     22.4 

      9     4     6.9 

      10     4     6.9 

Item      Group    n    Percent 

___________________________________________________________________________________ 

Video Hours    

 

       Full Group   0     48    48 

      1     24    24 

      2     14    14 

      3     6    6 

      4     2    2 

      5     2    2 

      6     1    1 

      7     2    2 

      8     1    1 

      Scout Group   0     5    33.3 

      1     5    33.3 

      2     3    20 

      3     1    6.7 

      4     1    6.7 

      5     --    -- 

      6     --    -- 

      7     --    -- 

      8     --    -- 

Camp Group   0     6    22.2 

      1     10    37 

      2     5    18.5 

      3     1    3.7 

      4     1    3.7 

      5     1    3.7 

      6     1    3.7 

      7     1    3.7 

      8     1    3.7 



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Private School   0     37    63.8 

      1     9    15.5 

      2     6    10.3 

      3     4    6.9 

      4     --    -- 

      5     1    1.7 

 Item     Group    n    Percent 

___________________________________________________________________________________ 

      6     --    -- 

      7     1    1.7 

       8     --    -- 

 

Table 2. Means and Standard Deviations for Age, Self-rating, and Video Hours for Full Group, 

and by Affiliation 

___________________________________________________________________________________ 

Item      Mean        Standard Deviation 

___________________________________________________________________________________ 

Age   

     Full Group    10.49         1  

     Scout Group   9.67         0.62  

     Camp Group   11.37         0.74 

     Private School   10.29         0.9 

Self-rating   

      Full Group    6.55         1.81 

      Scout Group   7.07         2.12 

      Camp Group   6         1.24 

      Private School   6.67         1.91 

Video Hours   

      Full Group    1.16         1.68 

      Scout Group   1.2         1.21 

      Camp Group   1.96         2.19 

      Private School   0.78         1.38 

 

 

 



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Table 3. Scores for the Spatial Skills and STEM Inventories for the Full Sample and by Pre Post 

Conditions within Affiliation 

___________________________________________________________________________________ 

Item     Group   n   Mean    SD 

___________________________________________________________________________________ 

Spatial Total Score   Full Group   100   2.89    1.6 

           Scout Group  15   0.87    1.19 

          Camp Group  27   2.78    1.6 

Item     Group   n      Mean    SD 

___________________________________________________________________________________ 

Private School   58   3.47    1.23 

 

Inventory Total Score   Full Group   100   121.08   35.5 

          Scout Group  15   114.4    32.92 

           Camp Group  27   104    33.88 

           Private School   58   130.76   33.97 

Spatial Pre    Full Group   55   3.24    1.48 

           Scout Group  7   1.14    1.35 

          Camp Group  16   3.06    1.77 

          Private School   32   3.78    0.79 

 

Spatial Post    Full Group   45   2.47    1.66 

           Scout Group  8   0.63    1.06 

          Camp Group  11   2.36    1.29 

          Private School   26   3.08    1.55 

 

Inventory Pre   Full Group   45   116.24   43.28 

           Scout Group  8   105.63   40.88 

          Camp Group  11   89    42.04 

          Private School   26   131.04   39.14 

 

Inventory Post   Full Group   55   125.04   27.36 

           Scout Group  7   124.43   18.94 

          Camp Group  16   114.31   23.08 

          Private School   32   130.53   29.77 



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Table 4. Reliability Statistics using Cronbach’s Alpha for STEM Inventory Subscales and Spatial 

Skills 

________________________________________________________________________________ 

Subscale    Cronbach’s Alpha     Published Cronbach’s  

Alpha 

___________________________________________________________________________________ 

Science     .91        .84 

Math      .91        .88 

Subscale    Cronbach’s Alpha     Published Cronbach’s 

             Alpha 

___________________________________________________________________________________ 

Engineering    .90        .92 

Technology     .91        .91 

Career     .89        .93 

Spatial     .70        -- 

 

Table 5. Results of Multivariate Analysis of Variance 

___________________________________________________________________________________ 

Effect    Value  F  Hypostudy  Error  Sig. Partial Eta  

          df  df   Squared 

________________________________________________________________________________ 

Intercept        

Pillai’s Trace  .363  26.26   2  92  <.001  .363 

Wilks’ Lambda  .637  26.26   2  92  <.001  .363 

Hotelling’s   .571  26.26   2  92  <.001  .363 

Trace 

 

Roy’s Largest     .571  26.26   2  92  <.001  .363 

Root 

 

Self-rating  

      

Pillai’s Trace  .110  5.69   2  92  .005  .110 

Wilks’ Lambda  .890  5.69   2  92  .005  .110 

Hotelling’s Trace  .124  5.69   2  92  .005  .110 



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Roy’s Largest Root   .124  5.69   2  92  .005  .110 

 

Affiliation  

      

Pillai’s Trace  .428  12.66   4  186  <.001  .363 

Wilks’ Lambda  .596  13.58   4  184  <.001  .363 

Hotelling’s Trace  .637  14.50   4  182  <.001  .363 

Roy’s Largest  .566  26.32   2  93  <.001  .363 

Root 

Effect    Value  F  Hypostudy  Error df Sig.   Partial Eta 

          df         Squared 

__________________________________________________________________________________ 

PRe-Post 

Condition     

Pillai’s Trace   .082  4.11   2  92  .019  .082 

Wilks’ Lambda  . 918  4.11   2  92  .019  .082 

Hotelling’s Trace  .089  4.11   2  92  .019  .082 

Roy’s Largest Root    .089  4.11   2  92  .019  .082 

 

Affiliation*Pre  

Post       

Pillai’s Trace   .030  .71   4  186  .586  .015 

Wilks’ Lambda   .970  .71   4  184  .586  .015 

Hotelling’s Trace  .031  .71   4  182  590  .015 

Roy’s Largest     .030  1.41   2  93    .015 

Root 

       

Table 6. Results of Univariate Analysis of Variance Between-subjects Effects 

___________________________________________________________________________________ 

      Type lll Sum   Mean      Partial Eta 

Source   Dependent Variable  of Squares df Square  F    Sig.  Squared 

___________________________________________________________________________________ 

Affiliation  Spatial Total  78.14  2 39.36  22.96  <.001  0.331 

   Inventory Total 12464.33 2 6232.17 6.1  0.003  0.116 

 



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Pre Post   Spatial Total  7.44  1 7.438  4.34  0.04  0.045 

 

Condition  Inventory Total 3751.09 1 3751.09 3.67  0.058  0.038 

Affiliation *  Spatial Total  0.14  2 0.067  0.04  0.961  0.001 

Pre Post  Inventory Total 2879.38 2 1439.69 1.41  0.25  0.029 

Condition  

       

Error   Spatial Total  159.39 93 1.71    

   Inventory Total 95069.43 93 1022.25    

      Type lll Sum  Mean      Partial Eta 

Source   Dependent Variables   df   f  Sig.   

      of Squares  Square      Squared 

_______________________________________________________________________________________________________ 

Total   Spatial Total  1089  100     

   Inventory Total 1590788 100     

Corrected  Spatial Total  253.79  99     

Total   Inventory Total 124751.36 99     

 

4. Discussion 

Significant Growth in STEM Inventory for Disadvantaged Participants 

The mean pre post scores for the STEM inventory showed that the Private School participants did not 

exhibit much change from the pre- to post evaluations, but the Scout and Camp groups did, with a 

statistically significant increase of almost 20 points for the Scout Group and 25 points for the Camp 

Group participants. It might seem counterintuitive to find that the more privileged audience remained 

relatively stable in their STEM inventory total scores, given that the participants’ behavior and 

observed and recorded comments were highly positive. However, it is useful to note that the private 

school is a leader in STEAM (Fogarty & Arcand, 2020) learning, with a well-equipped facility with 3D 

printers, robotics and coding programs, and an excellent staff-to-learner ratio. These participants 

attended a resource-rich school, and STEAM/learning exposure levels were high, while many, though 

not all, participants provided zip codes of often wealthy communities, in general. This group of 

participants came into the workshops strong in STEM interest and left as strong. For them, the 

workshops may have been more about reinforcing and providing practical examples of STEM topics, 

careers, role models, etc., than being novel or revelatory regarding STEM pathways. 

The significant positive growth for the Camp group firstly, and Scout group secondly, in the STEM 

inventory, however, gets to the core of the research. The Camp group was the most socio-economically 



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disadvantaged group of the three samples, as determined by Camp selection criteria and the 

requirements to attend the Camp free of charge. The participants in the program are considered 

potentially at-risk youth by the Camp. Besides signing up for the overall summer camp program and 

agreeing to attend the 3D workshop, Camp participants had less individual agency in selecting a 

day-long STEM activity, as the Camp directors selected the itinerary. This group, however, exhibited 

the most growth in the STEM inventory overall. As such, it is an example of the potential benefits of 

even short term (single day) STEM engagement on STEM interest for less socio-economically 

advantaged groups, and perhaps provides additional evidence regarding reaching groups who are not 

necessarily self-selecting for STEM activities already.  

Next in STEM inventory improvement, the Scout group had more variable individual agency than the 

Camp group, in that some participants selected to attend the workshop by themselves while others 

came as part of their chapter’s decision to attend. Overall, the Scout group participants came from 

mostly and generally middle-class income zip code districts, and was less diverse than either of the 

other workshop groups. Being members of an out-of-school group likely provided these participants 

with access to external enrichment programs, but still this half-day program saw a quantifiable effect 

on the growth of STEM interest.  

Lack of Impact of Spatial Reasoning Skills 

Although it was hoped that 3D data manipulation activities would yield improvement in spatial 

reasoning skills from the pre to post groups, ultimately perhaps it was not surprising to see no statistical 

improvement in the spatial reasoning evaluation. Current literature was not clear on the minimal 

baseline of interventions required to effect positive change in spatial reasoning skills, and specifically 

not for this age, gender, and schooling level, although research with older participants tended toward 

multiple steps of interventions spread over time (see e.g., Contero et al., 2006; Dominguez et al., 2012; 

Feng et al., 2007; Martín-Gutierrez et al., 2013; Rafi et al., 2005; Wright et al., 2008). The literature, 

therefore, lacks a baseline for understanding spatial reasoning intervention impacts, particularly for the 

age and gender groups that participated in this study. 

This study, then, adds to the literature on spatial skills improvement for young females, that single, 

relatively short interventions (6 hours or less) with spatial skills related activities are likely not enough 

to effect demonstrably positive change. However, effective spatial skills testing instruments for such 

demographic groups must be reconsidered in approach and methodology before reaching any 

conclusions in this area, as discussed in the following sections.  

It was, however, surprising to see a decrease in the spatial reasoning skills between pre- and 

post-evaluations, particularly as STEM interest went up as a whole in the study. It is possible that the 

participants were suffering from frustration with the spatial instrument used (see the next subsections 

critiquing the evaluation instruments chosen), test fatigue or more broad fatigue from hours of STEM 



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work in a space beyond their more typical daily schedule, or, in the case of the Scouts group, from a 

late activity after a school day and with the disruption of some classmates leaving early due to poor 

weather. STEM program evaluation fatigue has been remarked upon generally in U.S. projects 

(Malyn-Smith et al., 2013). Another more specific study on cognitive fatigue during administration of 

standardized tests showed that, in Danish public schools as the school day progresses, test scores 

tended to lower each hour by 0.9 of a standard deviation (Sievertsen et al., 2016). Such results might 

help explain why there was a decrease in spatial skills demonstrated even during the short Scout event, 

which was conducted in the evening, ending at 8:00 p.m., and with the younger girls (ages 9 and 10). 

It is important, therefore, to reflect on methodology at this point. There was a disparity, and difficulty in 

completion, of the survey instruments across the groups, with the workshops for the Camp and Scout 

groups having the most difficulty, as discussed in the next section. 

The Importance of Appropriate Instruments for Specific Demographics 

Spatial and Inventory Instruments 

The spatial skills inventory was the most challenging aspect of the day for all groups, with frustration 

and fatigue being evident during the exercise. The researcher found it necessary to talk through an 

example of the mental rotation exercise – not one included on the handout – to the participants, often 

with much confusion around it even with the demonstration. The researcher and assistant facilitators 

repeated directions for the evaluation for all groups, and provided noticeably more assistance for the 

Camp group, followed by slightly less assistance for the Scout group, followed by the least amount for 

the Private School. It would be useful to compare this data with similarly aged groups across different 

demographics, including of other genders (male and non-binary).  

Previous research has demonstrated that improvements could be made on mental rotation testing for 

female and male participants (aged 15-22) with attitude-based interventions (Moè, 2009; Moè & 

Pazzaglia, 2010). In one study, for females, providing positive information on gender before testing 

(e.g., telling subjects women tend to outperform men) led to improved test results, whereas for males, 

discussion of task difficulty before testing led to improved results (Moè, 2009). A second study (mean 

age 17.02) suggested that discussions of effort involved (“anyone can succeed in this task by putting in 

effort”) can improve performance in mental rotation tests (Moè & Pazzaglia, 2010, p. 464). The 

research suggested, then, that female improvement in spatial skills testing could be directed through 

targeted encouragement leading to increased self-efficacy. This technique could potentially be applied 

to future spatial instrument assessments to see if it expands results beyond mental rotation testing to 

help better capture the crucial data necessary for investigating spatial skills in young female-identifying 

populations, and particularly those that identify with other groups.  

The STEM inventory instrument was less problematic. In each of the workshops, the visual boxing of 

the STEM inventory questions by topic (science, technology etc.) led some of the younger participants 



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to believe they only needed to select one condition per topic. Others were confused by the switching of 

the sides for the positive and negative terms. While this is done purposefully to discourage automatic 

fill-ins without consideration, it was slightly more challenging for the age group in this study. 

Facilitators clarified directions, asking participants to complete the questions in each topical box.  

The Camp and Scout groups needed more assistance and reminders for the instruments overall. The 

Private School participants tended to socialize their instruments—unprompted by the 

researcher—seemingly trying as a collective not to leave fellow learners behind. The overall finding 

supports the literature that argues for “culturally and contextually responsive” instruments in STEM 

interventions, and particularly those that target underrepresented audiences in STEM (Malyn-Smith et 

al., 2013, p. 2). Understanding the needs of the specific demographic groups being evaluated, and being 

sensitive to the perspectives and experiences they may have, should be considered during instrument 

development, but also during instrument deployment where a real-time, flexible response could be 

practiced to guide the evaluation process (Malyn-Smith et al., 2013; National Academies of Sciences, 

Engineering, and Medicine, 2018; University of Birmingham, 2020). As noted earlier, cognitive fatigue 

could have been a factor in post-evaluation performance. It is also possible that the instruments 

resembled “high-stakes testing” during formal education, which can be stressful to learners 

(Malyn-Smith et al., 2013, p. 2).  

Instruments that are better situated for collecting such data angled for younger participants who might 

be underserved in informal STEM engagements (Allen & Peterman, 2019). This result extends the 

literature of current assessment instruments helping to show that additional research is required to 

refine existing instruments or develop more learner-specific methodologies that could better capture 

spatial reasoning skill data or STEM interest data for younger females (ages 9-12) and particularly for 

socio-economically disadvantaged group subsets within that demographic, who might otherwise 

experience frustration with existing instruments. In sum, the findings from this study highlight the 

importance of using or creating age appropriate, and level of learning appropriate ways to be able to 

evaluate progress. 

 

Additional Findings  

There was a significant positive correlation between perception of STEM knowledge and total career 

score in STEM inventory. This correlation suggests support for the literature regarding self-efficacy and 

interests in middle school populations (see, e.g., Blotnicky et al., 2018; Fouad & Smith, 1996; 

Simpkins et al., 2006). However, an alternative explanation for this result is that perhaps young females 

are drawn towards areas of perceived strength or self-efficacy.  

Positive attitudes by learners towards VR technologies have been established for some time (see 

Mikropoulos et al., 1998). Mantovani (2001) noted the potential for learners’ visualization, making 



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concrete what might seem abstract through other methods of display or interactions, and important for 

learning that which cannot be easily experienced in the real world. Additionally, VR has the potential to 

offer avenues of adaptation, collaboration, and evaluation (Mantovani, 2001). VR can also provide 

critical interactivity in multimedia that may help improve learning function, guiding the user to explore 

and understand scientific phenomenon (Mayer & Betrancourt, 2005). 3D visualization, therefore, is 

understood to be an immersive environment that can help solve the “problem of looking at 

two-dimensional images and requiring participants to understand relationships three-dimensionally” 

(Bengfort, 2020, para. 12). Though there is also caution to apply that such models should support 

learners with direction and guidance, particularly for topics such as astrophysics or other similar 

scientific visualizations that may provide overly complex models (Tversky, Morrison, & Betrancourt, 

2002) and require large amounts of information to be processed by the user (Lowe, 2003).  

In the case of the workshops presented in this study, traditional, or physical, and computer-based, or 3D, 

materials for data manipulation were combined so they could work “in concert” (Paranandi, 2002, p. 

333) to enrich the learning process and build up to the direct 3D modeling and VR application work, 

which capped off the final third of the workshops. The observed highly positive response to the 3D/VR 

segment, therefore, aligns with the literature that supports the use of VR as a communication and 

education tool (see e.g., Chen, 2006; Pantelidis, 2009), and simultaneously recognizes that much 

deeper research is required to identify and understand the models and techniques used. Findings from 

this research suggest that these techniques need to be examined and adapted to facilitate assessment of 

evaluative methods specific to younger age groups.  

Engagement and excitement levels notably increased during the 3D and VR parts of the workshops, 

and particularly the VR, as recorded in the field notes. If confirmed with more rigorous analysis, this 

could suggest that applications that provide immersive experiences for meaning making could lead to 

increased motivation and engagement levels (see Pantelidis, 1995, Pantelidis, 2009; Winn, 1993). The 

most positively remarked upon part of the workshops involved the 3D and VR activities. The 

complexity of questions, energy levels and noise increased greatly from those with regard the other 

activities. Though this result was observational, future programs that involve interactions with young 

females might want to include more 3D and XR related materials, aligning to current safety standards 

of a maximum of 15 minutes of VR with 10 minutes of rest afterwards (Hicks, 2018). The results, 

therefore, provide some observational evidence that the 3D and VR applications were particularly 

motivating for the participants, supporting the existing literature on this topic, and extending it to the 

age groups in the study. 

 

 

 



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Limitations 

The researcher acknowledges limitations related to choices made about the study design, including 

access to only a small portion of the total U.S. geographic area, and a moderate sized database of only 

100 participants in a limited age range. Additional limitations of the research include total exposure 

time, that is, the workshops being single-day events instead of multiple sessions across longer intervals 

of time and having one workshop of a shorter duration than the others. This research also should not be 

generalized to different underserved populations, such as those with specific physical disabilities, other 

underserved groups not included in the research population of this study, or males. 

 

Conclusion 

This study showed that interventions that utilize real world data manipulations and 3D applications as 

part of hands-on activities can be particularly effective for female-identifying youths from underserved 

groups aged 9-12. Further, interventions shorter than 6 hours in duration for female youths, or that are 

done in only one session, may not be enough to demonstrably effect spatial skills. Additionally, 

instrument development that is specialized for these age groups and learning levels is encouraged.  

The female-identifying youths in this study participated in STEM activities that provided authentic 

experiences manipulating actual observational data such as the NASA data used in the study, and they 

were engaged in such STEM activities with women researchers. There is, however, an issue that needs 

additional investigation, between groups having privilege and those with less privilege. In comparison 

with socio-economically disadvantaged females, more privileged female youths might start off higher 

in interest levels, but then leave just as high, which is encouraging for potential STEM identities and 

pathways for the ages of the participants in this research.  

The availability of the cutting-edge data sets in 3D used in this study that are able to be converted into 

3D prints and VR/AR is expanding at a rapid rate. There have been significant improvements in 

quantity and platform reach even since the data for this study were collected. Such scientific and 

technological advancements are leading to a larger, expanded pipeline of spatial data modeled into 3D 

that can potentially further enhance authentic science communication experiences for nonexperts in 

STEM topics, particularly for underrepresented learners. These also could inform the development of 

the spatial reasoning skills that are important for STEM. Using 3D data is not a novelty, but rather a 

tool for communicating and learning, with great potential for expanding scientific knowledge with 

nonexperts and perhaps to help diversify the potential influx of future researchers. However, it is 

crucial to recognize that the data and the technology are only one small part of putting the stars within 

reach for underrepresented young females. People around them, community support, will likely be 

needed to help drive the most far-reaching, out-of-this-world impact. 

 



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Acknowledgement 

My deepest thanks to Prof. Lisa Smith, without whom this research topic would never have happened, 

as well as to Dr. Keryn Pratt for lending me her helpful perspective. I also thank Sara Price for her 

wonderful help on data digitization and formatting. 

 

I have no known conflict of interest to disclose. This paper was supported by the University of Otago, 

New Zealand. Materials were developed with funding from NASA under contract NAS8-03060 for the 

Chandra X-ray Center, with additional support from funding through NASA's Universe of Learning, 

based upon work supported by NASA under award number NNX16AC65A to the Space Telescope 

Science Institute, in partnership with Caltech/IPAC, Center for Astrophysics | Harvard & Smithsonian, 

and the Jet Propulsion Laboratory. 

 

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	___________________________________________________________________________________
	Item       Group    n    Percent
	___________________________________________________________________________________
	Affiliation     Scout Group   15     15
	Camp Group   27     27
	Private School   58     58
	Pre-Post Group
	Full Group    Spatial/Inventory   55     55
	Inventory/Spatial   45     45
	Scout Group   Spatial/Inventory    7     46.7
	Inventory/Spatial    8     53.3
	Camp Group   Spatial/Inventory   16     59.3
	Inventory/Spatial   11     40.7
	Private School   Spatial/Inventory   32     55.2
	Inventory/Spatial   6     44.8
	Age
	Full Group    9     21     21
	10     25     25
	11     38     38
	12     16     16
	Scout Group    9      6     40
	10      8     53.3
	11      1     6.7
	12     --      --
	Camp Group    9     --      --
	10      4     14.8
	11      9     33.3
	12     14     51.9
	Private School    9     15     25.9
	10     13     22.4
	11     28     38
	12     2     3.4
	Item      Group    n     Percent
	___________________________________________________________________________________
	Grade
	Full Group      4     27     27
	5     25     25
	6     32     32
	7     16     16
	Gender       Female     99     99
	Nonbinary       1     1
	Ethnicity
	Full Group   Caucasian     45     45
	Black/African    11     11
	Hispanic/Latina    9     9
	Other      30     30
	Prefer NtA     5     5
	Scout Group  Caucasian     11     73.3
	Black/African    --     --
	Hispanic/Latina    --     --
	Other      2     13.3
	Prefer NtA     2     13.3
	Camp Group  Caucasian     --     --
	Black/African    8     29.6
	Hispanic/Latina    8     29.6
	Other      10     37
	Prefer NtA     1     3.7
	Private School  Caucasian     34     58.6
	Black/African    3     5.2
	Hispanic/Latina    1     1.7
	Other      18     31
	Prefer NtA     2     3.4
	Self-rating
	Full Group    1     2     2
	2     --     --
	3     1     1
	4     6     6
	5     21     21
	Item        Group     n     Percent
	___________________________________________________________________________________
	6     19     19
	7     20     20
	8     18     18
	9     7     7
	10     6     6
	Scout Group    1     --     --
	2     --     --
	3     --     --
	4     2     13.3
	5     3     20
	6     1     6.7
	7     2     13.3
	8     2     13.3
	9     3     20
	10     2     13.3
	Camp Group    1     --     --
	2     --     --
	3     1     3.7
	4     1     3.7
	5     8     29.6
	6     7     25.9
	7     7     25.9
	8     3     11.1
	9     --     --
	10     --     --
	Private School    1     2     3.4
	2     --     --
	3     --     --
	4     3     5.2
	5     10     17.2
	6     11     19
	7     11     19
	8     13     22.4
	9     4     6.9
	10     4     6.9
	Item      Group    n    Percent
	___________________________________________________________________________________
	Video Hours
	Full Group   0     48    48
	1     24    24
	2     14    14
	3     6    6
	4     2    2
	5     2    2
	6     1    1
	7     2    2
	8     1    1
	Scout Group   0     5    33.3
	1     5    33.3
	2     3    20
	3     1    6.7
	4     1    6.7
	5     --    --
	6     --    --
	7     --    --
	8     --    --
	Camp Group   0     6    22.2
	1     10    37
	2     5    18.5
	3     1    3.7
	4     1    3.7
	5     1    3.7
	6     1    3.7
	7     1    3.7
	8     1    3.7
	Private School   0     37    63.8
	1     9    15.5
	2     6    10.3
	3     4    6.9
	4     --    --
	5     1    1.7
	Item     Group    n    Percent
	___________________________________________________________________________________
	6     --    --
	7     1    1.7
	8     --    --
	Table 2. Means and Standard Deviations for Age, Self-rating, and Video Hours for Full Group, and by Affiliation
	___________________________________________________________________________________
	Item      Mean        Standard Deviation
	___________________________________________________________________________________
	Age
	Full Group    10.49         1
	Scout Group   9.67         0.62
	Camp Group   11.37         0.74
	Private School   10.29         0.9
	Self-rating
	Full Group    6.55         1.81
	Scout Group   7.07         2.12
	Camp Group   6         1.24
	Private School   6.67         1.91
	Video Hours
	Full Group    1.16         1.68
	Scout Group   1.2         1.21
	Camp Group   1.96         2.19
	Private School   0.78         1.38
	Table 3. Scores for the Spatial Skills and STEM Inventories for the Full Sample and by Pre Post Conditions within Affiliation
	___________________________________________________________________________________
	Item     Group   n   Mean    SD
	___________________________________________________________________________________
	Spatial Total Score   Full Group   100   2.89    1.6
	Scout Group  15   0.87    1.19
	Camp Group  27   2.78    1.6
	Item     Group   n      Mean    SD
	___________________________________________________________________________________
	Private School   58   3.47    1.23
	Inventory Total Score   Full Group   100   121.08   35.5
	Scout Group  15   114.4    32.92
	Camp Group  27   104    33.88
	Private School   58   130.76   33.97
	Spatial Pre    Full Group   55   3.24    1.48
	Scout Group  7   1.14    1.35
	Camp Group  16   3.06    1.77
	Private School   32   3.78    0.79
	Spatial Post    Full Group   45   2.47    1.66
	Scout Group  8   0.63    1.06
	Camp Group  11   2.36    1.29
	Private School   26   3.08    1.55
	Inventory Pre   Full Group   45   116.24   43.28
	Scout Group  8   105.63   40.88
	Camp Group  11   89    42.04
	Private School   26   131.04   39.14
	Inventory Post   Full Group   55   125.04   27.36
	Scout Group  7   124.43   18.94
	Camp Group  16   114.31   23.08
	Private School   32   130.53   29.77
	Table 4. Reliability Statistics using Cronbach’s Alpha for STEM Inventory Subscales and Spatial Skills ________________________________________________________________________________
	Subscale    Cronbach’s Alpha     Published Cronbach’s
	Alpha
	___________________________________________________________________________________
	Science     .91        .84
	Math      .91        .88
	Subscale    Cronbach’s Alpha     Published Cronbach’s
	Alpha
	___________________________________________________________________________________
	Engineering    .90        .92
	Technology     .91        .91
	Career     .89        .93
	Spatial     .70        --
	Table 5. Results of Multivariate Analysis of Variance
	___________________________________________________________________________________
	Effect    Value  F  Hypostudy  Error  Sig. Partial Eta            df  df   Squared
	________________________________________________________________________________
	Intercept
	Pillai’s Trace  .363  26.26   2  92  <.001  .363
	Wilks’ Lambda  .637  26.26   2  92  <.001  .363
	Hotelling’s   .571  26.26   2  92  <.001  .363
	Trace
	Roy’s Largest     .571  26.26   2  92  <.001  .363
	Root
	Self-rating
	Pillai’s Trace  .110  5.69   2  92  .005  .110
	Wilks’ Lambda  .890  5.69   2  92  .005  .110
	Hotelling’s Trace  .124  5.69   2  92  .005  .110
	Roy’s Largest Root   .124  5.69   2  92  .005  .110
	Affiliation
	Pillai’s Trace  .428  12.66   4  186  <.001  .363
	Wilks’ Lambda  .596  13.58   4  184  <.001  .363
	Hotelling’s Trace  .637  14.50   4  182  <.001  .363
	Roy’s Largest  .566  26.32   2  93  <.001  .363
	Root
	Effect    Value  F  Hypostudy  Error df Sig.   Partial Eta           df         Squared __________________________________________________________________________________
	PRe-Post Condition     Pillai’s Trace   .082  4.11   2  92  .019  .082
	Wilks’ Lambda  . 918  4.11   2  92  .019  .082
	Hotelling’s Trace  .089  4.11   2  92  .019  .082
	Roy’s Largest Root    .089  4.11   2  92  .019  .082
	Affiliation*Pre
	Post
	Pillai’s Trace   .030  .71   4  186  .586  .015
	Wilks’ Lambda   .970  .71   4  184  .586  .015
	Hotelling’s Trace  .031  .71   4  182  590  .015
	Roy’s Largest     .030  1.41   2  93    .015
	Root
	Table 6. Results of Univariate Analysis of Variance Between-subjects Effects
	___________________________________________________________________________________
	Type lll Sum   Mean      Partial Eta
	Source   Dependent Variable  of Squares df Square  F    Sig.  Squared ___________________________________________________________________________________
	Affiliation  Spatial Total  78.14  2 39.36  22.96  <.001  0.331
	Inventory Total 12464.33 2 6232.17 6.1  0.003  0.116
	Pre Post   Spatial Total  7.44  1 7.438  4.34  0.04  0.045
	Condition  Inventory Total 3751.09 1 3751.09 3.67  0.058  0.038
	Affiliation *  Spatial Total  0.14  2 0.067  0.04  0.961  0.001
	Pre Post  Inventory Total 2879.38 2 1439.69 1.41  0.25  0.029
	Condition
	Error   Spatial Total  159.39 93 1.71
	Inventory Total 95069.43 93 1022.25
	Type lll Sum  Mean      Partial Eta
	Source   Dependent Variables   df   f  Sig.         of Squares  Square      Squared _______________________________________________________________________________________________________
	Total   Spatial Total  1089  100
	Inventory Total 1590788 100
	Corrected  Spatial Total  253.79  99
	Total   Inventory Total 124751.36 99

