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Exploring the Relation between Spatial Abilities and STEM Expertise

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Title: Exploring the Relation between Spatial Abilities and STEM Expertise
Language: English
Authors: Eleni Tomai (ORCID 0000-0003-1162-7389), Margarita Kokla (ORCID 0000-0002-0369-7099), Christos Charcharos, Marinos Kavouras (ORCID 0000-0001-7563-0793)
Source: Journal of Geography in Higher Education. 2024 48(5):734-751.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 18
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Education Level: Secondary Education
Descriptors: Spatial Ability, STEM Education, Individual Differences, Expertise, Foreign Countries, Adults, Males, Secondary Education, Intellectual Disciplines, Attitudes, Lay People, Student Attitudes, Nonparametric Statistics, Statistical Analysis
Geographic Terms: Greece
DOI: 10.1080/03098265.2023.2263735
ISSN: 0309-8265
1466-1845
Abstract: Small-scale spatial abilities that involve the mental representation and transformation of two- and three-dimensional images and manipulation of objects at table-top have been studied extensively and are considered predictive of both interest and success in STEM disciplines. However, research investigating the relation of large-scale spatial abilities to STEM disciplines is sparse. The paper describes the design and implementation of a study for assessing individual differences (if any) in spatial abilities in both figural and environmental spaces between STEM experts (with over 10 years of experience) and non-experts (individuals without any studies in STEM fields). Participants' performance in 16 small-, 10 large-scale tasks, and one self-assessment questionnaire at environmental scale was evaluated to assess their corresponding abilities. Results indicate differences between experts and non-experts, which are mostly highlighted for small-scale abilities where experts outperform non-experts. At large scale, some significant differences are identified, which also favor experts. Correlations among the variables tested provide evidence that different abilities are prominent between experts and non-experts.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1445861
Database: ERIC
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  Value: <anid>AN0180474575;jgh01nov.24;2024Oct28.07:16;v2.2.500</anid> <title id="AN0180474575-1">Exploring the relation between spatial abilities and STEM expertise </title> <p>Small-scale spatial abilities that involve the mental representation and transformation of two- and three-dimensional images and manipulation of objects at table-top have been studied extensively and are considered predictive of both interest and success in STEM disciplines. However, research investigating the relation of large-scale spatial abilities to STEM disciplines is sparse. The paper describes the design and implementation of a study for assessing individual differences (if any) in spatial abilities in both figural and environmental spaces between STEM experts (with over 10 years of experience) and non-experts (individuals without any studies in STEM fields). Participants' performance in 16 small-, 10 large-scale tasks, and one self-assessment questionnaire at environmental scale was evaluated to assess their corresponding abilities. Results indicate differences between experts and non-experts, which are mostly highlighted for small-scale abilities where experts outperform non-experts. At large scale, some significant differences are identified, which also favor experts. Correlations among the variables tested provide evidence that different abilities are prominent between experts and non-experts.</p> <p>Keywords: spatial abilities; small-scale; large-scale; survey; STEM expertise</p> <hd id="AN0180474575-2">Introduction</hd> <p>Spatial thinking is recognized as a multifaceted ability at the forefront of human intelligence (Wang, [<reflink idref="bib55" id="ref1">55</reflink>]). Moreover, this ability is a key competency for Science, Technology, Engineering, and Mathematics (STEM) domains, since spatial skills play an important role in the development of STEM expertise (Uttal & Cohen, [<reflink idref="bib51" id="ref2">51</reflink>]; Wai et al., [<reflink idref="bib54" id="ref3">54</reflink>]).</p> <p>Scale is a crucial parameter in defining space (Montello, [<reflink idref="bib42" id="ref4">42</reflink>]) and in studying the relation between STEM learning and spatial skills (Nazareth et al., [<reflink idref="bib43" id="ref5">43</reflink>]). Spatial thinking is a multi-dimensional ability comprising different cognitive mechanisms for manipulating and visualizing objects (small-scale spatial abilities) or interacting with the environment (large-scale spatial abilities). Small-scale spatial skills involve different abilities, such as the ability to rotate a two- or three-dimensional figure (mental rotation) or the ability to mentally transform or manipulate the spatial properties of an object (spatial visualization). They have been studied extensively in relation to various domains such as physics, chemistry, and mathematics and are considered predictive of both interest and success in STEM disciplines.</p> <p>Large-scale or environmental spatial abilities are also important in many STEM domains and involve spatial reasoning and solving spatial problems occurring in the environment (Atit et al., [<reflink idref="bib4" id="ref6">4</reflink>]). Typical tasks that are used to assess large-scale spatial abilities are scene and landmark recognition of a learned environment, distance and direction estimations, drawing a sketch map of the environment, giving and interpreting directions, and pointing to local and distant locations (Hegarty et al., [<reflink idref="bib16" id="ref7">16</reflink>]). In contrast to small-scale spatial abilities, large-scale spatial abilities have not been extensively studied regarding their contribution to STEM expertise (Nazareth et al., [<reflink idref="bib43" id="ref8">43</reflink>]).</p> <p>Spatial abilities are assessed using different types of tests (Lee & Jo, [<reflink idref="bib30" id="ref9">30</reflink>]); small-scale spatial abilities are measured using traditional psychometric tests, while large-scale spatial abilities are usually measured using virtual environments that simulate the real-world or multiple-choice questions and performance tasks; finally, the perception of participants' own spatial skills at different scales is measured using self-assessment questionnaires.</p> <p>Several studies have delved into the association between spatial abilities at different scales of space providing insight into their commonalities and differences and the extent to which they reflect the same or different underlying skills. Their results provided evidence that although small-scale and large-scale spatial abilities are correlated, they are at least partially (Hegarty & Waller, [<reflink idref="bib21" id="ref10">21</reflink>]; Hegarty et al., [<reflink idref="bib19" id="ref11">19</reflink>]; Jansen et al., [<reflink idref="bib23" id="ref12">23</reflink>]), if not totally, dissociated (Jansen, [<reflink idref="bib22" id="ref13">22</reflink>]).</p> <p>The paper envisions shedding light on whether differences in spatial abilities between STEM-experts and non-experts can be identified, and at which scale; figural or environmental (Montello, [<reflink idref="bib42" id="ref14">42</reflink>]). Based on the evidence on the dissociation of small-scale and large-scale spatial abilities, we describe the design and implementation of a study for assessing participants' performance in 16 small-scale and 10 large-scale tasks that required participants' interaction with the physical environment.</p> <p>The document is structured as follows: the next section is dedicated to related work presenting surveys with similar underlying research questions. The following two sections, i.e. Method and Results, detail the survey and its outcomes. Finally, in the last section, we draw some conclusions and discuss the results.</p> <hd id="AN0180474575-3">Related work</hd> <p>A plethora of surveys have been conducted that assess small-scale spatial abilities, such as mental rotation and 3D visualization, in STEM disciplines such as mathematics (Atit et al., [<reflink idref="bib3" id="ref15">3</reflink>]; Gilligan-Lee et al., [<reflink idref="bib13" id="ref16">13</reflink>]), chemistry (Harle & Towns, [<reflink idref="bib14" id="ref17">14</reflink>]; Stieff, [<reflink idref="bib50" id="ref18">50</reflink>]), medicine (Hegarty et al., [<reflink idref="bib18" id="ref19">18</reflink>]; Kalun et al., [<reflink idref="bib26" id="ref20">26</reflink>]; Maurice-Ventouris et al., [<reflink idref="bib36" id="ref21">36</reflink>]), and geosciences (Liben & Titus, [<reflink idref="bib31" id="ref22">31</reflink>]; McLaughlin & Bailey, [<reflink idref="bib39" id="ref23">39</reflink>]). These context-independent spatial skills are considered fundamental since they constitute the basis for the field-dependent spatial skills required to develop STEM expertise (Atit et al., [<reflink idref="bib4" id="ref24">4</reflink>]; Uttal & Cohen, [<reflink idref="bib51" id="ref25">51</reflink>]; Wai et al., [<reflink idref="bib54" id="ref26">54</reflink>]).</p> <p>However, studies investigating the relation of large-scale spatial abilities to STEM expertise are sparse. Hegarty et al. ([<reflink idref="bib17" id="ref27">17</reflink>]) analyzed self-assessment questionnaires regarding small- and large-scale spatial abilities as well as verbal abilities from different scientists. Participants belonging to STEM disciplines had higher self-report ratings in both spatial abilities' questionnaires, whereas participants in humanities, social sciences, psychology, and other professions had higher self-report rating in the verbal ability questionnaire. However, within the STEM group, different behaviors were identified; geoscientists had the highest self-report ratings in both spatial abilities' questionnaires, geographers, on the other hand, had high self-ratings of environmental spatial abilities and engineers of small-scale spatial abilities. Other specialists' (non-STEM) self-ratings in spatial abilities were around the mean.</p> <p>Nazareth et al. ([<reflink idref="bib43" id="ref28">43</reflink>]) investigated the relation between STEM expertise and large-scale abilities and more specifically the relation between geology expertise and navigation abilities using a large-scale virtual environment. Highly experienced geologists and psychologists, as well as a comparison group of non-STEM undergraduate students, completed a pointing task, a model-building task, and the Santa Barbara Sense of Direction Scale (SOD) (Hegarty et al., [<reflink idref="bib20" id="ref29">20</reflink>]). Results indicated that geologists exhibit higher navigational abilities in comparison to psychologists and non-STEM students. At the same time, no significant differences in navigation performance were observed between psychologists and non-STEM students.</p> <p>The present study extends the investigation of the relation between STEM expertise and spatial abilities in two aspects. The first is that an extensive number of spatial abilities are assessed using a variety of spatial tests in contrast to most surveys that test a limited number of spatial abilities. As Ormand et al. ([<reflink idref="bib44" id="ref30">44</reflink>]) point out, it takes a suite of tests to measure an individual's spatial abilities since empirical evidence shows that someone "may excel at some spatial thinking skills while struggling with others". The second aspect is that, besides the self-reported measure of the sense of direction, 10 large-scale tasks have been assessed by direct interaction of participants with the physical environment, which although being more time-consuming and demanding provides a more objective measure of large-scale spatial abilities.</p> <hd id="AN0180474575-4">Method</hd> <p></p> <hd id="AN0180474575-5">Participants</hd> <p>The study took place in Levadea town in central Greece over a period of 6 months. Data were collected from subjects who participated voluntarily. Age constituted an exclusion criterion since spatial thinking gradually decreases from the age of 65 (McAvan et al., [<reflink idref="bib37" id="ref31">37</reflink>]). Furthermore, because the aim was to examine the effect of STEM expertise on spatial thinking, the study involved participants with extended expertise (more than 10 years); hence, participants' age is 35 and above. The sample of this study constitutes 60 participants (30 males); 30 STEM experts (21 males) and 30 non-experts (9 males). The majority of experts have studied engineering (civil, surveying, and mechanical), while a few have studied other STEM disciplines (geography, informatics, mathematics, and biology) or medicine. Non-experts either have finished secondary education with no further studies or have studied humanities, law, economics, etc. Participants' age ranges from 35 to 64 years old (Mean = 48.5, SD = 8.5), entailing STEM experience of participants, when relevant, between 10 and 40 years.</p> <hd id="AN0180474575-6">Spatial abilities measurement instruments</hd> <p>Overall, 16 small-scale abilities, 10 large-scale abilities, and one self-assessment scale were measured (Table 1). Two batteries of questionnaires were utilized; one of paper-and-pencil questionnaires assessed small-scale and the other one, large-scale abilities. All small-scale questionnaires are well-known (Table 2) and extensively used in similar studies, while large-scale questionnaires were selected through extensive literature review and have been customized for the study area.</p> <p>Table 1. Spatial abilities assessed and their definitions. References can be found at the uploaded supplementary material due to space limitations.</p> <p> <ephtml> <table><thead><tr><td>Spatial Ability (Abbreviation)</td><td>Definition</td></tr></thead><tbody><tr><td>Small-scale</td></tr><tr><td>Closure Speed (CS)</td><td>Identify an incomplete/distorted picture or combine disconnected, vague, visual stimuli into a meaningful whole (Lohman et al., <xref ref-type="bibr" rid="bibr35">1987</xref>).</td></tr><tr><td>Figural Fluency (FF)</td><td>Produce novel figures (Ruff, <xref ref-type="bibr" rid="bibr46">2011</xref>).</td></tr><tr><td>Flexibility of Closure (FC)</td><td>Break one gestalt from another or find a simple figure embedded in a more complex shape (Lohman et al., <xref ref-type="bibr" rid="bibr35">1987</xref>).</td></tr><tr><td>Mental Animation (MA)</td><td>Infer the state of one component of a system given information about the states of the other system components and the relations between the components (Hegarty, <xref ref-type="bibr" rid="bibr15">1992</xref>).</td></tr><tr><td>Mental Folding (MF)</td><td>Mentally imagine the folding of a 2- dimensional pattern into a mental representation of a 3- dimensional box, or mentally unfolding the 3-dimensional box into a 2-dimensional pattern (Lohman, <xref ref-type="bibr" rid="bibr34">2000</xref>).</td></tr><tr><td>Mental Rotation (MR)</td><td>Rapidly and accurately rotate a two- or three-dimensional figure (Linn & Petersen, <xref ref-type="bibr" rid="bibr32">1985</xref>).</td></tr><tr><td>Perceptual Speed (PS)</td><td>Speed in comparing figures or symbols, scanning to find figures or symbols (Ekstrom et al., <xref ref-type="bibr" rid="bibr11">1976</xref>).</td></tr><tr><td>Perspective Taking (PT)</td><td>Identify changes in the point of view of object or oneself with respect to the environment (Hegarty et al., <xref ref-type="bibr" rid="bibr19">2006</xref>).</td></tr><tr><td>Visual Memory (VM)</td><td>Short term memory of visual stimuli (Lohman, <xref ref-type="bibr" rid="bibr33">1979</xref>).</td></tr><tr><td>Spatial Orientation (SO)</td><td>Determine how an object or scene will appear when viewed from a new perspective (Lohman, <xref ref-type="bibr" rid="bibr33">1979</xref>).</td></tr><tr><td>Spatial Scanning (SC)</td><td>Visualise a path out of a maze or a field with many obstacles (Schneider & McGrew, <xref ref-type="bibr" rid="bibr48">2012</xref>)</td></tr><tr><td>Visual Penetrative Ability (VPA)</td><td>Mentally imagine what is inside of a solid object (Kali & Orion, <xref ref-type="bibr" rid="bibr25">1996</xref>).</td></tr><tr><td>Spatial Visualization (SV)</td><td>Apprehend a spatial form, shape, or scene while often at the same time rotating it in two or three dimensions one or more times (Lohman et al., <xref ref-type="bibr" rid="bibr35">1987</xref>).</td></tr><tr><td>Spatial Perception (SP)</td><td>Identify spatial relations among task components in spite of distracting information, such as identifying an object whose orientation is different from the others (Linn & Petersen,<xref ref-type="bibr" rid="bibr32">1985</xref>).</td></tr><tr><td>Serial Integration (SI)</td><td>Integrate temporally speeded visual stimuli. (Lohman et al., <xref ref-type="bibr" rid="bibr35">1987</xref>)</td></tr><tr><td>Visuospatial Working Memory (VWM)</td><td>The memory system devoted to maintaining and processing spatial information (Meneghetti et al., <xref ref-type="bibr" rid="bibr40">2014</xref>). It processes environmental information using both indirect sources, such as maps Coluccia & Louse, <xref ref-type="bibr" rid="bibr8">2004</xref>).</td></tr><tr><td>Large-scale</td></tr><tr><td>Draw Sketch Maps (SMAP)</td><td>An external measure of the self-reported feeling of orientation (Coluccia et al., <xref ref-type="bibr" rid="bibr7">2007</xref>).</td></tr><tr><td>Landmark Recognition (LR)</td><td>Relate to the landmarks along the routes (Sas & Mohd Noor, <xref ref-type="bibr" rid="bibr47">2009</xref>).</td></tr><tr><td>Scene Recognition (SR)</td><td>Recognize scenes from the original walk (Allen et al., <xref ref-type="bibr" rid="bibr2">1996</xref>).</td></tr><tr><td>Scene Sequencing (SS)</td><td>Nonmetric temporal-spatial knowledge of a route (Allen et al., <xref ref-type="bibr" rid="bibr2">1996</xref>)</td></tr><tr><td>Give Directions (GDIR)</td><td>External measure of an individuals' knowledge of the environment (Blades & Medlicott, <xref ref-type="bibr" rid="bibr5">1992</xref>).</td></tr><tr><td>Interpret Directions (IDIR)</td><td>Translate route information (verbally given) in a map like representation of the travel (Juan-Espinosa et al., <xref ref-type="bibr" rid="bibr24">2000</xref>).</td></tr><tr><td>Estimate Directions (EDIR)</td><td>Provide Euclidean or "crow-fly" directions estimates from one viewpoint to a series of unseen target locations along the walk (Allen et al., <xref ref-type="bibr" rid="bibr2">1996</xref>).</td></tr><tr><td>Estimate Distances (EDIST)</td><td>Provide Euclidean or "crow-fly" distance estimates from one viewpoint to a series of unseen target locations along the walk (Allen et al., <xref ref-type="bibr" rid="bibr2">1996</xref>).</td></tr><tr><td>Path Integration (PI)</td><td>Integrate self-motion information to estimate one's current position and orientation relative to the origin (Wan et al., <xref ref-type="bibr" rid="bibr56">2010</xref>).</td></tr><tr><td>Point to local and distant locations (PLOC)</td><td>Point to the direction of the target object relative to the imagined heading (Shelton, & McNamara, <xref ref-type="bibr" rid="bibr49">2001</xref>).</td></tr></tbody></table> </ephtml> </p> <p>Table 2. Assessment instruments used.</p> <p> <ephtml> <table><thead><tr><td>Spatial Ability</td><td>Assessment Instrument Used</td></tr></thead><tbody><tr><td>CS</td><td>Gestalt Completion Test (Ekstrom et al., <xref ref-type="bibr" rid="bibr11">1976</xref>)</td></tr><tr><td>FF</td><td>Five Point Test (5PT) (Regard et al., <xref ref-type="bibr" rid="bibr45">1982</xref>)</td></tr><tr><td>FC</td><td>Hidden Patterns Test (Ekstrom et al., <xref ref-type="bibr" rid="bibr11">1976</xref>)</td></tr><tr><td>MF</td><td>Surface Development Test (ibid.)</td></tr><tr><td>MR</td><td>Mental Rotation Test (Vandenberg & Kuse, <xref ref-type="bibr" rid="bibr52">1978</xref>)</td></tr><tr><td>PS</td><td>Identical Pictures Test (Ekstrom et al., <xref ref-type="bibr" rid="bibr11">1976</xref>)</td></tr><tr><td>PT</td><td>Spatial Orientation Test (Kozhevnikov & Hegarty, <xref ref-type="bibr" rid="bibr28">2001</xref>)</td></tr><tr><td>VM</td><td>Building Memory Test (Ekstrom et al., <xref ref-type="bibr" rid="bibr11">1976</xref>)</td></tr><tr><td>SO</td><td>Revised Gilford Zimmerman Orientation Test (Kyritsis & Gulliver, <xref ref-type="bibr" rid="bibr29">2009</xref>)</td></tr><tr><td>SC</td><td>Map Planning Test (Ekstrom et al., <xref ref-type="bibr" rid="bibr11">1976</xref>)</td></tr><tr><td>VPA</td><td>Santa Barbara Solids Test (Cohen & Hegarty, <xref ref-type="bibr" rid="bibr6">2012</xref>)</td></tr><tr><td>SV</td><td>Form Board Test (Ekstrom et al., <xref ref-type="bibr" rid="bibr11">1976</xref>)</td></tr><tr><td>SP</td><td>Water Level Task (Vasta & Liben, <xref ref-type="bibr" rid="bibr53">1996</xref>)</td></tr><tr><td>SI</td><td>Successive Figures Test (McDaniel, <xref ref-type="bibr" rid="bibr38">1974</xref>)</td></tr><tr><td>VWM</td><td>Corsi Block-Tapping Test (Corsi, <xref ref-type="bibr" rid="bibr9">1972</xref>)</td></tr><tr><td>SOD</td><td>Santa Barbara Sense of Direction Scale (Hegarty et al., <xref ref-type="bibr" rid="bibr20">2002</xref>)</td></tr></tbody></table> </ephtml> </p> <p>Most large-scale factors (LR, SR, GDIR, EDIR, EDIST, PLOC, SS, PI) have been assessed using elements (landmarks, distances, locations, and directions) of the 1.1 km route (Figure 1) at the northern part of Levadea, selected because it fulfilled the following criteria: to be simple with straight-line segments and limited number of navigational decision points (Sas & Mohd Noor, [<reflink idref="bib47" id="ref32">47</reflink>]), to be the optimal route connecting start and destination (Michon & Denis, [<reflink idref="bib41" id="ref33">41</reflink>]) and to have adequate landmarks (Allen, [<reflink idref="bib1" id="ref34">1</reflink>]).</p> <p>Graph: Figure 1. The study route. Numbered locations indicate the landmarks along the route.</p> <hd id="AN0180474575-7">Procedure</hd> <p>Participants were informed about the aim of the study and filled in a questionnaire with demographic data. Afterward, they completed a battery of small-scale questionnaires. The test administrator provided the necessary instructions for each spatial task, kept the time limit but did not intervene in any way. Subsequently, participants traversed the study route accompanied by the test administrator who indicated the selected landmarks. Immediately after, participants filled in a battery of large-scale questionnaires. Participants were tested individually and followed the same route only once. The whole process lasted approximately 3 h per participant. Questionnaires were collected anonymously.</p> <hd id="AN0180474575-8">Results</hd> <p></p> <hd id="AN0180474575-9">Data pre-processing</hd> <p>Participants' scores have been calculated following the norm, meaning that scoring methods, either as initially suggested by the developers of the tests/questionnaires used (especially for the small-scale factors) or as described in previous similar published studies, have been adopted. For the factors Scene Sequencing (SS) and Estimate Distances (EDIST), scores have been calculated as in previously published studies (Hegarty et al., [<reflink idref="bib19" id="ref35">19</reflink>]; Kirasic, [<reflink idref="bib27" id="ref36">27</reflink>]) as the correlation between log estimated distances and log actual distances using Kendall's tau. For the factors Landmark Recognition (LR) and Scene Recognition (SR), scores have been calculated by subtracting wrong answers from the correct ones.</p> <p>The sample consisted of two equally numbered groups of 30 participants each: STEM experts and non-experts, with mean ages and standard deviations of 49.7 ± 8.6 and 47.3 ± 8.3, respectively.</p> <hd id="AN0180474575-10">Differences between STEM experts and non-experts</hd> <p>Group performance was tested for normality; however, according to the Shapiro–Wilk test, groups depart from normality in 18 out of the 27 variables. Hence, for these variables, the Mann–Whitney <emph>U</emph>-test was used to compare groups' performance, while for the remaining ones the t-test was applied.</p> <p>Welch's t-test revealed that experts' scores in FF, FC, SO, and SV (all small scale) were significantly higher than non-experts' scores (Figures 2–5). In order to calculate the effect size, Hedges' g*s based on the non-pooled standard deviation was preferred over Cohen's d due to the small-group sizes and the unequal variance assumption (Delacre et al., [<reflink idref="bib10" id="ref37">10</reflink>]). Effect sizes show that for these factors, the sizes of these differences are very large, per Funder and Ozer ([<reflink idref="bib12" id="ref38">12</reflink>]) rules.</p> <p>Graph: Figure 2. Welch's t-test revealed that experts outperform non-experts at figural fluency (small-scale). This difference is significant (p =.004) with very large effect size (g*Hedges = 0.77).</p> <p>Graph: Figure 3. Welch's t-test revealed that experts outperform non-experts at flexibility of Closure (small-scale). This difference is significant (p =.017) with very large effect size (g*Hedges = 0.63).</p> <p>Graph: Figure 4. According to Welch's t-test, experts outperform non-experts at spatial orientation (small-scale). This difference is significant (p =.007) with very large effect size (g*Hedges = 0.71).</p> <p>Graph: Figure 5. According to Welch's t-test, experts outperform non-experts at spatial visualization (small-scale). This difference is significant (p =.017) with very large effect size (g*Hedges = 0.62).</p> <p>The Mann–Whitney U tests indicated that experts outperformed non-experts in six small-scale abilities: MA, MF, PT, VPA, SP, and SI (Figures 6–11), and in two large-scale abilities: SMAP and SS (Figures 12 and 13). The effect sizes as demonstrated by the rank-biserial correlation (</p> <p>Graph</p> <p> <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><msubsup><mi>r</mi><mrow><mi>b</mi><mi>i</mi><mi>serial</mi></mrow><mrow><mi mathvariant="italic">rank</mi></mrow></msubsup></math> </ephtml> ) range from large to very large per Funder and Ozer rules (ibid).</p> <p>Graph: Figure 6. The Mann–Whitney test shows that experts (mean Rank = 24.8) outperform non-experts (mean Rank = 36.3) in mental animation (small-scale). This difference is significant (p =.009) with large effect size (r = 0.38).</p> <p>Graph: Figure 7. The Mann–Whitney test shows that experts (mean Rank = 22.77) outperform non-experts (mean Rank = 38.23) in Mental folding (small-scale). This difference is significant (p <.001) with very large effect size (r = 0.52).</p> <p>Graph: Figure 8. The Mann–Whitney test shows that experts (mean Rank = 24.98) outperform non-experts (mean Rank = 36.02) in perspective taking (small-scale). This difference is significant (p =.015) with large effect size (r = 0.37).</p> <p>Graph: Figure 9. The Mann–Whitney test shows that experts (mean Rank = 22.43) outperform non-experts (mean Rank = 38.57) in visual penetrative ability (small-scale). This difference is significant (p <.001) with very large effect size (r = 0.54).</p> <p>Graph: Figure 10. The Mann–Whitney test shows that experts (mean Rank = 25.13) outperform non-experts (mean Rank = 35.87) in spatial Perception (small-scale). This difference is significant (p =.008) with large effect size (r = 0.36).</p> <p>Graph: Figure 11. The Mann–Whitney test shows that experts (mean Rank = 23.98) outperform non-experts (mean Rank = 37.02) in Serial Integration (small-scale). This difference is significant (p =.003) with very large effect size (r = 0.43).</p> <p>Graph: Figure 12. The Mann–Whitney test shows that experts (mean Rank = 26.02) outperform non-experts (mean Rank = 34.98) in drawing sketch maps (large-scale). This difference is significant (p =.043) with large effect size (r = 0.30).</p> <p>Graph: Figure 13. The Mann–Whitney test shows that experts (mean Rank = 25.02) outperform non-experts (mean Rank = 35.98) in Scene Sequencing (large-scale). This difference is significant (p =.015) with large effect size (r = 0.37).</p> <p>For the remaining variables, there was no statistically significant difference between the groups, implying that both groups demonstrated similar performance.</p> <hd id="AN0180474575-11">Relations among variables</hd> <p>Judging from the measures of dependence (Spearman's Rho correlation) between the variables for the two groups (Figure 14), the majority of small-scale abilities are correlated to some extent with each other for either group. On the other hand, large-scale abilities are less correlated with each other. For both groups, an equal number of significant correlations among large-scale abilities are observed. Across scales, fewer significant correlations can be observed for the experts' group than for the non-experts' group. Overall, the number of significant correlations among spatial abilities is larger for the non-experts' group than for the experts' one (Table 3). Small-scale factors exhibit more and stronger significant correlations than large-scale ones.</p> <p>Graph: Figure 14. Correlation matrix among variables; experts (left) and non-experts (right). Circles' size and color value denote different significant correlation coefficients; labels show correlation strengths. Empty squares indicate non-significant correlations. Spatial abilities are labeled using (s) or (L) next to their abbreviation to differentiate between small- and large-scale abilities respectively.</p> <p>Table 3. Number of significant correlations among spatial abilities within and across scales. Brackets: number of correlations for the most and the least significantly correlated abilities. Bold fonts: most or least correlated abilities common to both groups). Refer to Table 1 for spatial ability denominations.</p> <p> <ephtml> <table><thead><tr><td>Spatial Ability</td><td>Experts</td><td>Non-Experts</td></tr><tr><td>Correlations</td><td>Most Correlated</td><td>Least or Not at all Correlated</td><td>Correlations</td><td>Most Correlated</td><td>Least or Not at all Correlated</td></tr></thead><tbody><tr><td>Small</td><td>94</td><td>MF (10) MA, <bold>SC</bold>, SV (9)</td><td>SP (1)</td><td>104</td><td>FC (12) PS, <bold>SC</bold> (11) VPA (10)</td><td>PT, VM (1)</td></tr><tr><td>Large</td><td>24</td><td>PLOC (5) <bold>SMAP</bold>, PI (4)</td><td><bold>EDIST</bold> (0)</td><td>24</td><td>GDIR (5), <bold>SMAP</bold>, SS (4)</td><td>IDIR, <bold>EDIST</bold>(n), EDIR, PI (1)</td></tr><tr><td>Small-Large</td><td>42</td><td><bold>IDIR</bold> (6) PLOC (5) SMAP, PT (4)</td><td>FC, MR, VM, SO, VPA, SP, SR, SS, GDIR, <bold>EDIST</bold> (0)</td><td>68</td><td>EDIR (9), GDIR (7) <bold>IDIR</bold>, VPA, SP (6)</td><td>CS, SV, VWM, LR, <bold>EDIST</bold> (0)</td></tr></tbody></table> </ephtml> </p> <p>SOD shows significant correlations to one small- and four large-scale factors for each group; however, these factors are different for both groups with the exception of SMAP and PLOC (Figure 14).</p> <p>Table 3 and Figure 14 indicate that spatial abilities correlate differently for experts and non-experts. Only SC correlates with a large number of small-scale factors for both groups. Similarly, at large scale, only SMAP correlates with four other large-scale factors for both groups. The correlations between small- and large-scale abilities are mostly non-significant, indicating that small- and large-scale abilities are somehow dissociated. In fact, only one large-scale factor, IDIR, correlates with six small-scale ones for both groups.</p> <hd id="AN0180474575-12">Discussion</hd> <p>The analysis demonstrates better performance of experts in eight small- and two large-scale abilities. In all (but PS and VM), experts scored higher but not all differences are statistically significant. Hence, this study's results indicate difference in performance between experts and non-experts which is mostly highlighted for small-scale abilities where experts outperform non-experts. At large scale, some significant differences are identified, which also favor experts.</p> <p>All significant differences in spatial factors between groups exhibit medium, large, and very large effect sizes; hence, traditional statistics would lead us to conclude that these differences are important. However, recent research in statistical analysis states something quite different. According to Funder and Ozer ([<reflink idref="bib12" id="ref39">12</reflink>]), very large effect sizes (>.40) will rarely be replicated or found in large samples, and are likely to overestimate differences. On the contrary, large effect sizes (0.30–0.39) are potentially powerful in both the short and long runs, meaning they are more likely to be replicated and found in larger samples. Finally, medium effect sizes (0.20–0.29) have explanatory and practical uses in the short run.</p> <p>Hence, differences in MA, PT, SP, SMAP, and SS that present the large effect sizes are more important and more useful than the rest of significant differences. This is because our sample is small, while equally populated, to allow drawing extensive conclusions on how STEM-expertise affects spatial abilities. To fully explore and understand differences, if any, more studies should be followed by a larger number of participants. Other differentiating factors along with expertise can be investigated, such as gender and years of expertise/professional experience, making the necessity of a larger sample even more evident and compelled. Moreover, the study focused on STEM expertise but did not take into account other participants' hobbies or activities that might harness spatial abilities, such as orienteering, speleology, or sports.</p> <p>This research focused on the investigation of small- and large-scale spatial abilities; however, it did not delve into the nuances that may be involved in specific STEM disciplines. Researchers now recognize that spatial problems vary greatly among STEM disciplines; thus, they have proposed a context and domain-specific approach for evaluating spatial abilities and their contribution in the development of STEM expertise (Atit et al., [<reflink idref="bib4" id="ref40">4</reflink>]).</p> <hd id="AN0180474575-13">Acknowledgements</hd> <p>The authors would like to thank all individuals who participated in the study and Antonia Stavropoulou for local support. This work was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the "First Call for H.F.R.I. Research Projects to support Faculty members and Researchers and the procurement of high-cost research equipment" under Grant [Project Number: HFRI-FM17-2661]; and the Erasmus+ Programme under Grant [Project Number: 2020-1-SE01-KA201-077972].</p> <hd id="AN0180474575-14">Disclosure statement</hd> <p>No potential conflict of interest was reported by the author(s).</p> <hd id="AN0180474575-15">Data availability statement</hd> <p>The survey data that support the findings of this study are openly available in figshare at https://doi.org/10.6084/m9.figshare.20401047.v4.</p> <hd id="AN0180474575-16">Supplementary material</hd> <p>Supplemental data for this article can be accessed online at https://doi.org/10.1080/03098265.2023.2263735</p> <ref id="AN0180474575-17"> <title> References </title> <blist> <bibl id="bib1" idref="ref34" type="bt">1</bibl> <bibtext> Allen, G. (2000). Principles and practices for communicating route knowledge. 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Journal of Vision, 10 (11), 25. https://doi.org/10.1167/10.11.25</bibtext> </blist> </ref> <aug> <p>By Eleni Tomai; Margarita Kokla; Christos Charcharos and Marinos Kavouras</p> <p>Reported by Author; Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib55" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib51" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib54" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib42" firstref="ref4"></nolink> <nolink nlid="nl5" bibid="bib43" firstref="ref5"></nolink> <nolink nlid="nl6" bibid="bib16" firstref="ref7"></nolink> <nolink nlid="nl7" bibid="bib30" firstref="ref9"></nolink> <nolink nlid="nl8" bibid="bib21" firstref="ref10"></nolink> <nolink nlid="nl9" bibid="bib19" firstref="ref11"></nolink> <nolink nlid="nl10" bibid="bib23" firstref="ref12"></nolink> <nolink nlid="nl11" bibid="bib22" firstref="ref13"></nolink> <nolink nlid="nl12" bibid="bib13" firstref="ref16"></nolink> <nolink nlid="nl13" bibid="bib14" firstref="ref17"></nolink> <nolink nlid="nl14" bibid="bib50" firstref="ref18"></nolink> <nolink nlid="nl15" bibid="bib18" firstref="ref19"></nolink> <nolink nlid="nl16" bibid="bib26" firstref="ref20"></nolink> <nolink nlid="nl17" bibid="bib36" firstref="ref21"></nolink> <nolink nlid="nl18" bibid="bib31" firstref="ref22"></nolink> <nolink nlid="nl19" bibid="bib39" firstref="ref23"></nolink> <nolink nlid="nl20" bibid="bib17" firstref="ref27"></nolink> <nolink nlid="nl21" bibid="bib20" firstref="ref29"></nolink> <nolink nlid="nl22" bibid="bib44" firstref="ref30"></nolink> <nolink nlid="nl23" bibid="bib37" firstref="ref31"></nolink> <nolink nlid="nl24" bibid="bib47" firstref="ref32"></nolink> <nolink nlid="nl25" bibid="bib41" firstref="ref33"></nolink> <nolink nlid="nl26" bibid="bib27" firstref="ref36"></nolink> <nolink nlid="nl27" bibid="bib10" firstref="ref37"></nolink> <nolink nlid="nl28" bibid="bib12" firstref="ref38"></nolink>
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Exploring the Relation between Spatial Abilities and STEM Expertise
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Eleni+Tomai%22">Eleni Tomai</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-1162-7389">0000-0003-1162-7389</externalLink>)<br /><searchLink fieldCode="AR" term="%22Margarita+Kokla%22">Margarita Kokla</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-0369-7099">0000-0002-0369-7099</externalLink>)<br /><searchLink fieldCode="AR" term="%22Christos+Charcharos%22">Christos Charcharos</searchLink><br /><searchLink fieldCode="AR" term="%22Marinos+Kavouras%22">Marinos Kavouras</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-7563-0793">0000-0001-7563-0793</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Journal+of+Geography+in+Higher+Education%22"><i>Journal of Geography in Higher Education</i></searchLink>. 2024 48(5):734-751.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 18
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2024
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Spatial+Ability%22">Spatial Ability</searchLink><br /><searchLink fieldCode="DE" term="%22STEM+Education%22">STEM Education</searchLink><br /><searchLink fieldCode="DE" term="%22Individual+Differences%22">Individual Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Expertise%22">Expertise</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Adults%22">Adults</searchLink><br /><searchLink fieldCode="DE" term="%22Males%22">Males</searchLink><br /><searchLink fieldCode="DE" term="%22Secondary+Education%22">Secondary Education</searchLink><br /><searchLink fieldCode="DE" term="%22Intellectual+Disciplines%22">Intellectual Disciplines</searchLink><br /><searchLink fieldCode="DE" term="%22Attitudes%22">Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Lay+People%22">Lay People</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Nonparametric+Statistics%22">Nonparametric Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+Analysis%22">Statistical Analysis</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Greece%22">Greece</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1080/03098265.2023.2263735
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0309-8265<br />1466-1845
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Small-scale spatial abilities that involve the mental representation and transformation of two- and three-dimensional images and manipulation of objects at table-top have been studied extensively and are considered predictive of both interest and success in STEM disciplines. However, research investigating the relation of large-scale spatial abilities to STEM disciplines is sparse. The paper describes the design and implementation of a study for assessing individual differences (if any) in spatial abilities in both figural and environmental spaces between STEM experts (with over 10 years of experience) and non-experts (individuals without any studies in STEM fields). Participants' performance in 16 small-, 10 large-scale tasks, and one self-assessment questionnaire at environmental scale was evaluated to assess their corresponding abilities. Results indicate differences between experts and non-experts, which are mostly highlighted for small-scale abilities where experts outperform non-experts. At large scale, some significant differences are identified, which also favor experts. Correlations among the variables tested provide evidence that different abilities are prominent between experts and non-experts.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2024
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1445861
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1445861
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/03098265.2023.2263735
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 18
        StartPage: 734
    Subjects:
      – SubjectFull: Spatial Ability
        Type: general
      – SubjectFull: STEM Education
        Type: general
      – SubjectFull: Individual Differences
        Type: general
      – SubjectFull: Expertise
        Type: general
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Adults
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      – SubjectFull: Males
        Type: general
      – SubjectFull: Secondary Education
        Type: general
      – SubjectFull: Intellectual Disciplines
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      – SubjectFull: Attitudes
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      – SubjectFull: Lay People
        Type: general
      – SubjectFull: Student Attitudes
        Type: general
      – SubjectFull: Nonparametric Statistics
        Type: general
      – SubjectFull: Statistical Analysis
        Type: general
      – SubjectFull: Greece
        Type: general
    Titles:
      – TitleFull: Exploring the Relation between Spatial Abilities and STEM Expertise
        Type: main
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      – PersonEntity:
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            NameFull: Eleni Tomai
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            NameFull: Margarita Kokla
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            NameFull: Christos Charcharos
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            NameFull: Marinos Kavouras
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            – D: 01
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              Type: published
              Y: 2024
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            – Type: issn-print
              Value: 0309-8265
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              Value: 1466-1845
          Numbering:
            – Type: volume
              Value: 48
            – Type: issue
              Value: 5
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            – TitleFull: Journal of Geography in Higher Education
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