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High-Achieving Rural Youth and Math and Science Talent Development: An Application of SCCT

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Title: High-Achieving Rural Youth and Math and Science Talent Development: An Application of SCCT
Language: English
Authors: Taylor A. Sommers (ORCID 0009-0005-3772-6221), Saba Rasheed Ali, Duhita Mahatmya, Megan Foley-Nicpon, Susan G. Assouline
Source: Journal of Career Development. 2025 52(5):648-673.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 26
Publication Date: 2025
Sponsoring Agency: Office of Elementary and Secondary Education (OESE) (ED), Jacob K. Javits Gifted and Talented Students Education Program
Contract Number: S206A170017
Document Type: Journal Articles
Reports - Research
Education Level: Elementary Education
Grade 6
Intermediate Grades
Middle Schools
Grade 7
Junior High Schools
Secondary Education
Descriptors: High Achievement, Rural Youth, Talent Development, Academic Aspiration, Grade 6, Grade 7, Self Efficacy, Expectation, Student Interests, Community Influence, Gender Differences, Regional Characteristics, Differences, Mathematics, Sciences, Middle School Students
DOI: 10.1177/08948453251375947
ISSN: 0894-8453
1556-0856
Abstract: This study applied Social Cognitive Career Theory (SCCT) to examine factors influencing the academic aspirations of high-achieving sixth and seventh graders (n = 178) in a Talent Identification and Career Exploration (TICE) program, emphasizing geographical and gender differences. Participants were from rural, micropolitan, and college towns in the Midwest. Hierarchical linear regression explored the role of math and science aptitude, self-efficacy, outcome expectations, interests, and community influence on academic aspirations. Math self-efficacy was significantly associated with four-year college aspirations, while science outcome expectations and community influence were associated with advanced degree aspirations. Notable gender and geographic differences emerged; girls reported higher aspirations for advanced degrees despite lower aptitude in math and science than boys, whereas students from college towns reported higher advanced degree aspirations than those from micropolitan and rural areas. Implications for research, intervention, and policy are discussed, including promoting math and science as a pathway for rural revitalization.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1483299
Database: ERIC
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  Value: <anid>AN0189408572;[2yf6]01oct.25;2025Nov21.01:39;v2.2.500</anid> <title id="AN0189408572-1">High-Achieving Rural Youth and Math and Science Talent Development: An Application of SCCT </title> <p>This study applied Social Cognitive Career Theory (SCCT) to examine factors influencing the academic aspirations of high-achieving sixth and seventh graders (n = 178) in a Talent Identification and Career Exploration (TICE) program, emphasizing geographical and gender differences. Participants were from rural, micropolitan, and college towns in the Midwest. Hierarchical linear regression explored the role of math and science aptitude, self-efficacy, outcome expectations, interests, and community influence on academic aspirations. Math self-efficacy was significantly associated with four-year college aspirations, while science outcome expectations and community influence were associated with advanced degree aspirations. Notable gender and geographic differences emerged; girls reported higher aspirations for advanced degrees despite lower aptitude in math and science than boys, whereas students from college towns reported higher advanced degree aspirations than those from micropolitan and rural areas. Implications for research, intervention, and policy are discussed, including promoting math and science as a pathway for rural revitalization.</p> <p>Keywords: social learning and cognitive theory; career aspirations/goals/choices; children/youth; academic achievement</p> <hd id="AN0189408572-2">Introduction</hd> <p>Rural America has long been a cornerstone of the U.S. economy and identity, fueling agriculture, manufacturing, and resource-based industries ([<reflink idref="bib28" id="ref1">28</reflink>]; [<reflink idref="bib31" id="ref2">31</reflink>]; [<reflink idref="bib32" id="ref3">32</reflink>]; [<reflink idref="bib70" id="ref4">70</reflink>]). For much of the 20th century, these regions sustained vibrant local economies and supported strong educational institutions. However, shifting economic structures, largely driven by globalization, the decline of traditional industries, and uneven policy attention, have eroded local opportunities, leading to systemic challenges in rural areas that have impacted educational access and aspirations of rural youth ([<reflink idref="bib12" id="ref5">12</reflink>]; [<reflink idref="bib20" id="ref6">20</reflink>]; [<reflink idref="bib23" id="ref7">23</reflink>]; [<reflink idref="bib70" id="ref8">70</reflink>]; [<reflink idref="bib87" id="ref9">87</reflink>]), particularly in the domains of math and science.</p> <p>As a result, rural students are less likely to attend four-year colleges and major in math and science than their urban and suburban peers ([<reflink idref="bib30" id="ref10">30</reflink>]; [<reflink idref="bib34" id="ref11">34</reflink>]; [<reflink idref="bib67" id="ref12">67</reflink>]). This educational shortfall places rural students at a greater risk for underachievement ([<reflink idref="bib10" id="ref13">10</reflink>]), decreased financial security ([<reflink idref="bib32" id="ref14">32</reflink>]), and limited job mobility ([<reflink idref="bib83" id="ref15">83</reflink>]). These challenges disproportionately affect high-achieving rural students who, despite demonstrating academic performance at least one standard deviation above their peers on grade-level standardized achievement tests, remain underserved by gifted and talented identification processes and advanced educational programming ([<reflink idref="bib12" id="ref16">12</reflink>]). In rural areas, these students are significantly less likely to be identified for gifted programs or enrolled in advanced coursework, which in turn limits their academic aspirations and narrows their future career opportunities ([<reflink idref="bib10" id="ref17">10</reflink>]). Rather than a reflection of deficits in rural areas, disparities in access to math and science preparation are rooted in broader structural inequities ([<reflink idref="bib69" id="ref18">69</reflink>]; [<reflink idref="bib70" id="ref19">70</reflink>]), highlighting the importance of how economic and human capital are distributed across geography. This fuels a persistent <emph>rural brain drain</emph>, where talented rural youth leave their hometowns in search of better economic opportunities ([<reflink idref="bib25" id="ref20">25</reflink>]; [<reflink idref="bib67" id="ref21">67</reflink>]), even though many hold a strong desire to remain close to home ([<reflink idref="bib18" id="ref22">18</reflink>]).</p> <p>Technological advances in math and science (e.g., artificial intelligence, precision agriculture, and healthcare) offer exciting opportunities for revitalizing rural economies ([<reflink idref="bib28" id="ref23">28</reflink>]; [<reflink idref="bib47" id="ref24">47</reflink>]). Early interventions tailored toward math and science can spark interest and build skills that connect young people to these emerging industries. It's not just about closing a gap; it's about building a pipeline. The Javits Grant supports this mission by identifying and serving gifted and talented students from underserved populations to ensure high-potential learners aren't overlooked due to identity, geographic region, or socioeconomic barriers. In particular, the Talent Identification and Career Exploration (TICE) program focuses on high-achieving sixth and seventh grade students from rural, suburban, and urban areas in the Midwest.</p> <p>Without a workforce equipped with the necessary math and science skills to keep up with advancing technology, rural communities risk exclusion from critical decision-making that shape policy, technology, healthcare, and education ([<reflink idref="bib2" id="ref25">2</reflink>]; [<reflink idref="bib26" id="ref26">26</reflink>], [<reflink idref="bib61" id="ref27">61</reflink>]; [<reflink idref="bib49" id="ref28">49</reflink>]). Therefore, it is essential to understand factors that influence academic aspirations in math and science for rural, high-achieving youth.</p> <p>Social Cognitive Career Theory (SCCT; [<reflink idref="bib51" id="ref29">51</reflink>]) has been widely used to understand the development of adolescents' academic and career aspirations in the context of math and science ([<reflink idref="bib5" id="ref30">5</reflink>]; [<reflink idref="bib7" id="ref31">7</reflink>]; [<reflink idref="bib35" id="ref32">35</reflink>]; [<reflink idref="bib55" id="ref33">55</reflink>]) and for individuals from lower income or underrepresented groups ([<reflink idref="bib37" id="ref34">37</reflink>]; [<reflink idref="bib79" id="ref35">79</reflink>]). The purpose of this study is to apply the SCCT framework to understand factors associated with the academic aspirations of high-achieving youth, with a focus on differences between geographic region, gender, and community influence.</p> <hd id="AN0189408572-3">Social Cognitive Career Theory</hd> <p>SCCT has roots in Bandura's social-cognitive theory ([<reflink idref="bib15" id="ref36">15</reflink>]) and [<reflink idref="bib43" id="ref37">43</reflink>] career self-efficacy theory. The theory posits that career development is influenced by an interplay of psychosocial variables such as self-efficacy beliefs, outcome expectations, and interests. These constructs are shaped by various person inputs and contextual factors, which directly or indirectly impact an individual's academic aspirations, behaviors, and career trajectories ([<reflink idref="bib53" id="ref38">53</reflink>]). The major components of the framework (building self-efficacy, developing outcome expectations, fostering career-related interests, and building career-related aspirations) are thought to begin during a child's elementary and middle school years, and can be cultivated throughout one's life ([<reflink idref="bib51" id="ref39">51</reflink>]). This study is interested in examining the role of person inputs, background and contextual affordances, and psychosocial variables on academic aspirations of sixth and seventh grade high-achieving students.</p> <hd id="AN0189408572-4">Math and Science Self-Efficacy, Outcome Expectations, and Interests</hd> <p>In their 25-year review of SCCT theory, [<reflink idref="bib50" id="ref40">50</reflink>] reviewed the latest status of the complex associations between self-efficacy, outcome expectations, and interests. They called for further examination of how these three constructs interrelate to choice goals (e.g., one's desire to pursue a particular occupational path). This study conceptualizes choice goals as students' aspirations to attend a four-year college or to obtain an advanced degree (PhD, MD, JD).</p> <p>Adolescents with high self-efficacy in math and science are more likely to develop strong academic interests in these domains, which in turn, predict their engagement in related coursework and extracurricular activities ([<reflink idref="bib64" id="ref41">64</reflink>]). This relationship is particularly pronounced in domain-specific relationships, where STEM-related self-efficacy has been shown to mediate the effects of gender and socioeconomic status on academic choices in a STEM field ([<reflink idref="bib79" id="ref42">79</reflink>]). Further, [<reflink idref="bib55" id="ref43">55</reflink>] found correlations between self-efficacy and outcome expectations (.49) and interests (.60), and that self-efficacy and outcome expectations explained 46% of the variance in STEM interests. A path analysis of 288 11th and 12th grade students found that science self-efficacy directly predicted student's anticipated likelihood of choosing STEM majors or graduating in a STEM field ([<reflink idref="bib48" id="ref44">48</reflink>]), and science and math self-efficacy was a predictor of achievement ([<reflink idref="bib22" id="ref45">22</reflink>]; [<reflink idref="bib27" id="ref46">27</reflink>]; [<reflink idref="bib38" id="ref47">38</reflink>]; [<reflink idref="bib72" id="ref48">72</reflink>]) and directly or indirectly influenced outcome expectations ([<reflink idref="bib38" id="ref49">38</reflink>]). Finally, higher career search self-efficacy scores were associated with increased odds of persisting in an engineering major ([<reflink idref="bib24" id="ref50">24</reflink>]).</p> <p>Rural adolescents who demonstrated higher levels of sociopolitical development reported stronger healthcare career interests, greater self-efficacy, and more positive outcome expectations, suggesting that fostering sociopolitical awareness through interventions aimed at exposing adolescents to math and science earlier may enhance STEM-related interests among underserved youth ([<reflink idref="bib2" id="ref51">2</reflink>]). This is important given that research indicates higher outcome expectations can mitigate the negative impact of low self-efficacy on career aspirations, particularly among underrepresented groups ([<reflink idref="bib7" id="ref52">7</reflink>]). [<reflink idref="bib59" id="ref53">59</reflink>] found that having an interest in math and science was a predictor of enrolling in math and science courses during high school, which subsequently led to persistence in earning STEM degrees. The connection between self-efficacy, career interests, and academic aspirations may look different based on vocational interests ([<reflink idref="bib65" id="ref54">65</reflink>]), which is why this study examines domain-specific profiles.</p> <hd id="AN0189408572-5">Geographic Region, Gender, and Community Influence</hd> <p></p> <hd id="AN0189408572-6">Geographic Differences</hd> <p>While much of the existing research has focused on the influence of socioeconomic status (SES) on SCCT factors ([<reflink idref="bib6" id="ref55">6</reflink>]; [<reflink idref="bib17" id="ref56">17</reflink>]; [<reflink idref="bib77" id="ref57">77</reflink>]; [<reflink idref="bib79" id="ref58">79</reflink>]), there is a growing recognition of the need to consider geographic differences in students' academic aspirations. Specifically, the environments of college towns, micropolitan areas, and rural areas offer distinct opportunities to explore challenges that may impact adolescents' confidence in their ability to succeed in math and science-related fields ([<reflink idref="bib23" id="ref59">23</reflink>]; [<reflink idref="bib29" id="ref60">29</reflink>]). We begin with the premise that each region is unique, whether urban or rural, and stress the importance of not assuming homogeneity across geographic and regional areas ([<reflink idref="bib70" id="ref61">70</reflink>]). For example, there are known cultural, societal, and economic differences between rural areas in the Midwest compared to rural areas in Appalachia ([<reflink idref="bib4" id="ref62">4</reflink>]; [<reflink idref="bib40" id="ref63">40</reflink>]) and forested regions (Bernson et al., 2022). Further, [<reflink idref="bib87" id="ref64">87</reflink>] highlight how variability within each geographic region exists and can impact observed differences in academic aspirations as well as actual college enrollment rates. That is, college enrollment gaps were more pronounced for rural students from low and middle SES compared to high-SES, reinforcing the notion that rural students, regardless of geographic region, are not a monolithic group.</p> <hd id="AN0189408572-7">College Towns</hd> <p>College towns can be defined as a place where the presence of a college or university and its associated culture dominates the character of the community and serves as an economic and social hub ([<reflink idref="bib42" id="ref65">42</reflink>]). College towns are typically highly educated ([<reflink idref="bib1" id="ref66">1</reflink>]), which often translates to higher family income and economic stability ([<reflink idref="bib73" id="ref67">73</reflink>]). They contribute to a strong academic culture that fosters high academic aspirations ([<reflink idref="bib14" id="ref68">14</reflink>]; [<reflink idref="bib78" id="ref69">78</reflink>]). Moreover, they provide exposure to academic content in the local communities, such as performing arts centers and access to mentors, which present opportunities for vicarious learning that may positively influence academic aspirations ([<reflink idref="bib15" id="ref70">15</reflink>]; [<reflink idref="bib66" id="ref71">66</reflink>]; [<reflink idref="bib76" id="ref72">76</reflink>]).</p> <hd id="AN0189408572-8">Rural</hd> <p>Rural adolescents, more than urban adolescents, often have strong connections to their immediate and extended families ([<reflink idref="bib31" id="ref73">31</reflink>]). Rural parents often report high educational expectations for their children and expect them to complete a bachelor's degree ([<reflink idref="bib28" id="ref74">28</reflink>]). Many rural youth harbor ambitious aspirations for higher education, but they may lack practical knowledge about how to achieve their goals and are reluctant to leave their communities ([<reflink idref="bib28" id="ref75">28</reflink>]), possessing strong feelings of attachment and social responsibility to their families and communities ([<reflink idref="bib31" id="ref76">31</reflink>]). Despite noted strengths, economic challenges tend to be more pronounced in rural areas, with higher poverty rates and limited job opportunities, which can further restrict students' ability to pursue higher education in terms of both affordability and access to financial aid ([<reflink idref="bib23" id="ref77">23</reflink>]; [<reflink idref="bib67" id="ref78">67</reflink>]). Additionally, lower levels of parental education and peer support in rural communities can negatively impact students' academic aspirations ([<reflink idref="bib7" id="ref79">7</reflink>]; [<reflink idref="bib20" id="ref80">20</reflink>]). Academically, students in rural areas face significant barriers, including limited access to advanced coursework and fewer opportunities to engage with math and science professionals ([<reflink idref="bib49" id="ref81">49</reflink>]; [<reflink idref="bib79" id="ref82">79</reflink>]).</p> <hd id="AN0189408572-9">Micropolitan</hd> <p>Micropolitan areas blend characteristics of both rural and urban settings ([<reflink idref="bib36" id="ref83">36</reflink>]). They typically have more educational resources than rural areas but fewer compared to college towns ([<reflink idref="bib29" id="ref84">29</reflink>]). Educational institutions in micropolitan areas often include community colleges or smaller universities, which may have less national recognition and therefore opportunities for advancement post-graduation compared to universities in college towns. Students in these areas generally have access to advanced coursework, extracurricular activities, and college preparation programs, but these opportunities may be more limited in scope and availability compared to college towns ([<reflink idref="bib36" id="ref85">36</reflink>]). Better understanding how geographic differences influence the development of academic aspirations is crucial for designing interventions that can effectively support adolescents across diverse geographic contexts ([<reflink idref="bib88" id="ref86">88</reflink>]).</p> <hd id="AN0189408572-10">Gender</hd> <p>Many efforts were made in the early 2000s to address disparities between men and women in math and science fields. Over the past several decades, women have outpaced men in college enrollment, comprising approximately 59% of U.S. undergraduates as of 2020 (NCES, 2021). This trend, steady since the 1980s, has been attributed to stronger academic performance among girls in high school, higher educational aspirations, and broader societal changes that have expanded access to higher education for women ([<reflink idref="bib33" id="ref87">33</reflink>]; [<reflink idref="bib46" id="ref88">46</reflink>]). However, gender differences have not extended evenly across disciplines. Women remain underrepresented in certain domains of math and science advanced degrees, where women were awarded only 29% of doctorates in mathematics and statistics, 19% in computer and information sciences, 23% in engineering, and 34% in the physical and technological sciences ([<reflink idref="bib62" id="ref89">62</reflink>]). In contrast, women have achieved greater presence in fields such as biological and biomedical sciences and medicine, earning 54% of doctorates in the former and 48% of medical degrees (M.D.) since 2012 ([<reflink idref="bib63" id="ref90">63</reflink>]). These data underscore the notion that math-intensive disciplines remain among the few areas where gender equity has not yet been achieved ([<reflink idref="bib86" id="ref91">86</reflink>]).</p> <p>One explanation for the lag in male academic participation more broadly is the cultural framing of masculinity as incompatible with higher education ([<reflink idref="bib16" id="ref92">16</reflink>]). As certain domains become increasingly feminized, boys may feel a diminished sense of belonging, contributing to lower academic aspirations ([<reflink idref="bib33" id="ref93">33</reflink>]; [<reflink idref="bib56" id="ref94">56</reflink>]). However, others argue that the gender gap in four-year college enrollment rate for boys is merely a reflection of the gaps that have been accumulating all the way through the K-12 system, as opposed to causing them ([<reflink idref="bib71" id="ref95">71</reflink>]). Despite these broader enrollment patterns, boys continue to report higher math and science self-efficacy in adolescence, which is closely tied to stronger STEM career aspirations ([<reflink idref="bib37" id="ref96">37</reflink>]). [<reflink idref="bib45" id="ref97">45</reflink>] found that for middle school boys, math self-efficacy mediated the relationship between growth mindset and STEM career interest. For girls, however, math anxiety emerged as a stronger predictor, negatively affecting their interest in math and science careers regardless of their self-efficacy or growth mindset. Addressing both geographic and gender-specific factors is essential for fostering equitable math and science career aspirations among high-achieving adolescents.</p> <hd id="AN0189408572-11">Community Influence</hd> <p>Community influence, as conceptualized by SCCT, refers to the broader social and cultural environment that shapes the development of personal and academic self-efficacy, outcome expectations, and, ultimately, career-related goals ([<reflink idref="bib51" id="ref98">51</reflink>]). Adolescents who perceive strong community support for education are likely to develop higher self-efficacy beliefs and more positive outcome expectations, which encourage them to pursue challenging academic fields and higher education ([<reflink idref="bib52" id="ref99">52</reflink>]). Research suggests that community influences may have a larger effect on adolescents' self-efficacy beliefs and outcome expectations than other sources of social support ([<reflink idref="bib3" id="ref100">3</reflink>]). The external environment also contributes to the development of career-related self-efficacy and outcome expectations by providing social capital and support, which are essential for the realization of college aspirations ([<reflink idref="bib58" id="ref101">58</reflink>]). It is hypothesized that community influence, particularly in the form of social capital, may be more potent in college towns due to the presence of university faculty, staff, and students who can serve as mentors, thereby creating an enriched environment that fosters academic ambitions and strengthens students' belief in their ability to achieve success ([<reflink idref="bib66" id="ref102">66</reflink>]; [<reflink idref="bib76" id="ref103">76</reflink>]). To date, no existing studies have examined the effects of community influence in the form of the academic intentions of current high school seniors in students' respective communities across college towns, micropolitan, and rural areas.</p> <hd id="AN0189408572-12">The Current Study</hd> <p>The purpose of the current study is to understand factors associated with the academic aspirations of high-achieving youth from rural and non-rural areas who participated in a Talent Identification and Career Exploration (TICE) program (<emph>n</emph> = 178). Using SCCT as a foundation, research questions one and two examined the relationship between (<reflink idref="bib1" id="ref104">1</reflink>) math and science aptitude, (<reflink idref="bib2" id="ref105">2</reflink>) community influence, (<reflink idref="bib3" id="ref106">3</reflink>) math and science self-efficacy, (<reflink idref="bib4" id="ref107">4</reflink>) math and science outcome expectations, and (<reflink idref="bib5" id="ref108">5</reflink>) math and science career interest <emph>and</emph> academic aspirations (i.e., the expressed likelihood to attend a four-year college and to obtain an advanced degree [PhD, MD, JD]). Research questions three and four explored differences in SCCT factors based on geographic region (college town vs. micropolitan vs. rural) and gender (boys vs. girls), respectively. The following hypotheses were used to base our analyses, as depicted in Figure 1.</p> <p>Above and beyond person inputs and community influence variables, math and science self-efficacy and outcome expectations will be positively associated with aspirations to attend a four-year college.</p> <p>Above and beyond person inputs and community influence variables, math and science self-efficacy and outcome expectations will be positively associated with aspirations to obtain an advanced degree (i.e., PhD, MD, and JD).</p> <p>Students in college towns will have higher math and science self-efficacy, higher outcome expectations, higher interests in math and science, and higher academic aspirations compared to students from micropolitan and rural areas.</p> <p>Boys will have equal levels of math and science aptitude and community influence, will endorse higher levels of math and science self-efficacy, outcome expectations, and interests, but will endorse lower academic aspirations compared to girls.</p> <hd id="AN0189408572-13">Methods</hd> <p></p> <hd id="AN0189408572-14">Participants</hd> <p></p> <hd id="AN0189408572-15">School Recruitment</hd> <p>Schools were recruited via a statewide listserv of talented and gifted (TAG) coordinators. TAG coordinators at interested schools were asked to provide information regarding student demographics (e.g., racial and ethnic diversity and free and reduced lunch (FRL) eligibility) and the building's TAG identification processes. This information was used to compare the schools in the sample to each other and to schools across the state with regard to geographic region, demographics, and existing TAG processes; no school was excluded a priori due to any school characteristics. Schools that chose to participate received grant funds to facilitate and implement TICE curriculum. A total of 11 unique school districts participated; the average FRL rate for the 11 schools was 45.1% (range 27.1%–60.4%). White students made up 73% of the total sample.</p> <hd id="AN0189408572-16">Student Identification</hd> <p>Students were identified using standard Talent Search Model procedures ([<reflink idref="bib11" id="ref109">11</reflink>])<bold>,</bold> which guided the larger Javits grant from which this study originated ([<reflink idref="bib82" id="ref110">82</reflink>]). At each participating school (<emph>n</emph> = 11), all sixth and seventh grade students who scored in the 85<sups>th</sups> percentile or higher on at least one subtest of a standardized state grade-level achievement test (as reported by parents or teachers) were invited to participate in the program; these included students who were not already part of the school's TAG programs. Students then took the I-Excel achievement test, an online above-level test, to gather additional information about students' high-ability domains. Informed parental consent to collect data was obtained for students invited to participate. There were 1,053 students who were identified for participation in the curriculum, of which 488 consented to their data being collected for research. There was no individual incentive for participation, and participants did not pay fees to engage in the curriculum. Regarding grade, the sample included 77% sixth graders (<emph>n</emph> = 137) and 23% seventh graders (<emph>n</emph> = 41). Regarding year of data collection, the sample included 56% from 2018 (<emph>n</emph> = 99), 23% from 2019 (<emph>n</emph> = 41), and 21% from 2021 (<emph>n</emph> = 38). 2020 data was not included due to the global pandemic.</p> <hd id="AN0189408572-17">Procedures</hd> <p>Students participated in the TICE curriculum during one academic year (2018–2021). The TICE curriculum consisted of four modules completed over nine sessions. The building facilitator determined the frequency of these sessions (i.e., every day over nine school days, 3 days per week over 3 weeks, and 1 day per week over 9 weeks). Although most students completed sessions at school, in person, some students completed the program synchronously online following school closures related to the COVID-19 pandemic. As part of the TICE curriculum, students completed a pre-test, a post-test, and a feedback survey following each module, all of which were completed online. Only pre-test data was used for this study due to high levels of data attrition between pre-test and post-test data. All components of the study received human subjects' approval by the primary institution.</p> <hd id="AN0189408572-18">Measures</hd> <p></p> <hd id="AN0189408572-19">Person Inputs</hd> <p></p> <hd id="AN0189408572-20">Math and Science Aptitude</hd> <p>I-Excel is an online test of eighth-grade content licensed from ACT (2013) and administered as an above-level test to sixth and seventh graders. I-Excel includes four tests: English (40 items), Math (30 items), Reading (30 items), and Science (28 items). Raw scores are converted to a scale score; scale score ranges are 1–25. The use of I-Excel has been established in the literature on above-level standardized tests and the talent search model ([<reflink idref="bib13" id="ref111">13</reflink>]). Predictive validity analyses conducted by ACT demonstrate predictive correlations with standardized assessments such as with the ACT (.56–.83) and with GPA (.40–.45). Additionally, Kuder Richardson (KR-20) revealed internal consistency reliability coefficients for Form A. Grade 8 ACT Explore scale scores are English, 0.84 (SEM = 1.66); math, 0.76 (SEM = 1.71); reading, 0.86 (SEM = 1.44); and science, 0.79 (SEM = 1.53). Our analyses used student's pre-test scale scores for math and science.</p> <hd id="AN0189408572-21">Gender</hd> <p>Gender was treated as a binary variable in this study, with participants self-reporting as either male or female. Data on gender were collected through a demographic questionnaire administered before participation in the TICE program. The sample included 58% male students (<emph>n</emph> = 104) and 39% female students (<emph>n</emph> = 69).</p> <hd id="AN0189408572-22">Background/Contextual Affordances</hd> <p></p> <hd id="AN0189408572-23">Community Influence</hd> <p>We used district-level college intention data found publicly online from the Iowa Department of Education as a proxy to measure community influence. Data were obtained from a state-wide survey of high school seniors measuring their postsecondary academic intentions for the year following high school graduation. The dataset includes the total number of students in the district who reported whether they planned to attend a two-year college, community college, four-year public college, or four-year private college; or whether they planned to enter the military or enter the job market. For this study, we used the four-year public and four-year private college numbers from the districts that participated in TICE. These two options were collapsed and converted to proportions, dividing the raw number by the total number of 12<sups>th</sups> graders enrolled in the district that academic year. The final number represents the proportion of students in each district intending to attend four-year college the year after they graduated. Data from the 2022–2023 academic year were used for this study because this was the last year of project implementation.</p> <hd id="AN0189408572-24">Geographic region</hd> <p>Geographic region was categorized into three distinct groups: college town, micropolitan area, and rural area. Our selections were guided by economic and social characteristics found in the literature and population parameters defined by the [<reflink idref="bib81" id="ref112">81</reflink>] and the [<reflink idref="bib80" id="ref113">80</reflink>]. These selections were then cross-referenced with descriptions provided by the National Center for Education and Statistics ([<reflink idref="bib60" id="ref114">60</reflink>]).</p> <p>College towns were categorized based on the following characteristics: The first included having a college or university dominate the character of the community ([<reflink idref="bib42" id="ref115">42</reflink>]). The second included the student population forming a significant proportion of the town's population. The third included the university or college serving as one of the largest employers in the town ([<reflink idref="bib73" id="ref116">73</reflink>]). These criteria were important because college towns can fall within a range of geographic classifications, from rural to micropolitan to metropolitan areas. The two college towns in our study were categorized by the NCES as city: small and town: fringe.</p> <p>Micropolitan areas often serve as a trade center and regional hub for commerce, health care services, shopping, and as a source of employment for many people who commute from an extensive surrounding area ([<reflink idref="bib29" id="ref117">29</reflink>]; [<reflink idref="bib36" id="ref118">36</reflink>]). In this study, we followed the population parameters of a core city or town with a population of at least 10,000 but fewer than 50,000 residents ([<reflink idref="bib81" id="ref119">81</reflink>]). Of the seven micropolitan areas in our study, six were categorized by the NCES as town: distant and one as city: small.</p> <p>Rural areas can be defined as regions that are geographically isolated and with lower population density compared to non-rural areas ([<reflink idref="bib70" id="ref120">70</reflink>]). They are typically characterized by a population density lower than 1,000 people per square mile and are often geographically expansive ([<reflink idref="bib80" id="ref121">80</reflink>]). Socially and economically, rural areas are incredibly diverse, and different rural populations have distinctive personalities and histories ([<reflink idref="bib28" id="ref122">28</reflink>]). The NCES categorizes rural areas into three distinct categories: fringe, distant, and remote. Of the five areas defined as rural in our study, four were categorized by the NCES as rural: distant, and one as rural: remote.</p> <hd id="AN0189408572-25">SCCT Variables</hd> <p></p> <hd id="AN0189408572-26">Subject Specific Self-Efficacy</hd> <p>The Subject Specific Self-Efficacy Scale ([<reflink idref="bib39" id="ref123">39</reflink>]; [<reflink idref="bib38" id="ref124">38</reflink>]; [<reflink idref="bib74" id="ref125">74</reflink>]) quantifies middle school students' perceptions that they can succeed in school, with subscales in art (3 items), social studies (3 items), science (2 items), math (2 items), and English (3 items). The scale is measured on a 5-point scale ranging from 1 (<emph>Not True at All</emph>) to 5 (<emph>Very True</emph>), with higher scores representing greater belief in their ability to succeed in each subject. Sample items include, "I think I can lead a book discussion in my class or club," and "I think I can decide what is the same or different about two kinds of art." In the original study, the five subscales attained Cronbach's alpha coefficients of 0.86, 0.90, 0.85, 0.85, and 0.88, respectively. In the current study, the combined Cronbach's alpha for math/science was 0.67.</p> <hd id="AN0189408572-27">Subject Specific Outcome Expectations</hd> <p>The Subject Specific Outcome Expectation Scale ([<reflink idref="bib38" id="ref126">38</reflink>]; [<reflink idref="bib74" id="ref127">74</reflink>]) measures middle school students' belief that activities in art (3 items), social studies (3 items), math (2 items), science (2 items; SOE), and English (3 items) class will increase their potential achievement in the future. Sample items include, "If I do well in science classes, then I will be better prepared for my job" and "Taking classes in art will help me decide what job I want to have as an adult." The subscales are measured on a 5-point Likert scale ranging from 1 (<emph>Not True at All</emph>) to 5 (<emph>Very True</emph>) with high scores reflecting greater belief that classes in the subject will increase their potential future achievement. The five subscales achieved a Cronbach's alpha coefficient of.87,.84,.81,.81, and.81, respectively, in the study they were developed ([<reflink idref="bib38" id="ref128">38</reflink>]). In the current study, the combined Cronbach's alpha for math/science was 0.86.</p> <hd id="AN0189408572-28">Academic-Subject Specific Interests</hd> <p>The Subject Specific Interests Scale ([<reflink idref="bib38" id="ref129">38</reflink>]; [<reflink idref="bib74" id="ref130">74</reflink>]) measures how much middle school students enjoy art (3-items; AI), social studies (3 items; SSI), science (2 items; SI), math (2 items; MI), and English (3 items; EI). Sample items include, "I would like being a member of a theater or acting group" and "I would like working in a science lab or room with science materials." The scale is measured on a 5-point Likert scale ranging from 1 (<emph>Not True at All)</emph> to 5 (<emph>Very True)</emph> with high scores reflecting greater enjoyment of the specific subject. In the original study, the five subscales achieved a Cronbach's alpha coefficient of.84,.76,.94,.94, and.87. In the current study, the combined Cronbach's alpha for math/science was 0.69.</p> <hd id="AN0189408572-29">Outcome Variable</hd> <p></p> <hd id="AN0189408572-30">Academic Aspirations</hd> <p>On a scale from "<emph>Not at all likely</emph>" (<reflink idref="bib1" id="ref131">1</reflink>) to "<emph>Extremely likely</emph>" (<reflink idref="bib5" id="ref132">5</reflink>), students answered how likely they were to do the following: Finish high school, attend a one to 2 year technical or vocational school after high school, attend a four-year college, graduate college, attend a higher level of school after graduating college (e.g., master's degree), and graduate with an advanced degree (i.e., PhD, MD, and JD). For this study, academic aspirations were interpreted as student's expressed likelihood to attend a four-year college and likelihood to graduate with an advanced degree. Higher scores on the 1–5 scale indicate greater academic aspirations. It is noted that the term "aspirations" and "desire" will be used interchangeably throughout the remainder of the study.</p> <hd id="AN0189408572-31">Data Analysis</hd> <p>We first addressed missing data using the complete case analysis approach ([<reflink idref="bib68" id="ref133">68</reflink>]). This approach involves including only students who provided complete data for all scale items and the two background variables, including gender and geographic region. In addition, we excluded unengaged respondents who provided identical responses across all scale items. Before conducting the primary analysis, the data were examined for normality. The distribution of the dependent variables was assessed using visual histograms and statistical methods (i.e., Shapiro–Wilk test). The data appeared normally distributed, and the statistical methods did not reveal deviations from normality (p >.05).</p> <p>Our primary analyses involved two hierarchical linear regressions to examine how Social Cognitive Career Theory (SCCT) variables relate to students' academic aspirations. Specifically, we modeled two outcome variables: (<reflink idref="bib1" id="ref134">1</reflink>) students' reported likelihood to attend a four-year college and (<reflink idref="bib2" id="ref135">2</reflink>) their reported likelihood to obtain an advanced degree (e.g., PhD, MD, and JD). Hierarchical multiple regression is appropriate for exploratory, theory-driven research involving multiple predictors ([<reflink idref="bib44" id="ref136">44</reflink>]; [<reflink idref="bib84" id="ref137">84</reflink>]). Each regression followed a three-step model informed by the SCCT framework ([<reflink idref="bib51" id="ref138">51</reflink>]). In Step 1, we entered students' math and science aptitude scores along with the community influence variable. Step 2 added math and science self-efficacy and math and science outcome expectations. Step 3 added math and science interests. Math and science variables were entered separately to address domain-specific abilities, which is particularly relevant for high-ability students. To ensure complete data for our outcome measures, we included only participants who provided responses to the four-year college intention item (<emph>n</emph> = 178) and the advanced degree intention item (<emph>n</emph> = 177), which was in line with best practices to conduct our regression analysis given the number of variables of interest ([<reflink idref="bib41" id="ref139">41</reflink>]). As a secondary analysis, we conducted a two-way multivariate analysis of variance (MANOVA) to examine the influence of geographic region (college town, micropolitan, rural) and gender (boys, girls) on SCCT variables, including likelihood to attend a four-year college and likelihood to obtain an advanced degree.</p> <hd id="AN0189408572-32">Results</hd> <p></p> <hd id="AN0189408572-33">Descriptive Statistics and Correlation Analysis</hd> <p>Table 1 presents the mean, standard deviations, and min and max values for the study variables. Table 2 presents the descriptive statistics for all academic aspirations. Results indicate that academic aspirations were high amongst this group of high-achieving youth, specifically for aspirations to attend and graduate from a four-year college. Pearson correlation coefficients were calculated to assess the relationships between the independent variables are displayed in Table 3. Results suggest that the strongest positive and significant correlations existed between math outcome expectations and science outcome expectations (r = 0.74), followed by math self-efficacy and science self-efficacy (r = 0.54). Correlations with academic aspirations include a desire to attend a four-year college being positive and significantly correlated with a desire to obtain an advanced degree (r = 0.46), which is expected considering a four-year degree is a prerequisite to higher education. Math self-efficacy (r = 0.32) and science outcome expectations (r = 0.30) were positive and significantly correlated with aspirations to attend a four-year college.</p> <p>Table 1. Descriptive Statistics of SCCT Variables</p> <p>Graph</p> <p> <ephtml> <table><thead valign="top"><tr><th align="left">Variable Name</th><th align="left">N</th><th align="left">Mean</th><th align="left">Std</th><th align="left">Min</th><th align="left">Max</th></tr></thead><tbody valign="top"><tr><td align="left">Math aptitude</td><td align="char" char=".">178</td><td align="char" char=".">13.88</td><td align="char" char=".">3.28</td><td align="char" char=".">3.00</td><td align="char" char=".">25.00</td></tr><tr><td align="left">Science aptitude</td><td align="char" char=".">178</td><td align="char" char=".">17.07</td><td align="char" char=".">3.38</td><td align="char" char=".">0.00</td><td align="char" char=".">24.00</td></tr><tr><td align="left">Community influence</td><td align="char" char=".">178</td><td align="char" char=".">0.39</td><td align="char" char=".">0.14</td><td align="char" char=".">0.19</td><td align="char" char=".">0.57</td></tr><tr><td align="left">Math self-efficacy</td><td align="char" char=".">178</td><td align="char" char=".">4.98</td><td align="char" char=".">0.97</td><td align="char" char=".">2.00</td><td align="char" char=".">6.00</td></tr><tr><td align="left">Science self-efficacy</td><td align="char" char=".">178</td><td align="char" char=".">5.14</td><td align="char" char=".">0.95</td><td align="char" char=".">1.00</td><td align="char" char=".">6.00</td></tr><tr><td align="left">Math OE</td><td align="char" char=".">178</td><td align="char" char=".">4.69</td><td align="char" char=".">1.12</td><td align="char" char=".">1.00</td><td align="char" char=".">6.00</td></tr><tr><td align="left">Science OE</td><td align="char" char=".">178</td><td align="char" char=".">4.59</td><td align="char" char=".">1.05</td><td align="char" char=".">1.00</td><td align="char" char=".">6.00</td></tr><tr><td align="left">Math interest</td><td align="char" char=".">178</td><td align="char" char=".">3.41</td><td align="char" char=".">1.36</td><td align="char" char=".">1.00</td><td align="char" char=".">6.00</td></tr><tr><td align="left">Science interest</td><td align="char" char=".">178</td><td align="char" char=".">4.45</td><td align="char" char=".">1.25</td><td align="char" char=".">1.00</td><td align="char" char=".">6.00</td></tr><tr><td align="left">Valid N (listwise)</td><td align="char" char=".">177</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr></tbody></table> </ephtml> </p> <p>Table 2. Descriptives of Academic Aspirations</p> <p>Graph</p> <p> <ephtml> <table><thead valign="top"><tr><th align="left">Endorsed likelihood (on a scale from 1-5)</th><th align="left">N</th><th align="left">Range</th><th align="left">Min</th><th align="left">Max</th><th align="left">Mean</th><th align="left">Std</th></tr></thead><tbody valign="top"><tr><td align="left">Finish high school</td><td align="char" char=".">178.00</td><td align="char" char=".">2.00</td><td align="char" char=".">3.00</td><td align="char" char=".">5.00</td><td align="char" char=".">4.93</td><td align="char" char=".">0.28</td></tr><tr><td align="left">Attend a 1-2 year technical or vocational school after high school</td><td align="char" char=".">175.00</td><td align="char" char=".">4.00</td><td align="char" char=".">1.00</td><td align="char" char=".">5.00</td><td align="char" char=".">2.95</td><td align="char" char=".">1.21</td></tr><tr><td align="left">Attend a four-year college</td><td align="char" char=".">178.00</td><td align="char" char=".">4.00</td><td align="char" char=".">1.00</td><td align="char" char=".">5.00</td><td align="char" char=".">4.49</td><td align="char" char=".">0.87</td></tr><tr><td align="left">Graduate college</td><td align="char" char=".">178.00</td><td align="char" char=".">4.00</td><td align="char" char=".">1.00</td><td align="char" char=".">5.00</td><td align="char" char=".">4.57</td><td align="char" char=".">0.74</td></tr><tr><td align="left">Attend a higher level of school after graduating college (e.g., master's degree)</td><td align="char" char=".">176.00</td><td align="char" char=".">4.00</td><td align="char" char=".">1.00</td><td align="char" char=".">5.00</td><td align="char" char=".">3.42</td><td align="char" char=".">1.10</td></tr><tr><td align="left">Graduate with an advanced degree (e.g., PhD, MD, and JD)</td><td align="char" char=".">177.00</td><td align="char" char=".">4.00</td><td align="char" char=".">1.00</td><td align="char" char=".">5.00</td><td align="char" char=".">3.44</td><td align="char" char=".">1.12</td></tr><tr><td align="left">Valid N (listwise)</td><td align="char" char=".">172.00</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr></tbody></table> </ephtml> </p> <p>Table 3. Correlation Matrix of SCCT Variables</p> <p>Graph</p> <p> <ephtml> <table><thead valign="top"><tr><th align="left">Variable Name</th><th align="left">1</th><th align="left">2</th><th align="left">3</th><th align="left">4</th><th align="left">5</th><th align="left">6</th><th align="left">7</th><th align="left">8</th><th align="left">9</th><th align="left">10</th><th align="left">11</th></tr></thead><tbody valign="top"><tr><td align="left">1. Math aptitude</td><td align="char" char=".">1</td><td align="char" char=".">0.45**</td><td align="char" char=".">−0.04</td><td align="char" char=".">0.24**</td><td align="char" char=".">0.12</td><td align="char" char=".">0.03</td><td align="char" char=".">0.06</td><td align="char" char=".">−0.04</td><td align="char" char=".">0.02</td><td align="char" char=".">0.09</td><td align="char" char=".">−0.01</td></tr><tr><td align="left">2. Science aptitude</td><td align="char" char=".">0.45**</td><td align="char" char=".">1</td><td align="char" char=".">0.16*</td><td align="char" char=".">0.06</td><td align="char" char=".">0.1</td><td align="char" char=".">0.03</td><td align="char" char=".">0.12</td><td align="char" char=".">−0.03</td><td align="char" char=".">−0.03</td><td align="char" char=".">0.11</td><td align="char" char=".">0.11</td></tr><tr><td align="left">3. Community influence</td><td align="char" char=".">−0.04</td><td align="char" char=".">0.16*</td><td align="char" char=".">1</td><td align="char" char=".">0.18*</td><td align="char" char=".">0.17*</td><td align="char" char=".">−0.02</td><td align="char" char=".">0.16*</td><td align="char" char=".">0.12</td><td align="char" char=".">0.15*</td><td align="char" char=".">0.19*</td><td align="char" char=".">0.35**</td></tr><tr><td align="left">4. Math self-efficacy</td><td align="char" char=".">0.24**</td><td align="char" char=".">0.06</td><td align="char" char=".">0.18*</td><td align="char" char=".">1</td><td align="char" char=".">0.54**</td><td align="char" char=".">0.30**</td><td align="char" char=".">0.14</td><td align="char" char=".">0.24**</td><td align="char" char=".">0.28**</td><td align="char" char=".">0.32**</td><td align="char" char=".">0.23**</td></tr><tr><td align="left">5. Science self-efficacy</td><td align="char" char=".">0.12</td><td align="char" char=".">0.1</td><td align="char" char=".">0.17*</td><td align="char" char=".">0.55*</td><td align="char" char=".">1</td><td align="char" char=".">0.12</td><td align="char" char=".">0.24**</td><td align="char" char=".">0.29**</td><td align="char" char=".">0.42**</td><td align="char" char=".">0.19**</td><td align="char" char=".">0.20**</td></tr><tr><td align="left">6. Math interest</td><td align="char" char=".">0.03</td><td align="char" char=".">0.03</td><td align="char" char=".">−0.02</td><td align="char" char=".">0.30**</td><td align="char" char=".">0.12</td><td align="char" char=".">1</td><td align="char" char=".">0.32**</td><td align="char" char=".">0.33**</td><td align="char" char=".">0.29**</td><td align="char" char=".">0.23**</td><td align="char" char=".">0.22**</td></tr><tr><td align="left">7. Science interest</td><td align="char" char=".">0.06</td><td align="char" char=".">0.12</td><td align="char" char=".">0.16*</td><td align="char" char=".">0.14</td><td align="char" char=".">0.24**</td><td align="char" char=".">0.32**</td><td align="char" char=".">1</td><td align="char" char=".">0.31**</td><td align="char" char=".">0.45**</td><td align="char" char=".">0.22**</td><td align="char" char=".">0.19*</td></tr><tr><td align="left">8. Math OE</td><td align="char" char=".">−0.04</td><td align="char" char=".">−0.03</td><td align="char" char=".">0.12</td><td align="char" char=".">0.24**</td><td align="char" char=".">0.29**</td><td align="char" char=".">0.33**</td><td align="char" char=".">0.31**</td><td align="char" char=".">1</td><td align="char" char=".">0.74**</td><td align="char" char=".">0.25**</td><td align="char" char=".">0.24**</td></tr><tr><td align="left">9. Science OE</td><td align="char" char=".">0.02</td><td align="char" char=".">−0.03</td><td align="char" char=".">0.15*</td><td align="char" char=".">0.28**</td><td align="char" char=".">0.42**</td><td align="char" char=".">0.29**</td><td align="char" char=".">0.45**</td><td align="char" char=".">0.74**</td><td align="char" char=".">1</td><td align="char" char=".">0.30**</td><td align="char" char=".">0.32**</td></tr><tr><td align="left">10. 4-year college</td><td align="char" char=".">0.09</td><td align="char" char=".">0.11</td><td align="char" char=".">0.19*</td><td align="char" char=".">0.32**</td><td align="char" char=".">0.19**</td><td align="char" char=".">0.23**</td><td align="char" char=".">0.22**</td><td align="char" char=".">0.25**</td><td align="char" char=".">0.30**</td><td align="char" char=".">1</td><td align="char" char=".">0.46**</td></tr><tr><td align="left">11. Advanced degree</td><td align="char" char=".">−0.01</td><td align="char" char=".">0.11</td><td align="char" char=".">0.35**</td><td align="char" char=".">0.23**</td><td align="char" char=".">0.20**</td><td align="char" char=".">0.22**</td><td align="char" char=".">0.19*</td><td align="char" char=".">0.24**</td><td align="char" char=".">0.32**</td><td align="char" char=".">0.46**</td><td align="char" char=".">1</td></tr></tbody></table> </ephtml> </p> <p>1 **Correlation is significant at or below the 0.01 level.</p> <p>2 *Correlation is significant at or below the 0.05 level.</p> <p>Table 4. Hierarchical Regression Analysis of Aspirations to Attend Four-year College with SCCT Variables (N = 178)</p> <p>Graph</p> <p> <ephtml> <table><thead valign="top"><tr><th align="left">Model</th><th align="left">Variable Name</th><th align="left">B</th><th align="left">SE B</th><th align="left">Beta</th><th align="left">R<sup>2</sup></th><th align="left">R<sup>2</sup><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mo>∆</mo></mrow></math></p></th><th align="left">F <p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mo>∆</mo></mrow></math></p></th><th align="left">Cohen's F</th></tr></thead><tbody valign="top"><tr><td align="left" rowspan="3">1</td><td align="left">Math aptitude</td><td align="char" char=".">0.02</td><td align="char" char=".">0.02</td><td align="char" char=".">0.08</td><td align="char" char=".">0.05</td><td align="char" char=".">0.05</td><td align="char" char=".">2.85*</td><td align="left" /></tr><tr><td align="left">Science aptitude</td><td align="char" char=".">0.01</td><td align="char" char=".">0.02</td><td align="char" char=".">0.04</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Community influence</td><td align="char" char=".">1.13</td><td align="char" char=".">0.46</td><td align="char" char=".">0.19*</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left" rowspan="7">2</td><td align="left">Math aptitude</td><td align="char" char=".">0.00</td><td align="char" char=".">0.02</td><td align="char" char=".">0.00</td><td align="char" char=".">0.17</td><td align="char" char=".">0.13</td><td align="char" char=".">6.51**</td><td align="char" char=".">0.16</td></tr><tr><td align="left">Science aptitude</td><td align="char" char=".">0.02</td><td align="char" char=".">0.02</td><td align="char" char=".">0.09</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Community influence</td><td align="char" char=".">0.61</td><td align="char" char=".">0.45</td><td align="char" char=".">0.10</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Math self-efficacy</td><td align="char" char=".">0.24</td><td align="char" char=".">0.08</td><td align="char" char=".">0.27**</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Science self-efficacy</td><td align="char" char=".">−0.07</td><td align="char" char=".">0.08</td><td align="char" char=".">−0.08</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Math OE</td><td align="char" char=".">0.03</td><td align="char" char=".">0.08</td><td align="char" char=".">0.03</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Science OE</td><td align="char" char=".">0.18</td><td align="char" char=".">0.09</td><td align="char" char=".">0.22</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left" rowspan="9">3</td><td align="left">Math aptitude</td><td align="char" char=".">0.00</td><td align="char" char=".">0.02</td><td align="char" char=".">0.00</td><td align="char" char=".">0.18</td><td align="char" char=".">0.01</td><td align="char" char=".">1.07</td><td align="char" char=".">0.02</td></tr><tr><td align="left">Science aptitude</td><td align="char" char=".">0.02</td><td align="char" char=".">0.02</td><td align="char" char=".">0.08</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Community influence</td><td align="char" char=".">0.64</td><td align="char" char=".">0.46</td><td align="char" char=".">0.11</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Math self-efficacy</td><td align="char" char=".">0.22</td><td align="char" char=".">0.08</td><td align="char" char=".">0.25**</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Science self-efficacy</td><td align="char" char=".">−0.07</td><td align="char" char=".">0.08</td><td align="char" char=".">−0.07</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Math OE</td><td align="char" char=".">0.02</td><td align="char" char=".">0.08</td><td align="char" char=".">0.02</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Science OE</td><td align="char" char=".">0.15</td><td align="char" char=".">0.10</td><td align="char" char=".">0.18</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Math interest</td><td align="char" char=".">0.05</td><td align="char" char=".">0.05</td><td align="char" char=".">0.08</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Science interest</td><td align="char" char=".">0.04</td><td align="char" char=".">0.06</td><td align="char" char=".">0.06</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr></tbody></table> </ephtml> </p> <p>3 *<emph>p</emph> <.05. **<emph>p</emph> <.01.</p> <p>Table 5. Hierarchical Regression Analysis of Aspirations to Obtain an Advanced Degree with SCCT Variables (N = 177)</p> <p>Graph</p> <p> <ephtml> <table><thead valign="top"><tr><th align="left">Model</th><th align="left">Variable name</th><th align="left">B</th><th align="left">SE B</th><th align="left">Beta</th><th align="left">R<sup>2</sup></th><th align="left">R<sup>2</sup><p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mo>∆</mo></mrow></math></p></th><th align="left">F <p><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow xmlns=""><mo>∆</mo></mrow></math></p></th><th align="left">Cohen's F</th></tr></thead><tbody valign="top"><tr><td align="left" rowspan="3">1</td><td align="left">MathAptitude</td><td align="left">−0.01</td><td align="left">0.03</td><td align="left">−0.03</td><td align="left">0.12</td><td align="left">0.12</td><td align="left">8.05**</td><td align="left" /></tr><tr><td align="left">ScienceAptitude</td><td align="left">0.02</td><td align="left">0.03</td><td align="left">0.07</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Community influence</td><td align="left">2.63</td><td align="left">0.58</td><td align="left">0.33**</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left" rowspan="7">2</td><td align="left">MathAptitude</td><td align="left">−0.03</td><td align="left">0.03</td><td align="left">−0.08</td><td align="left">0.21</td><td align="left">0.09</td><td align="left">4.84**</td><td align="left">0.11</td></tr><tr><td align="left">Science aptitude</td><td align="left">0.03</td><td align="left">0.03</td><td align="left">0.10</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Community influence</td><td align="left">2.09</td><td align="left">0.57</td><td align="left">0.27**</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Math self-efficacy</td><td align="left">0.17</td><td align="left">0.10</td><td align="left">0.15</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Science self-efficacy</td><td align="left">−0.04</td><td align="left">0.10</td><td align="left">−0.04</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Math OE</td><td align="left">−0.02</td><td align="left">0.10</td><td align="left">−0.02</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Science OE</td><td align="left">0.29</td><td align="left">0.12</td><td align="left">0.27**</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left" rowspan="8">3</td><td align="left">Math aptitude</td><td align="left">−0.02</td><td align="left">0.03</td><td align="left">−0.07</td><td align="left">0.23</td><td align="left">0.02</td><td align="left">1.86</td><td align="left">0.02</td></tr><tr><td align="left">ScienceAptitude</td><td align="left">0.03</td><td align="left">0.03</td><td align="left">0.09</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Community influence</td><td align="left">2.26</td><td align="left">0.57</td><td align="left">0.29**</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Math self-efficacy</td><td align="left">0.11</td><td align="left">0.10</td><td align="left">0.10</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Science self-efficacy</td><td align="left">−0.01</td><td align="left">0.10</td><td align="left">−0.01</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Math OE</td><td align="left">−0.05</td><td align="left">0.10</td><td align="left">−0.05</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Science OE</td><td align="left">0.29</td><td align="left">0.12</td><td align="left">0.27*</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Math interest</td><td align="left">0.12</td><td align="left">0.06</td><td align="left">0.15</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left" /><td align="left">Science interest</td><td align="left">−0.03</td><td align="left">0.07</td><td align="left">−0.03</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr></tbody></table> </ephtml> </p> <p>4 *<emph>p</emph> <.05. **<emph>p</emph> <.01.</p> <hd id="AN0189408572-34">Hierarchical Linear Regression Analysis for Academic Aspirations</hd> <p>Model 1 explained 4.7% of the variance in aspirations to attend a four-year college, which was significant, ΔR<sups>2</sups> =.05, F(<reflink idref="bib3" id="ref140">3</reflink>,<reflink idref="bib174" id="ref141">174</reflink>) = 2.85, <emph>p</emph> =.039. Community influence (β = 0.19, <emph>p</emph> =.015) was the only significant positive variable. Model 2 explained 17% of the variance, ΔR<sups>2</sups> = 0.13, F(<reflink idref="bib4" id="ref142">4</reflink>,<reflink idref="bib170" id="ref143">170</reflink>) = 6.51, <emph>p</emph> <.001, with math self-efficacy (β = 0.27, <emph>p</emph> =.002) as a significant positive variable and science outcome expectations as a marginally significant positive variable (β = 0.22, <emph>p</emph> =.051). The effect size for this model, Cohen's F, was 0.16, indicating a medium effect. Model 3 explained 18% of the variance, but did not significantly improve the model, ΔR<sups>2</sups> = 0.01, F(<reflink idref="bib2" id="ref144">2</reflink>,<reflink idref="bib168" id="ref145">168</reflink>) = 1.07, <emph>p</emph> = 0.35. Math self-efficacy (β = 0.25, <emph>p</emph> =.007) remained the only significant positive variable. Cohen's F was 0.02, indicating a small effect. In summary, the addition of psychosocial variables in Model 2, after accounting for person inputs and background environment variables, explained a greater portion of the variance in the aspirations to attend a four-year college. The addition of math and science interest in Model 3 did not significantly enhance the model, suggesting that psychosocial variables (e.g., math self-efficacy and science outcome expectations) are more influential on the aspirations to attend a four-year college. The most consistent and significant positive variable across the three models was math self-efficacy (Table 4).</p> <p>Model 1 explained 12% of the variance in aspirations to obtain an advanced degree, which was significant, ΔR<sups>2</sups> = 0.12, F(<reflink idref="bib3" id="ref146">3</reflink>, 173) = 8.05, <emph>p</emph> <.001. Community influence (β = 0.33, <emph>p</emph> <.001) was the only significant positive variable. Model 2 accounted for 21% of the variance in academic aspirations, which was significant, ΔR<sups>2</sups> = 0.09, F(<reflink idref="bib4" id="ref147">4</reflink>,<reflink idref="bib169" id="ref148">169</reflink>) = 4.84, <emph>p</emph> =.001. Community influence (β = 0.27, <emph>p</emph> <.001) and science outcome expectations (β =.27, <emph>p</emph> =.013) were significant positive variables. The effect size for this model (Cohen's F = 0.11) indicated a small to medium effect. Model 3 explained 23% of the variance but did not significantly improve the model, ΔR<sups>2</sups> = 0.02, F(<reflink idref="bib2" id="ref149">2</reflink>,<reflink idref="bib167" id="ref150">167</reflink>) = 1.86, <emph>p</emph> = 0.159. Community influence (β = 0.29, <emph>p</emph> <.001) and science outcome expectations (β = 0.27, <emph>p</emph> =.020) remained significant positive variables, whereas math interest was positive and marginally significant (β = 0.15, <emph>p</emph> =.056). Cohen's F was 0.02, indicating a small effect. In summary, the addition of psychosocial variables in Model 2 provided a significant improvement over person inputs and background environment variables in understanding the associations with SCCT variables and students' aspirations to obtain an advanced degree. Adding subject-specific interests suggests that a strong interest in math may play a marginally important role in one's desire to obtain an advanced degree. The most consistent and significant positive variables across the three models were community influence and science outcome expectations. Results suggest that background and contextual factors, such as community influence, may have more influence on aspirations to obtain an advanced degree for our sample than it does on aspirations to attend a four-year college (Table 5).</p> <hd id="AN0189408572-35">Multivariate Analysis of Variance (MANOVA) for Geographic Region and Gender</hd> <p>A two-way multivariate analysis of variance (MANOVA) was conducted to examine differences in SCCT variables (math and science aptitude, community influence, math and science self-efficacy, math and science outcome expectations, math and science interest, aspirations to attend a four-year college, and aspirations to obtain an advanced degree) by geographic region and gender. Wilks' Lamba test indicated a significant effect on the combined dependent variables for both geographic region (Λ = 0.08, F(<reflink idref="bib22" id="ref151">22</reflink>, 312) = 36.15, <emph>p</emph> <.001) and gender (Λ = 0.85, F(<reflink idref="bib11" id="ref152">11</reflink>, 156) = 2.60, <emph>p</emph> = 0.005), but not for the interaction of geographic region and gender (Λ = 0.91, F(<reflink idref="bib22" id="ref153">22</reflink>, 156) = 0.65, <emph>p</emph> = 0.89).</p> <p>A series of univariate ANOVAs to examine the effect of geographic region on each variable is displayed in Table 6. Results revealed a significant effect of geographic region on community influence F(<reflink idref="bib2" id="ref154">2</reflink>, 328) = 832.71, <emph>p</emph> <.001, math self-efficacy F(<reflink idref="bib2" id="ref155">2</reflink>, 3.28) = 5.06, <emph>p</emph> =.007, aspiration to attend a four-year college F(<reflink idref="bib2" id="ref156">2</reflink>, 328) = 5.47, <emph>p</emph> =.005, and aspiration to obtain an advanced degree F(<reflink idref="bib2" id="ref157">2</reflink>, 328) = 12.99, <emph>p</emph> <.001. Using the Bonferroni Correction for multiple comparisons suggests that college towns have significantly higher levels of community influence compared to micropolitan and rural areas, with large effects (Cohen's D = 5.99 and 6.62, respectively). That is, significantly more high school seniors in college towns reported higher intentions to attend a four-year college in the year following high school graduation compared to seniors in micropolitan and rural areas. Additionally, high school seniors from micropolitan areas demonstrated significantly higher community influence compared to rural areas, but with medium effects (Cohen's D = 0.51). Regarding math self-efficacy, students from college towns demonstrated significantly higher levels of math self-efficacy compared to those from micropolitan areas (Cohen's D = 0.61), but not compared to rural areas. Academic aspirations also varied by location. Students from college towns demonstrated significantly higher aspirations to attend a four-year college compared to rural towns (Cohen's D = 0.56), but not compared to micropolitan areas. Finally, students from college towns were significantly more likely to have a desire to obtain an advanced degree compared to students from both micropolitan and rural areas, with medium to large effect (Cohen's D = 0.82 and 0.79, respectively). (Table 7)</p> <p>Table 6. Descriptive Statistics and MANOVA Results of SCCT Variables by Geographic Region</p> <p>Graph</p> <p> <ephtml> <table><thead valign="top"><tr><th align="left">SCCT variables</th><th align="left">Total (M ± SD, <italic>N</italic> = 177)</th><th align="left">College town (M ± SD, <italic>N</italic> = 62)</th><th align="left">Micropolitan (M ± SD, <italic>N</italic> = 49)</th><th align="left">Rural (M ± SD, <italic>N</italic> = 66)</th><th align="left">F</th><th align="left">Comparison</th></tr></thead><tbody valign="top"><tr><td align="left">Math aptitude</td><td align="char" char="plusmn">13.88 ± 3.29</td><td align="char" char="plusmn">13.44 ± 3.81</td><td align="char" char="plusmn">14.20 ± 2.79</td><td align="char" char="plusmn">14.05 ± 3.11</td><td align="char" char=".">0.89</td><td align="left" /></tr><tr><td align="left">Science aptitude</td><td align="char" char="plusmn">17.07 ± 3.39</td><td align="char" char="plusmn">17.66 ± 4.64</td><td align="char" char="plusmn">16.43 ± 2.73</td><td align="char" char="plusmn">17.00 ± 2.18</td><td align="char" char=".">1.85</td><td align="left" /></tr><tr><td align="left">Community influence</td><td align="char" char="plusmn">0.39 ± 0.14</td><td align="char" char="plusmn">0.57 ± 0.00</td><td align="char" char="plusmn">0.30 ± 0.02</td><td align="char" char="plusmn">0.28 ± 0.07</td><td align="char" char=".">832.71**</td><td align="left">College > Micro, rural; Micro > rural</td></tr><tr><td align="left">Math self-efficacy</td><td align="char" char="plusmn">4.97 ± 0.97</td><td align="char" char="plusmn">5.22 ± 0.91</td><td align="char" char="plusmn">4.64 ± 0.91</td><td align="char" char="plusmn">4.99 ± 1.01</td><td align="char" char=".">5.06**</td><td align="left">College > Micro</td></tr><tr><td align="left">Science self-efficacy</td><td align="char" char="plusmn">5.13 ± 0.95</td><td align="char" char="plusmn">5.26 ± 0.97</td><td align="char" char="plusmn">5.02 ± 0.80</td><td align="char" char="plusmn">5.09 ± 1.02</td><td align="char" char=".">0.95</td><td align="left" /></tr><tr><td align="left">Math OE</td><td align="char" char="plusmn">4.68 ± 1.11</td><td align="char" char="plusmn">4.84 ± 1.01</td><td align="char" char="plusmn">4.47 ± 1.21</td><td align="char" char="plusmn">4.70 ± 1.12</td><td align="char" char=".">1.52</td><td align="left" /></tr><tr><td align="left">Science OE</td><td align="char" char="plusmn">4.58 ± 1.05</td><td align="char" char="plusmn">4.78 ± 0.97</td><td align="char" char="plusmn">4.44 ± 1.03</td><td align="char" char="plusmn">4.51 ± 1.11</td><td align="char" char=".">1.77</td><td align="left" /></tr><tr><td align="left">Math interest</td><td align="char" char="plusmn">3.41 ± 1.36</td><td align="char" char="plusmn">3.44 ± 1.33</td><td align="char" char="plusmn">3.37 ± 1.33</td><td align="char" char="plusmn">3.41 ± 1.44</td><td align="char" char=".">0.03</td><td align="left" /></tr><tr><td align="left">Science interest</td><td align="char" char="plusmn">4.45 ± 1.25</td><td align="char" char="plusmn">4.68 ± 1.18</td><td align="char" char="plusmn">4.29 ± 1.13</td><td align="char" char="plusmn">4.37 ± 1.37</td><td align="char" char=".">1.60</td><td align="left" /></tr><tr><td align="left">Education (four-year college)</td><td align="char" char="plusmn">4.49 ± 0.87</td><td align="char" char="plusmn">4.77 ± 0.46</td><td align="char" char="plusmn">4.39 ± 1.04</td><td align="char" char="plusmn">4.30 ± 0.96</td><td align="char" char=".">5.47**</td><td align="left">College > rural</td></tr><tr><td align="left">Education (advanced degree)</td><td align="char" char="plusmn">3.43 ± 1.12</td><td align="char" char="plusmn">3.98 ± 0.97</td><td align="char" char="plusmn">3.12 ± 1.07</td><td align="char" char="plusmn">3.15 ± 1.11</td><td align="char" char=".">12.99**</td><td align="left">College > Micro, rural</td></tr></tbody></table> </ephtml> </p> <p>5 *<emph>p</emph> <.05. **<emph>p</emph> <.01.</p> <p>Table 7. Reported Effect Size (Cohen's D) by Geographic Region</p> <p>Graph</p> <p> <ephtml> <table><thead valign="top"><tr><th align="left">SCCT variables</th><th align="left">College town > micropolitan</th><th align="left">College town > rural</th><th align="left">Micropolitan > rural</th></tr></thead><tbody valign="top"><tr><td align="left">Math aptitude</td><td align="char" char=".">−0.23</td><td align="char" char=".">−0.19</td><td align="char" char=".">0.05</td></tr><tr><td align="left">Science aptitude</td><td align="char" char=".">0.36</td><td align="char" char=".">0.20</td><td align="char" char=".">−0.17</td></tr><tr><td align="left">Community influence</td><td align="char" char=".">5.99</td><td align="char" char=".">6.62</td><td align="char" char=".">0.51</td></tr><tr><td align="left">Math self-efficacy</td><td align="char" char=".">0.61</td><td align="char" char=".">0.24</td><td align="char" char=".">−0.37</td></tr><tr><td align="left">Science self-efficacy</td><td align="char" char=".">0.25</td><td align="char" char=".">0.18</td><td align="char" char=".">−0.07</td></tr><tr><td align="left">Math OE</td><td align="char" char=".">0.33</td><td align="char" char=".">0.13</td><td align="char" char=".">−0.20</td></tr><tr><td align="left">Science OE</td><td align="char" char=".">0.33</td><td align="char" char=".">0.26</td><td align="char" char=".">−0.07</td></tr><tr><td align="left">Math interest</td><td align="char" char=".">0.05</td><td align="char" char=".">0.02</td><td align="char" char=".">−0.03</td></tr><tr><td align="left">Science interest</td><td align="char" char=".">0.31</td><td align="char" char=".">0.25</td><td align="char" char=".">−0.07</td></tr><tr><td align="left">Education (Four-year college)</td><td align="char" char=".">0.46</td><td align="char" char=".">0.56</td><td align="char" char=".">0.10</td></tr><tr><td align="left">Education (advanced degree)</td><td align="char" char=".">0.82</td><td align="char" char=".">0.79</td><td align="char" char=".">−0.03</td></tr></tbody></table> </ephtml> </p> <p>A series of one-way ANOVAs to examine the effect of gender on each variable is found in Table 8. There was a significant effect of gender on math aptitude, F(<reflink idref="bib1" id="ref158">1</reflink>, 170) = 5.18, <emph>p</emph> =.024, where boys scored significantly higher on math aptitude (M = 14.38, SD = 3.61) compared to girls (M = 13.22, SD = 2.61), with a medium effect (Cohen's D = 0.35). Similarly, there was a significant effect of gender on science aptitude, F(<reflink idref="bib1" id="ref159">1</reflink>, 170) = 6.74, p =.010, where boys scored significantly higher on science aptitude (M = 17.59, SD = 2.88) than girls (M = 16.24, SD = 3.93), with a medium effect (Cohen's D = 0.41). There was a significant effect of gender for the desire to obtain an advanced degree, F(<reflink idref="bib1" id="ref160">1</reflink>, 170) = 5.59, <emph>p</emph> =.019. Girls demonstrated significantly higher aspirations to obtain an advanced degree (M = 3.71, SD = 1.01) compared to boys (M = 3.30, SD = 1.16), with a medium effect (Cohen's D = −0.37). In summary, the results indicate that boys scored significantly higher on measures of math and science aptitude, whereas girls expressed a significantly higher desire to obtain an advanced degree.</p> <p>Table 8. Descriptive Statistics and MANOVA Results of SCCT Variables by Gender</p> <p>Graph</p> <p> <ephtml> <table><thead valign="top"><tr><th align="left">SCCT variables</th><th align="left">Total (M ± SD, <italic>N</italic> = 172)</th><th align="left">Male (M ± SD, <italic>N</italic> = 104)</th><th align="left">Female (M ± SD, <italic>N</italic> = 68)</th><th align="left">F value</th><th align="left">Cohen's D</th></tr></thead><tbody valign="top"><tr><td align="left">Math aptitude</td><td align="char" char="plusmn">13.92 ± 3.29</td><td align="char" char="plusmn">14.38 ± 3.61</td><td align="char" char="plusmn">13.22 ± 2.61</td><td align="char" char=".">5.18*</td><td align="char" char=".">0.35</td></tr><tr><td align="left">Science aptitude</td><td align="char" char="plusmn">17.05 ± 3.39</td><td align="char" char="plusmn">17.59 ± 2.88</td><td align="char" char="plusmn">16.24 ± 3.93</td><td align="char" char=".">6.74**</td><td align="char" char=".">0.41</td></tr><tr><td align="left">Community influence</td><td align="char" char="plusmn">0.39 ± 0.14</td><td align="char" char="plusmn">0.40 ± 0.14</td><td align="char" char="plusmn">0.37 ± 0.14</td><td align="char" char=".">1.369</td><td align="char" char=".">0.18</td></tr><tr><td align="left">Math self-efficacy</td><td align="char" char="plusmn">5.01 ± 0.95</td><td align="char" char="plusmn">5.06 ± 0.87</td><td align="char" char="plusmn">4.92 ± 1.07</td><td align="char" char=".">.929</td><td align="char" char=".">0.15</td></tr><tr><td align="left">Science self-efficacy</td><td align="char" char="plusmn">5.16 ± 0.90</td><td align="char" char="plusmn">5.17 ± 0.91</td><td align="char" char="plusmn">5.13 ± 0.90</td><td align="char" char=".">.084</td><td align="char" char=".">0.05</td></tr><tr><td align="left">Math OE</td><td align="char" char="plusmn">4.69 ± 1.10</td><td align="char" char="plusmn">4.75 ± 1.07</td><td align="char" char="plusmn">4.60 ± 1.16</td><td align="char" char=".">.728</td><td align="char" char=".">0.13</td></tr><tr><td align="left">Science OE</td><td align="char" char="plusmn">4.59 ± 1.04</td><td align="char" char="plusmn">4.58 ± 1.04</td><td align="char" char="plusmn">4.61 ± 1.04</td><td align="char" char=".">.042</td><td align="char" char=".">−0.03</td></tr><tr><td align="left">Math interest</td><td align="char" char="plusmn">3.42 ± 1.36</td><td align="char" char="plusmn">3.45 ± 1.35</td><td align="char" char="plusmn">3.38 ± 1.38</td><td align="char" char=".">.107</td><td align="char" char=".">0.05</td></tr><tr><td align="left">Science interest</td><td align="char" char="plusmn">4.44 ± 1.25</td><td align="char" char="plusmn">4.32 ± 1.26</td><td align="char" char="plusmn">4.63 ± 1.21</td><td align="char" char=".">2.570</td><td align="char" char=".">−0.25</td></tr><tr><td align="left">Education (Four-year college)</td><td align="char" char="plusmn">4.51 ± 0.85</td><td align="char" char="plusmn">4.47 ± 0.90</td><td align="char" char="plusmn">4.57 ± 0.76</td><td align="char" char=".">.598</td><td align="char" char=".">−0.12</td></tr><tr><td align="left">Education (advanced degree)</td><td align="char" char="plusmn">3.46 ± 1.12</td><td align="char" char="plusmn">3.30 ± 1.16</td><td align="char" char="plusmn">3.71 ± 1.01</td><td align="char" char=".">5.59*</td><td align="char" char=".">−0.37</td></tr></tbody></table> </ephtml> </p> <ulist> <item>6 **Significance at or below the 0.01 level.</item> <item>7 *Significance at or below the 0.05 level.</item> </ulist> <p>Graph: Figure 1.Social Cognitive Career Theory ([<reflink idref="bib51" id="ref161">51</reflink>]) Adapted for Math and Science Domain</p> <hd id="AN0189408572-36">Discussion</hd> <p>The current study examined the relationships between SCCT variables (math and science aptitude, community influence, math and science self-efficacy, math and science outcome expectations, math and science interests), and the academic aspirations of high-achieving youth from rural and non-rural areas, specifically their desire to attend a four-year college and obtain an advanced degree (i.e., PhD, MD, and JD). Our findings advance the SCCT literature by the inclusion of a unique community influence variable, as measured by district-level college intention data from high school seniors in the sampled communities, and its focus on differences in SCCT variables across distinct geographic regions and gender.</p> <hd id="AN0189408572-37">Relationship Between SCCT Variables and Academic Aspirations</hd> <p>We examined the relationship between SCCT variables and the academic aspirations of high-achieving youth. Our findings provide support for H1 and H2, which predicted that math and science self-efficacy and outcome expectations would be positively associated with academic aspirations, beyond the influence of person input, background variables, and math and science interests. In line with our expectations, Model 2 had the largest F statistic in both regression analyses, explaining 17% and 21% of the variance for likelihood to attend a four-year college and obtain an advanced degree, respectively. Math self-efficacy emerged as the only significant and positive variable across Models 2 and Model 3 for four-year college aspirations, whereas community influence and science outcome expectations emerged as significant positive variables for advanced degree aspirations.</p> <p>Our findings underscore the crucial role that self-efficacy and outcome expectations play in shaping aspirations for higher education in math and science fields among high-achieving youth ([<reflink idref="bib19" id="ref162">19</reflink>]; [<reflink idref="bib38" id="ref163">38</reflink>]; [<reflink idref="bib50" id="ref164">50</reflink>]; [<reflink idref="bib55" id="ref165">55</reflink>]). [<reflink idref="bib54" id="ref166">54</reflink>] demonstrated that math self-efficacy predicted both STEM career choices and career success in math-related fields. Consistent with the SCCT literature, outcome expectations play a crucial role in shaping career and academic choices ([<reflink idref="bib51" id="ref167">51</reflink>]). The significant positive variable of science outcome expectations on advanced degree aspirations suggests that interventions aimed at improving students' perceptions of the value of science education continue to be instrumental in motivating students to pursue advanced degrees in math and science. Findings also suggest that contextual supports and environmental factors, such as community influence, may exert influence on the desire to pursue an advanced degree in math and science fields ([<reflink idref="bib3" id="ref168">3</reflink>]; [<reflink idref="bib55" id="ref169">55</reflink>]). This notion is also supported by [<reflink idref="bib85" id="ref170">85</reflink>] and [<reflink idref="bib64" id="ref171">64</reflink>], who found that student's career aspirations are shaped by their academic preparation and external influences, such as community support.</p> <hd id="AN0189408572-38">Relationship Between SCCT Variables and Geographic Region</hd> <p>We explored differences in SCCT measures across geographic regions (college towns, micropolitan, rural). Our findings provide partial support for H3. Students from college towns demonstrated significantly higher aspirations to obtain advanced degrees compared to those from micropolitan and rural areas. Additionally, students from college towns were significantly more likely to express a desire to attend a four-year college than rural students, though no significant difference was observed between college town and micropolitan students. These findings are consistent with prior research indicating that rural students face unique challenges in accessing higher education, such as limited availability of resources and lower levels of community and academic support ([<reflink idref="bib13" id="ref172">13</reflink>]; [<reflink idref="bib30" id="ref173">30</reflink>]; [<reflink idref="bib67" id="ref174">67</reflink>]).</p> <p>Contrary to our expectations, no significant differences were found in science self-efficacy, math and science outcome expectations, or math and science interest across geographic regions, except for math self-efficacy, which was significantly higher in college towns compared to micropolitan areas, but not compared to rural areas. This is notable given that rural students showed comparable math self-efficacy to college town students, yet reported significantly lower academic aspirations. These findings highlight that, despite facing different structural barriers, our distinct geographic regions possess similar internal motivations and self-beliefs.</p> <p>Finally, community influence was significantly higher in college towns compared to both micropolitan and rural areas, as well as significantly higher in micropolitan areas compared to rural areas. [<reflink idref="bib13" id="ref175">13</reflink>] underscore the role of community infrastructure in supporting high-achieving students, noting that rural students often lack access to advanced coursework and extracurricular programs that foster academic aspirations. Similarly, [<reflink idref="bib67" id="ref176">67</reflink>] found that geographic isolation often limits rural students' exposure to academic role models and college-related resources, further contributing to their lower academic aspirations. The current study's findings highlight the continued need for targeted interventions, particularly by improving access to community resources and strengthening local support systems in both micropolitan and rural communities, given the significant difference in academic aspirations observed.</p> <hd id="AN0189408572-39">Relationship Between SCCT Variables and Gender</hd> <p>We explored gender differences among SCCT variables. Our findings provided partial support for H4. Contrary to our prediction, boys demonstrated significantly higher levels of math and science aptitude compared to girls, but were not statistically different from girls on all other SCCT variables, with the exception of aspirations to obtain an advanced degree. [<reflink idref="bib12" id="ref177">12</reflink>] found that patterns of self-efficacy in high-achieving girls, particularly in rural areas, can deviate from broader trends in a more heterogeneous sample. These findings suggest that, while boys performed higher on aptitude tests in math and science, this did not translate into higher math and science self-efficacy, outcome expectations, or interest in these fields relative to girls.</p> <p>Our finding of observed differences in math and science aptitude across gender conflicts with results by [<reflink idref="bib13" id="ref178">13</reflink>], who found no significant differences in math and science aptitude between boys and girls. Additionally, [<reflink idref="bib75" id="ref179">75</reflink>] critically examined biological claims of gender differences in math and science ability, suggesting that such differences that emerge in early childhood are a result of gendered socialization compared to true differences in aptitude. For example, girls often begin to self-report lower math abilities than boys, despite having similar scores on math achievement tests ([<reflink idref="bib21" id="ref180">21</reflink>]). [<reflink idref="bib86" id="ref181">86</reflink>] outlined several factors contributing to gender disparities in math-intensive STEM fields, including absolute and relative ability differences, career and lifestyle preferences, field-specific beliefs, and gender stereotypes. In their review on absolute cognitive ability, they highlight that between 2006 and 2010, boys outnumbered girls approximately 4:1 and 3:1 in the top 0.01 % of the distribution for the math subtests of the SAT and ACT, respectively. They subsequently argue that if cognitive ability alone were a primary cause for women's underrepresentation in math-intensive fields, we could expect to see proportions of male and female STEM workers comparable to these ratios, which is not the case.</p> <p>Despite observed significant differences in math and science aptitude, girls in our study demonstrated significantly higher aspirations to pursue an advanced degree than boys, but not four-year college, reflecting recent trends in higher education where women are increasingly outpacing men in four-year degree attainment ([<reflink idref="bib56" id="ref182">56</reflink>]). These findings emphasize the need for interventions that continue to challenge gender norms and socialization processes, particularly around girls' participation in math and science. It may also be important to consider interventions that address the question of high-achieving boys falling behind girls in their desire to obtain an advanced degree. Educators play a critical role in addressing these disparities early in students' academic careers to ensure that high-achieving rural girls <emph>and</emph> boys have equitable access to opportunities for higher education.</p> <hd id="AN0189408572-40">Limitations</hd> <p>While the findings of this study contribute insights into the factors influencing academic aspirations for our population of interest, several limitations must be acknowledged. First, the cross-sectional nature of the study limits our ability to draw causal inferences about the relationships between our variables of interest and academic aspirations. Longitudinal research is needed to better understand how these variables interact over time and how they influence students' educational trajectories. Second, there is a question about social desirability in students' self-reported data. For example, there is consideration of whether students from college towns feel more pressure to endorse a higher likelihood of attending college because they feel external pressures in their communities ([<reflink idref="bib21" id="ref183">21</reflink>]). This desirability effect may become more pronounced given this sample captures those who are high-achieving in math and science, who may subsequently inflate reports of self-efficacy, interests, and academic aspirations. Third, the sample was not fully representative of the broader population, particularly regarding geographic diversity (i.e., Midwest sample) and students who are high-achieving. Although the study included students from college town, micropolitan, and rural areas, the findings may not generalize to other geographic regions outside of the Midwest (i.e., Appalachia or forested regions), demographic groups such as urban or suburban students, or to students who are not high-achieving. The impact of rural versus urban educational contexts is a well-documented issue ([<reflink idref="bib67" id="ref184">67</reflink>]), and future research should aim to include a more diverse sample to enhance generalizability about the academic aspirations of youth across broader geographic regions and across academic ability.</p> <hd id="AN0189408572-41">Implications and Future Research</hd> <p>The findings of this study have important implications for educational policy, practice, and future research. First, enhancing self-efficacy, particularly in mathematics, could be a key strategy in increasing college attendance and encouraging students to pursue advanced degrees. This aligns with recommendations from [<reflink idref="bib19" id="ref185">19</reflink>] and [<reflink idref="bib54" id="ref186">54</reflink>], who emphasize the importance of self-efficacy in career decision-making as well as many goals of existing career interventions ([<reflink idref="bib6" id="ref187">6</reflink>], [<reflink idref="bib8" id="ref188">8</reflink>]; [<reflink idref="bib12" id="ref189">12</reflink>]; [<reflink idref="bib57" id="ref190">57</reflink>]). Classroom interventions, extracurricular activities, and community-based programs that focus on math self-efficacy should be a continued priority. Second, efforts to improve students' science outcome expectations should be a focus. This may look like helping students understand the potential long-term benefits of a science-related career and creating partnerships with local businesses and higher education institutions to provide students with internships, job shadowing opportunities, and other experiences that highlight the relevance of science education. Finally, fostering a supportive community environment, particularly in rural and micropolitan areas, is crucial. Community leaders and educators should work together to create networks of support that encourage students to set high academic and career goals. This could involve creating local mentorship programs, celebrating the achievements of local graduates who pursue advanced degrees, and providing resources for families to support their children's educational journeys. This is consistent with the recommendations of [<reflink idref="bib7" id="ref191">7</reflink>] and ([<reflink idref="bib5" id="ref192">5</reflink>]), who advocate for community-based interventions to support the academic aspirations of rural students.</p> <p>While the study focused on domain-specific math and science-related variables, it did not account for other academic domains that might influence academic aspirations in math and science careers, such as reading or writing skills. The exclusive focus on math and science-related constructs may overlook the broader range of academic abilities that contribute to students' overall academic aspirations ([<reflink idref="bib74" id="ref193">74</reflink>]; [<reflink idref="bib77" id="ref194">77</reflink>]). Future research could include additional subject-specific SCCT variables to have a more holistic view of the interplay between subjects beyond math and science and their influence on academic aspirations ([<reflink idref="bib50" id="ref195">50</reflink>]). For example, it would be interesting to consider how skills in writing and reading contribute to academic aspirations in math and science beyond interest alone.</p> <p>This study intended to capture elements of community influence by looking at district-level college intention data, which is arguably a narrowed definition of community influence and does not capture nuances at the individual and societal level. For example, we did not explore the potential association of familial, peer, or teacher influences on the relationships between the study variables in terms of community influence ([<reflink idref="bib7" id="ref196">7</reflink>]; [<reflink idref="bib64" id="ref197">64</reflink>]), or individual students' perceptions of community influence. Future research could explore additional components of community influence, such as students' perceptions of peer, parental, and teacher support (e.g., [<reflink idref="bib9" id="ref198">9</reflink>]). Future studies may consider implementing existing measures of broader community-based support for higher education, such as the Contextual Support for Post-Secondary Planning Scale ([<reflink idref="bib3" id="ref199">3</reflink>]), to inform student's perceptions of what community influence looks like and means to them.</p> <p>Lastly, our study did not provide a holistic insight into trends of academic disparities or barriers for boys. Our study does suggest that girls expressed a higher likelihood of obtaining an advanced degree, which could speak to the trend of young girls benefiting from women pursuing doctoral degrees. It is possible that, because our sample contained only high-achieving students, it was not representative of recent trends in educational aspirations between boys and girls ([<reflink idref="bib16" id="ref200">16</reflink>]). Future research should explore trends across gender in a more heterogeneous sample in terms of ability and geography.</p> <hd id="AN0189408572-42">Conclusion</hd> <p>This study highlights the complex interplay between math and science aptitude, community influence, math and science self-efficacy, math and science outcome expectations, and math and science interests in shaping academic aspirations. While aptitude remains an important factor, the findings suggest that psychosocial constructs, particularly math self-efficacy, science outcome expectations, and community influence, play an important role in influencing students' intentions to pursue higher education. These insights underscore the need for comprehensive interventions that address both individual and contextual factors to support students in achieving their educational and career goals.</p> <hd id="AN0189408572-43">ORCID iD</hd> <p>Taylor A. Sommers https://orcid.org/0009-0005-3772-6221</p> <hd id="AN0189408572-44">Data Availability Statement</hd> <p>The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. *</p> <ref id="AN0189408572-45"> <title> References </title> <blist> <bibl id="bib1" idref="ref66" type="bt">1</bibl> <bibtext> Abel J. R., Deitz R. (2012). 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Understanding first-year persistence at a micropolitan university: Do geographic characteristics of students' home city matter? College Student Journal, 44(2), 362.</bibtext> </blist> </ref> <ref id="AN0189408572-46"> <title> Footnotes </title> <blist> <bibtext> The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project was funded by the US Department of Education Jacob K. Javits Talented and Gifted Education Program. Culturally Responsive Talent Identification and Career Exploration (TICE) Project, S206A170017.</bibtext> </blist> <blist> <bibtext> The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.</bibtext> </blist> </ref> <aug> <p>By Taylor A. Sommers; Saba Rasheed Ali; Duhita Mahatmya; Megan Foley-Nicpon and Susan G. Assouline</p> <p>Reported by Author; Author; Author; Author; Author</p> <p></p> <p>Taylor A. Sommers is a doctoral candidate in Counseling Psychology at the University of Iowa. Her research focuses on the academic and career aspirations of high-achieving youth, with broader interests in work psychology and women's experiences in the workplace. She provides career counseling to undergraduates pursuing helping professions. Taylor currently lives in Iowa City with her family. In her free time, she enjoys hiking and cooking, and can often be found at a local coffee shop.</p> <p>Saba Rasheed Ali, PhD serves as the Interim Associate Provost for Graduate and Professional Education and Dean of the Graduate College and Professor of Counseling Psychology at the University of Iowa. She earned her Ph.D. in Counseling Psychology from the University of Oregon in 2001 and her research has focused on vocational psychology and community-engaged research with an emphasis on rural K-12 students. Her scholarship has been published in various vocational psychology journals and she has co-edited The Handbook of Career and Workforce Development.</p> <p>Duhita Mahatmya, PhD, is an assistant professor of Learning Sciences and Educational Psychology at the University of Iowa. She received her doctorate in Human Development and Family Studies from Iowa State University and her Bachelor's degree in Psychology and English from Drake University. In her research, she uses an ecological systems framework and mixed methods to examine youth academic and social development across family, school, and community contexts. Her work is deeply influenced by her multicultural upbringing as a first-generation immigrant. She loves to travel, crochet, and a good podcast recommendation.</p> <p>Megan Foley-Nicpon, PhD, is the Myron and Jacqueline Blank Endowed Chair and Director of the University of Iowa's Belin-Blank Center, as well as a licensed psychologist and professor of Counseling Psychology. Dr. Foley-Nicpon's research and clinical interests include assessment and intervention with academically advanced students with disabilities, and the social and emotional development of talented and diverse students. She regularly writes and presents about advanced academics, counseling psychology, and twice-exceptionality. She is a fellow of the American Psychological Association, Division 17.</p> <p>Susan G. Assouline, PhD, is Emeritus Director of the University of Iowa Belin-Blank Center, and Emeritus Blank Endowed Chair in Gifted Education. Throughout her 50+ years in education, her practice and research have included talent discovery, academic acceleration, and twice-exceptionality. She led the collaboration with Belin-Blank Center colleagues to co-develop instruments about academic acceleration.</p> </aug> <nolink nlid="nl1" bibid="bib28" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib31" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib32" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib70" firstref="ref4"></nolink> <nolink nlid="nl5" bibid="bib12" firstref="ref5"></nolink> <nolink nlid="nl6" bibid="bib20" firstref="ref6"></nolink> <nolink nlid="nl7" bibid="bib23" firstref="ref7"></nolink> <nolink nlid="nl8" bibid="bib87" firstref="ref9"></nolink> <nolink nlid="nl9" bibid="bib30" firstref="ref10"></nolink> <nolink nlid="nl10" bibid="bib34" firstref="ref11"></nolink> <nolink nlid="nl11" bibid="bib67" firstref="ref12"></nolink> <nolink nlid="nl12" bibid="bib10" firstref="ref13"></nolink> <nolink nlid="nl13" bibid="bib83" firstref="ref15"></nolink> <nolink nlid="nl14" bibid="bib69" firstref="ref18"></nolink> <nolink nlid="nl15" bibid="bib25" 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Header DbId: eric
DbLabel: ERIC
An: EJ1483299
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: High-Achieving Rural Youth and Math and Science Talent Development: An Application of SCCT
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Taylor+A%2E+Sommers%22">Taylor A. Sommers</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0005-3772-6221">0009-0005-3772-6221</externalLink>)<br /><searchLink fieldCode="AR" term="%22Saba+Rasheed+Ali%22">Saba Rasheed Ali</searchLink><br /><searchLink fieldCode="AR" term="%22Duhita+Mahatmya%22">Duhita Mahatmya</searchLink><br /><searchLink fieldCode="AR" term="%22Megan+Foley-Nicpon%22">Megan Foley-Nicpon</searchLink><br /><searchLink fieldCode="AR" term="%22Susan+G%2E+Assouline%22">Susan G. Assouline</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Journal+of+Career+Development%22"><i>Journal of Career Development</i></searchLink>. 2025 52(5):648-673.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 26
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: SourceSuprt
  Label: Sponsoring Agency
  Group: SrcSuprt
  Data: Office of Elementary and Secondary Education (OESE) (ED), Jacob K. Javits Gifted and Talented Students Education Program
– Name: NumberContract
  Label: Contract Number
  Group: NumCntrct
  Data: S206A170017
– 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="%22Elementary+Education%22">Elementary Education</searchLink><br /><searchLink fieldCode="EL" term="%22Grade+6%22">Grade 6</searchLink><br /><searchLink fieldCode="EL" term="%22Intermediate+Grades%22">Intermediate Grades</searchLink><br /><searchLink fieldCode="EL" term="%22Middle+Schools%22">Middle Schools</searchLink><br /><searchLink fieldCode="EL" term="%22Grade+7%22">Grade 7</searchLink><br /><searchLink fieldCode="EL" term="%22Junior+High+Schools%22">Junior High Schools</searchLink><br /><searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22High+Achievement%22">High Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Rural+Youth%22">Rural Youth</searchLink><br /><searchLink fieldCode="DE" term="%22Talent+Development%22">Talent Development</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Aspiration%22">Academic Aspiration</searchLink><br /><searchLink fieldCode="DE" term="%22Grade+6%22">Grade 6</searchLink><br /><searchLink fieldCode="DE" term="%22Grade+7%22">Grade 7</searchLink><br /><searchLink fieldCode="DE" term="%22Self+Efficacy%22">Self Efficacy</searchLink><br /><searchLink fieldCode="DE" term="%22Expectation%22">Expectation</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Interests%22">Student Interests</searchLink><br /><searchLink fieldCode="DE" term="%22Community+Influence%22">Community Influence</searchLink><br /><searchLink fieldCode="DE" term="%22Gender+Differences%22">Gender Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Regional+Characteristics%22">Regional Characteristics</searchLink><br /><searchLink fieldCode="DE" term="%22Differences%22">Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematics%22">Mathematics</searchLink><br /><searchLink fieldCode="DE" term="%22Sciences%22">Sciences</searchLink><br /><searchLink fieldCode="DE" term="%22Middle+School+Students%22">Middle School Students</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1177/08948453251375947
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0894-8453<br />1556-0856
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This study applied Social Cognitive Career Theory (SCCT) to examine factors influencing the academic aspirations of high-achieving sixth and seventh graders (n = 178) in a Talent Identification and Career Exploration (TICE) program, emphasizing geographical and gender differences. Participants were from rural, micropolitan, and college towns in the Midwest. Hierarchical linear regression explored the role of math and science aptitude, self-efficacy, outcome expectations, interests, and community influence on academic aspirations. Math self-efficacy was significantly associated with four-year college aspirations, while science outcome expectations and community influence were associated with advanced degree aspirations. Notable gender and geographic differences emerged; girls reported higher aspirations for advanced degrees despite lower aptitude in math and science than boys, whereas students from college towns reported higher advanced degree aspirations than those from micropolitan and rural areas. Implications for research, intervention, and policy are discussed, including promoting math and science as a pathway for rural revitalization.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1483299
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1483299
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1177/08948453251375947
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 26
        StartPage: 648
    Subjects:
      – SubjectFull: High Achievement
        Type: general
      – SubjectFull: Rural Youth
        Type: general
      – SubjectFull: Talent Development
        Type: general
      – SubjectFull: Academic Aspiration
        Type: general
      – SubjectFull: Grade 6
        Type: general
      – SubjectFull: Grade 7
        Type: general
      – SubjectFull: Self Efficacy
        Type: general
      – SubjectFull: Expectation
        Type: general
      – SubjectFull: Student Interests
        Type: general
      – SubjectFull: Community Influence
        Type: general
      – SubjectFull: Gender Differences
        Type: general
      – SubjectFull: Regional Characteristics
        Type: general
      – SubjectFull: Differences
        Type: general
      – SubjectFull: Mathematics
        Type: general
      – SubjectFull: Sciences
        Type: general
      – SubjectFull: Middle School Students
        Type: general
    Titles:
      – TitleFull: High-Achieving Rural Youth and Math and Science Talent Development: An Application of SCCT
        Type: main
  BibRelationships:
    HasContributorRelationships:
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          Name:
            NameFull: Taylor A. Sommers
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            NameFull: Saba Rasheed Ali
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            NameFull: Duhita Mahatmya
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            NameFull: Megan Foley-Nicpon
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            NameFull: Susan G. Assouline
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          Dates:
            – D: 01
              M: 10
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 0894-8453
            – Type: issn-electronic
              Value: 1556-0856
          Numbering:
            – Type: volume
              Value: 52
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              Value: 5
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            – TitleFull: Journal of Career Development
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