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 www.scholink.org/ojs/index.php/csm Communication, Society and Media Vol. 5, No. 2, 2022 38 Published by SCHOLINK INC. 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). www.scholink.org/ojs/index.php/csm Communication, Society and Media Vol. 5, No. 2, 2022 39 Published by SCHOLINK INC. 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). www.scholink.org/ojs/index.php/csm Communication, Society and Media Vol. 5, No. 2, 2022 40 Published by SCHOLINK INC. 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 www.scholink.org/ojs/index.php/csm Communication, Society and Media Vol. 5, No. 2, 2022 41 Published by SCHOLINK INC. 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. www.scholink.org/ojs/index.php/csm Communication, Society and Media Vol. 5, No. 2, 2022 42 Published by SCHOLINK INC. 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 www.scholink.org/ojs/index.php/csm Communication, Society and Media Vol. 5, No. 2, 2022 43 Published by SCHOLINK INC. 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. www.scholink.org/ojs/index.php/csm Communication, Society and Media Vol. 5, No. 2, 2022 44 Published by SCHOLINK INC. 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 www.scholink.org/ojs/index.php/csm Communication, Society and Media Vol. 5, No. 2, 2022 45 Published by SCHOLINK INC. 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 www.scholink.org/ojs/index.php/csm Communication, Society and Media Vol. 5, No. 2, 2022 46 Published by SCHOLINK INC. 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. www.scholink.org/ojs/index.php/csm Communication, Society and Media Vol. 5, No. 2, 2022 47 Published by SCHOLINK INC. 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. www.scholink.org/ojs/index.php/csm Communication, Society and Media Vol. 5, No. 2, 2022 48 Published by SCHOLINK INC. 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. www.scholink.org/ojs/index.php/csm Communication, Society and Media Vol. 5, No. 2, 2022 49 Published by SCHOLINK INC. 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. www.scholink.org/ojs/index.php/csm Communication, Society and Media Vol. 5, No. 2, 2022 50 Published by SCHOLINK INC. 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 www.scholink.org/ojs/index.php/csm Communication, Society and Media Vol. 5, No. 2, 2022 51 Published by SCHOLINK INC. 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 www.scholink.org/ojs/index.php/csm Communication, Society and Media Vol. 5, No. 2, 2022 52 Published by SCHOLINK INC. 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 www.scholink.org/ojs/index.php/csm Communication, Society and Media Vol. 5, No. 2, 2022 53 Published by SCHOLINK INC. 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 www.scholink.org/ojs/index.php/csm Communication, Society and Media Vol. 5, No. 2, 2022 54 Published by SCHOLINK INC. 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 www.scholink.org/ojs/index.php/csm Communication, Society and Media Vol. 5, No. 2, 2022 55 Published by SCHOLINK INC. 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 www.scholink.org/ojs/index.php/csm Communication, Society and Media Vol. 5, No. 2, 2022 56 Published by SCHOLINK INC. 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 www.scholink.org/ojs/index.php/csm Communication, Society and Media Vol. 5, No. 2, 2022 57 Published by SCHOLINK INC. 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 www.scholink.org/ojs/index.php/csm Communication, Society and Media Vol. 5, No. 2, 2022 58 Published by SCHOLINK INC. 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 www.scholink.org/ojs/index.php/csm Communication, Society and Media Vol. 5, No. 2, 2022 59 Published by SCHOLINK INC. 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 www.scholink.org/ojs/index.php/csm Communication, Society and Media Vol. 5, No. 2, 2022 60 Published by SCHOLINK INC. 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 www.scholink.org/ojs/index.php/csm Communication, Society and Media Vol. 5, No. 2, 2022 61 Published by SCHOLINK INC. 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 www.scholink.org/ojs/index.php/csm Communication, Society and Media Vol. 5, No. 2, 2022 62 Published by SCHOLINK INC. 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. www.scholink.org/ojs/index.php/csm Communication, Society and Media Vol. 5, No. 2, 2022 63 Published by SCHOLINK INC. 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. www.scholink.org/ojs/index.php/csm Communication, Society and Media Vol. 5, No. 2, 2022 64 Published by SCHOLINK INC. 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. 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Retrieved from https://eric.ed.gov/?id=EJ1052049 ___________________________________________________________________________________ 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