choice behavior. .................................................................................................................17 Figure Arduino Code for Automated Irrigation System Control Based on Soil Moisture Thresholds ..........................................................................................................................27 Figure The site of the Study: Southeast of Nebraska, Johnson County, Sterling, and Syracuse public schools .....................................................................................................29 Figure Research study implementation workflow ..........................................................34 Figure Gender differences in pre-intervention SCCT constructs across rural areas .......60 Figure Hypothesized path model depicting Problem-Based learning SCCT ..................62 viii List of Tables Table Reliability analysis Table Model Fit Indices for Confirmatory Factor Analysis of Educational Construct ..45 Table Factor Loadings and Composite Reliability of Educational Construct ................46 Table Descriptive statistics of the participant, demographic location ............................48 Table Paired sample t-test results: changes in variables from pre to post intervention ..51 Table Multivariate test (Pillai's Trace
There is a pressing need to educate the next generation of STEM (Science, Technology, Engineering, and Mathematics) professionals, particularly in engineering fields. However, many countries have observed declining rates of STEM participation and achievement among K-12 students (Kennedy & Odell, 2023;Prendergast et al., 2014). This trend is concerning as it can hinder societies from maintaining economic and intellectual competitiveness on a global scale (Osborne & Dillon, 2008). Engaging students in STEM education from an early age is crucial for developing their identity and nurturing their enthusiasm over time (Tai et al., 2006). This is particularly important during the middle school years, as research suggests that by age 13, most students have already determined which careers they wish to exclude from consideration (Maltese & Tai, 2010). This critical period often results in measurable declines in scientific interest during early adolescence (Vedder-Weiss & Fortus, 2012). Underrepresented and underserved populations, including females, students of color, low-income students, and rural students, often lack access to high-quality engineering education opportunities (Jeffers et al., 2004). This lack of exposure during the middle school years can hinder the development of engineering interest, self-efficacy, and identity, which are essential precursors to pursuing engineering degree programs and careers (Dabney et al., 2012). Rural areas, in particular, face unique challenges in providing diverse STEM experiences, making them an important focus for intervention and study (Avery, 2013).
A major challenge in engaging students with STEM subjects, including engineering, is that many view these disciplines as abstract and irrelevant to their daily lives. This perception results in disengagement and decreased participation, particularly in the later stages of their STEM education (Ker et al., 2013;M. Te Wang & Degol, 2017). Factors such as selfefficacy, prior achievement, perceived difficulty, interest, and career aspirations all contribute to this disengagement. To address these challenges, educators and researchers have explored the potential of place-based, contextualized approaches to teaching and learning STEM. Place-based learning aims to increase students' positive attitudes towards science and engineering careers by introducing authentic, contextually relevant experiences into school science (Smith, 2002). By grounding STEM education in students' local environments and lived experiences, these approaches aim to improve student engagement, motivation, and the perceived relevance of STEM subjects (Smith, 2002;Sobel, 2004). Existing research suggests that Place-based learning can positively impact students' STEM career aspirations, particularly among underrepresented populations (Semken & Freeman, 2008). This approach could be particularly beneficial for engineering education, as it can help demonstrate the field's relevance and value to society, potentially increasing interest and participation rates (Holmes et al., 2021). However, despite the potential benefits of Place-based learning, few studies have directly examined its effects on middle school students' STEM career trajectories, especially in engineering, through the lens of social cognitive career theory (SCCT) (Lent et al., 1994). This gap in literature presents an opportunity for further investigation.
A major challenge in engaging students with STEM subjects, including engineering, is that many view these disciplines as abstract and irrelevant to their daily lives. This perception results in disengagement and decreased participation, particularly in the later stages of their STEM education (Ker et al., 2013;M. Te Wang & Degol, 2017). Factors such as selfefficacy, prior achievement, perceived difficulty, interest, and career aspirations all contribute to this disengagement. To address these challenges, educators and researchers have explored the potential of place-based, contextualized approaches to teaching and learning STEM. Place-based learning aims to increase students' positive attitudes towards science and engineering careers by introducing authentic, contextually relevant experiences into school science (Smith, 2002). By grounding STEM education in students' local environments and lived experiences, these approaches aim to improve student engagement, motivation, and the perceived relevance of STEM subjects (Smith, 2002;Sobel, 2004). Existing research suggests that Place-based learning can positively impact students' STEM career aspirations, particularly among underrepresented populations (Semken & Freeman, 2008). This approach could be particularly beneficial for engineering education, as it can help demonstrate the field's relevance and value to society, potentially increasing interest and participation rates (Holmes et al., 2021). However, despite the potential benefits of Place-based learning, few studies have directly examined its effects on middle school students' STEM career trajectories, especially in engineering, through the lens of social cognitive career theory (SCCT) (Lent et al., 1994). This gap in literature presents an opportunity for further investigation.
The current study aims to investigate the extent to which middle school students' experiences with Place-based learning affect their expectations to pursue engineeringrelated careers. By analyzing data through the SCCT framework, this research seeks to provide insights into effective strategies for broadening participation in the engineering pipeline, particularly among rural populations. This Place-based learning approach could help mediate the perceived difficulty of engineering subjects and make the field more appealing and accessible to a broader range of students.
Findings from this study can inform approaches to broadening participation in engineering by inspiring students through contextualized place-based projects. The results will help inform the best practices in broadening participation in engineering through outreach programming tailored to middle school students' geographies and lived experiences. The insights gained will be useful to outreach program developers, school counselors, STEM educators, and education policymakers interested in strengthening the engineering workforce pipeline. Identifying effective strategies for sparking students' motivational development can lead to increased diversity and sustainability of the engineering workforce. By investigating the effects of place-based engineering projects on rural Nebraska students' motivational outcomes and career aspirations through the lens of the social cognitive career theory (SCCT), this research aims to inform the development of tailored outreach programs and interventions that can enhance the diversity and growth of the engineering workforce.
The goal of this study was to demonstrate the impact of a place-based, hands-on engineering activities on rural middle school students' engineering career aspirations. By leveraging the SCCT framework, the aim was to understand the socio-cognitive mechanisms that connect the Place-based learning experiences to engineering career goals.
Our research is guided by the following objectives:
Goal 1: To investigate the impact of the place-based engineering activity on students' engineering career aspiration scores, examining how this Place-based learning approach influences their intentions to pursue engineering professions.
• Present statistical analysis of pre-and post-survey data showing changes in students' engineering career aspirations scores.
• Examine change in career aspiration scores following participation in the placebased engineering activity.
• Discuss implications of these quantitative findings for the effectiveness of placebased approaches in influencing career aspirations.
Goal 2: To examine the socio-cognitive mechanisms that mediate between students' Place-based learning experiences and their engineering career aspirations, identifying how these psychological processes connect Place-based learning activities to career development outcomes.
• Present path analysis of SCCT factors (self-efficacy, outcome expectations, interest) in relation to the place-based activity.
• Identify key relationships between SCCT constructs and Place-based learning experiences.
• Discuss a model illustrating how these factors interact to influence engineering career aspirations.
Goal 3: To investigate the role of place-based education in fostering the growth of engineering career aspirations, analyzing how these contextual learning experiences shape students' professional development in engineering fields.
• Present thematic analysis of students' video reflections.
• Highlight key factors from the activity that identified as contributing to increased engineering career aspirations.
• Discuss how place-based elements influenced students' perceptions of engineering careers, supported by qualitative evidence.
Accordingly, this study addressed the following research questions:
To what extent does participation in place-based engineering activity influence rural middle school students' engineering career aspirations? Sub-questions:
• Are there statistically significant differences (p-value <0.05) in engineering career aspiration scores before and after the Place-based learning activity
• Are there significant differences in the impact of place-based engineering activity on career aspirations scores between students who strongly identify with farming/agriculture and those who do not?
• Are there significant differences in the impact of place-based engineering activity on SCCT constructs (i.e., self-efficacy, goals, interest and outcome expectations) between male and female students in rural areas? 2. What is the added value of incorporating place-based elements into engineering activities for predicting career aspirations compared to engineering activities without place-based contextualization that rely solely on traditional SCCT constructs? Sub-questions:
• Does the Place-based learning experience directly predict students' engineering career aspirations?
• How do socio-cognitive factors, as defined by the SCCT, mediate the relationship between Place-based learning experiences and students' engineering career goals?
Qualitative Research Questions:
1. How does a place-based engineering activity affect students' future career goals in engineering?
• What elements of the activity do the students feel contributed to their interest and engagement in the activity?
• What belief and understanding about engineering emerged for the students as a result of the Place-based learning activity?
Chapter 2: Review of the Literature
Place-based learning is an instructional approach that centers on developing students' sense of place and facilitating learning through meaningful engagement with their environment.
By definition, Place-based learning emphasizes the relationship between physical locations, educational content, and students' lived experiences, creating contextually rich learning opportunities (Smith, 2002;Knapp, 2005).
• Integration of local contexts and environments into educational experiences (Zandvliet, 2014;Gruenewald, 2003;Knapp, 2005). This versatility allows Place-based learning to manifest in diverse forms and locations, including urban areas, cultural centers such as museums, or rural environments (Gruenewald, 2003).
Examples of effective Place-based learning implementation include on-campus activities utilizing green spaces and university facilities; community-based projects in gardens, local businesses, and non-profit organizations; field excursions to wildlife refuges and specialized locations; and international study abroad programs that immerse students in different cultural and environmental contexts.
Recent research has highlighted the potential of place-based and contextualized approaches in STEM education, particularly in addressing challenges related to student engagement and participation. This review synthesizes findings from several studies that explore the implementation and impact of such approaches.
Previous studies demonstrate that Place-based learning approaches yield significant benefits: fostering students' sense of belonging, enhancing learning outcomes, improving academic persistence, and narrowing equity gaps (Johnson et al., 2020). While students may initially experience anxiety about non-traditional learning environments (Goodlad et al., 2018;Maguire et al., 2007), most report positive experiences and perceive increased understanding of course content through these experiences (Maguire et al., 2007). Faculty similarly report higher levels of student engagement with place-based approaches compared to traditional classroom instruction (Goodlad et al., 2018).
Research has consistently shown that Place-based learning positively impacts student engagement and learning outcomes. According to (Goodlad et al., 2018), instructors report higher levels of student engagement with Place-based learning compared to traditional classroom lectures. This increased engagement is particularly noteworthy as it creates a more dynamic learning environment that encourages active participation rather than passive reception of information.
Moreover, others have demonstrated that Place-based learning contributes to multiple positive educational outcomes. (Johnson et al., 2020) found that Place-based learning fostered a stronger sense of belonging among students, improved academic persistence, and notably helped narrow equity gaps. These findings suggest that Place-based learning not only enhances learning but also addresses crucial aspects of student success and educational equity.
One of Place-based learning's strongest attributes is its versatility across different educational contexts and disciplines. As highlighted by Gruenewald (2003), Place-based learning can be effectively implemented in various settings, including urban areas, cultural centers, and rural environments. This flexibility allows for the integration of multiple educational approaches. Research indicates that students generally respond positively to Place-based learning experiences. Maguire et al. (2007) reported that most students express positive feelings toward outdoor field experiences, specifically noting high responses to indicators such as "thoroughly enjoyed," "wanted to go again," and "worthwhile." Importantly, students also perceived these experiences as enhancing their understanding of course content, suggesting that Place-based learning successfully combines engagement with effective learning.
A fundamental strength of Place-based learning lies in its ability to transform students from passive recipients of knowledge into active knowledge creators. As noted by (Smith, 2002) and (Knapp, 2005), Place-based learning encourages students to:
• Engage in inquiry-based learning
• Study real-world issues in specific locations
• Develop practical solutions to community problems
• Create meaningful connections between academic content and lived experience Place-based learning uniquely positions students to engage with their communities and apply their learning in practical contexts. Whether through cultural studies, community issue investigation, environmental research, or civic participation, students develop realworld skills while contributing to their communities. This approach aligns education with actual community needs and challenges, making learning more relevant and meaningful (Zandvliet, 2014). Through internships and community engagement opportunities, Placebased learning provides valuable professional development experiences. These experiences allow students to understand the practical applications of their studies and develop professional skills such as communication, teamwork, problem-solving, time management, and technical proficiency in real-world contexts, better preparing them for future careers (Smith, 2002). Delahunty et al. (2021) examined integrated STEM curriculum models through Irish primary school teachers' perspectives. Their research revealed that teachers observed enhanced student learning outcomes and development of collaborative skills when implementing integrated approaches similar to Place-based learning. From the educators' observations, these integrated STEM activities created more authentic learning experiences that connected classroom concepts with real-world applications. The study demonstrated how integrated approaches can make STEM subjects more engaging and accessible to diverse learners from the instructional perspective, highlighting Place-based learning's potential to transform student engagement with STEM content. Fraser et al. (2021) addressed the issue of lower STEM participation among rural, regional, and remote (RRR) communities. They proposed the place-based STEMalignment framework as an analytical tool to map and document place-based STEM discourse across academic and community stakeholders. This approach aims to enhance RRR students' engagement with STEM by aligning learning with their rural and community-based identities and creating stronger pathways to further STEM education and careers through contextually relevant applications and local connections. Attard et al. (2021) reported on a teacher professional learning program that incorporated industry expertise into inquiry-based STEM teaching. By involving experts from large infrastructure projects, the program enhanced student engagement across operative, cognitive, and affective domains. Both students and teachers appreciated the contextualization of learning within local infrastructure projects, highlighting the benefits of applying knowledge to authentic, place-based contexts.
Similarly, Gallay et al. (2021) found that using local environmental issues as context for the mathematics and science curriculum was effective in non-dominant urban communities. This approach not only improved STEM engagement but also promoted civic action, empowering youth to positively impact their communities while gaining authentic STEM knowledge. Holmes et al. conducted a literature review of studies from 2016-2021 on localized STEM curriculum that adapted science, technology, engineering, and mathematics content to incorporate local contexts, challenges, and cultural elements (Holmes et al., 2021). They identified numerous benefits, including increased student aspirations, enjoyment, interest, and engagement in STEM, as well as improvements in transferable skills like teamwork and communication. However, the review also revealed limitations, such as time constraints for teachers and difficulties in securing community engagement. Videla et al. (2021) proposed an interactive and ecological approach to STEM and STEAM learning, emphasizing interactions between learners and their environment.
Their empirical studies in New Zealand and Chile demonstrated how basing learning on authentic STEM problems can help learners develop scientific skills with contextual utility.
Place-based learning has shown potential to improve student engagement, increase interest in STEM, and foster long-term benefits such as increased STEM career aspirations (Martín-Páez et al., 2019;Shahali et al., 2017). These benefits are particularly salient for RRR students. As highlighted by Mills et al. (2021), rural students' ethnogeographiestheir cultural knowledge and connections to people and places-play a crucial role in shaping their career aspirations, including those related to STEM fields. The collective findings from these studies underscore the significant benefits of Place-based learning approaches in STEM education, particularly in shaping students' career aspirations. By grounding STEM concepts in familiar, local contexts, these approaches can demystify STEM careers and make them more attainable in students' minds. This is especially crucial in rural Nebraska, where its low population density makes it more challenging to provide centralized educational resources and experiences, highlighting why exposing rural populations to current technology and STEM education is both particularly challenging and critically important. Research indicates that nearly all students choosing higher education selected career programs they had encountered in their communities (Byun et al., 2012), further emphasizing the importance of introducing STEM concepts in these familiar, yet often resource-limited, community settings.
However, implementing Place-based learning approaches is not without challenges.
The literature reveals several limitations, including teachers' lack of confidence in teaching across multiple disciplines, perceived lack of subject knowledge, an overcrowded curriculum, and resource constraints (Delahunty et al., 2021). Additionally, there are challenges in maintaining ongoing connections between schools and STEM industries to keep learning relevant and authentic, as well as the increased time required for teachers to plan integrated curriculum programs (Margot & Kettler, 2019). Despite the growing body of research on Place-based learning in STEM education, there remains a significant gap in literature. While hands-on engineering design projects have been shown to positively impact students' motivation and identity development related to engineering (Carr et al., 2012), most school curricula do not incorporate substantial place-based engineering design challenges. Moreover, a few studies have directly investigated the impact of Place-based learning on engineering career aspirations among rural students specifically. Hence, the aim of this study was to investigate the extent to which students' experience in place-based engineering learning affected their expectation to pursue engineering as their future career.
As the literature review suggests, while Place-based learning has shown promise in educational contexts (Johnson et al., 2020), there remains a significant gap in examining the specific impact of place-based hands-on engineering experiences on rural middle school students' engineering career aspirations. This study addresses this gap by investigating how localized engineering learning experiences influence students' career goals through the lens of SCCT.
The existing literature demonstrates that Place-based learning can foster a sense of belonging and improve student academic persistence in STEM fields (Johnson et al., 2020), yet limited research has explored its specific application in rural engineering education contexts. While studies have shown that Place-based learning can effectively combine problem-based learning with community-specific issues (Knapp, 2005;Smith, 2002), there is a notable absence of comprehensive research examining how these approaches specifically influence rural students' engineering career aspirations and selfefficacy.
Furthermore, while researchers have documented the general benefits of Placebased learning in various educational settings (Goodlad et al., 2018;Gruenewald, 2003), few studies have employed a mixed-methods approach to understand how these experiences specifically shape rural students' career goals through socio-cognitive factors. This study will contribute to the existing literature by providing insights into how placebased, hands-on engineering experiences can be effectively designed and implemented to enhance rural students' engagement with engineering concepts and career pathways. The findings will be particularly valuable for educators and researchers seeking to develop more effective approaches to engineering education in rural contexts and for those working to broaden participation in engineering fields.
The theoretical framework used in this study was the social cognitive career theory (SCCT) which is a well-established theoretical framework developed by Lent et al. (1994). SCCT is derived from Albert Bandura's general social cognitive theory (1986) and is particularly useful in understanding and explaining the processes involved in academic and career development, choice, and performance (Bandura, 1986). This model emphasizes the crucial roles of self-efficacy and outcome expectations in shaping future goals. In science education, numerous studies have demonstrated a positive correlation between selfefficacy and students' performance (Lavonen & Laaksonen, 2009;Britner & Pajares, 2001) as well as their engagement in science-related activities (Britner & Pajares, 2001).
Similarly, outcome expectancy has been identified as a significant predictor of students' career intentions in science (Fouad & Smith, 1996;Holmegaard et al., 2014).
The SCCT model expands on these concepts by incorporating contextual factors and personal inputs to explain career decision-making processes. These external factors, represented as contextual support and barriers, interact with personal inputs to moderate goals and actions, as illustrated in Figure 1. A key component of the SCCT model is the role of learning experiences, which serve as a mediator between personal backgrounds and socio-cognitive mechanisms. Despite the significance of learning experiences in the model, relatively few vocational developmental studies have explored this aspect in depth. One notable exception is the work of Gainor and Lent (1998), who examined the impact of four math-related learning experiences on self-efficacy and outcome expectations (Gainor et al., 1998). Their findings revealed significant relationships between these learning experiences and socio-cognitive mechanisms.
Note. Variables in the shaded boxes are the primary focus of the current study. ))
The SCCT model provides a comprehensive framework for predicting students' academic or career choices, taking into account the interplay between personal backgrounds, selfefficacy, outcome expectations, and interests. This holistic approach offers valuable insights into the complex factors influencing career decision-making processes. Schaub et al. (2005) examined the relationship between personality and interest across Holland's (1997) RIASEC themes (i.e., realistic, investigative, artistic, social, enterprising, and conventional occupational interest categories), mediated by learning experiences. Their findings strongly supported the positive correlation between learning experiences and both self-efficacy beliefs and outcome expectations. Importantly, they observed that learning experiences primarily influenced outcome expectations indirectly through self-efficacy, aligning with the predictions of Lent et al. (1994) SCCT model. In the context of science education, Taskinen et al. (2013) investigated how extracurricular science activities impact students' career choices through interest and self-concept. Their multi-level analyses revealed that these extra learning experiences indirectly affected students' future-oriented motivation by influencing their interest and self-concept. Wang et al. (2013) conducted a longitudinal study with recent high school graduates entering universities to expand the understanding of STEM major selection based on the SCCT framework. The results indicated a strong correlation between the intent to major in STEM (influenced by exposure to science courses) and actual STEM major choices. These studies collectively support the hypothesis that Place-based learning, as a learning experience, influences students' future career choices through self-efficacy and outcome expectations, as depicted in the SCCT model. This framework provides a valuable lens for understanding the complex interplay between learning experiences, personal factors, and career decision-making in STEM fields. By grounding the current study in SCCT, a better understanding of the mechanisms through which place-based engineering projects may inspire and prepare underrepresented rural students to pursue engineering pathways can be gained. The theory's emphasis on self-efficacy, outcome expectations, contextual influences, and personal factors aligns well with the study's objectives and design, making it a suitable lens for investigating the impact of these contextualized engineering experiences on rural students' motivational outcomes and career aspirations. Second, SCCT recognizes the importance of contextual influences, such as learning experiences and exposure to role models, in shaping career development processes. The place-based nature of the engineering project, which connects students' local communities, serves as a contextual influence that may positively impact their interests and goals.
Qualitative data from student reflections will shed light on how this contextualized approach shapes their motivations. Third, the theory accounts for the role of personal and background factors, like gender and prior exposure to engineering, as potential moderators or mediators in the relationship between interventions and career-related outcomes. This study can explore how these factors may influence the effects of the place-based project on rural students' aspirations. Furthermore, SCCT has been successfully applied in previous research examining the career development of underrepresented groups in STEM fields, including middle school students (Navarro et al., 2019;Nugent et al., 2015). These studies have demonstrated the utility of SCCT in understanding the factors that influence STEM career aspirations and in informing interventions to broaden participation.
By grounding this study in SCCT, the aim was to gain a better understanding of the mechanisms through which place-based engineering projects may inspire and prepare underrepresented rural students to pursue engineering pathways. The theory's emphasis on self-efficacy, outcome expectations, contextual influences, and personal factors aligns well with the study's objectives and design, making it a suitable lens for investigating the impact of these contextualized engineering experiences on rural students' motivational outcomes and career aspirations.
Chapter 3: Intervention: The Place-Based Engineering Activity
The novel engineering activity introduced as "Smart Garden Activity" was specifically designed to resonate with the lived experiences of students in rural, agricultural communities. Focusing on the design process for an automated irrigation system utilizing soil moisture sensors and Arduino programming, this activity was intentionally crafted to bridge the gap between cutting-edge engineering concepts and the agricultural backdrop familiar to many rural students. This approach is especially crucial in rural areas like Nebraska, where a significant portion of the population (1 in 4 jobs) is involved in farming and agriculture-related occupations (Mills et al., 2021). This 50-minute hands-on activity Students then learned about the engineering design process and its steps, identifying the problem as designing a water pump that could be activated by the Arduino soil moisture engineering design principles could be applied to create an automated irrigation system, tying together the activity's various components. This approach aimed to create a highly engaging and relevant learning experience that resonated with the rural students' ethnogeographies and agricultural backgrounds.
The place-based engineering activity was delivered as a structured, hands-on learning experience focused on agricultural engineering relevant to the rural Nebraska context. The activity, titled "Build Your Smart Garden!", was facilitated by a diverse research team of three engineers including myself from the University of Nebraska-Lincoln in April and May 2024. While I led all sessions, the supporting team members varied across different school visits, ensuring a diverse representation of engineering disciplines and backgrounds at each location
The session began with the three-member research team introducing themselves to students. The team was led by the author, and other team members included both female and male engineering students. Each team member shared their engineering backgroundbiomedical, chemical, and civil engineering -and briefly described the nature of their work.
This introduction was intentionally designed to provide students with diverse role models in engineering and to demonstrate various engineering pathways. By highlighting their different specializations, the team illustrated the interdisciplinary nature of engineering while making their roles accessible and relatable to middle school students.
Following introductions, the team engaged students in a foundational discussion about engineering, establishing key concepts before moving into the hands-on activity. Students were first introduced to what engineering is, illustrated through visual examples of young people engaging in engineering activities. This was followed by a discussion about the importance of engineering across various domains, with particular emphasis on areas including improving people's lives, medical innovations, communication systems, transportation systems and agricultural applications. The introduction highlighted essential engineering skills including hands-on work, creativity, problem-solving, and teamwork, making explicit connections between engineering and skills students were already developing.
To make the activity relevant to students' lived experiences, the facilitators employed an engaging approach, asking students to raise their hands if they had plants at home and discuss how they determined when plants needed watering. This personal connection established relevance before scaling up to agricultural applications.
The team then expanded the context by asking: "If you were responsible for caring for thousands of plants or managing a larger field, how would you effectively water such a vast area?" This question bridged students' personal experiences with larger agricultural challenges faced in their communities, demonstrating how engineering could address real local needs. Students were prompted to think about devices or technologies that might help determine soil moisture levels, introducing them to the concept of soil moisture sensors as an engineering solution to agricultural water management.
The activity incorporated authentic examples of engineering in agriculture, showing how soil moisture sensors could be deployed in fields to monitor conditions and how automated irrigation systems could use this data to water crops efficiently. This demonstration included:
• How moisture sensors provide data to computers or mobile devices
• How automated irrigation systems respond to sensor readings
To further establish relevance, the team compared large-scale agricultural requirements (showing that a tomato field of one acre requires approximately 27,924 liters of water per day) with small container gardening (requiring approximately 20 ml per day), illustrating the scalability of engineering solutions while connecting to both home gardening and commercial agriculture.
After establishing context and relevance, students were presented with their engineering design activity: to design a water pump system that could automatically determine when to stop watering plants based on soil moisture readings. The facilitators guided students through a structured engineering design process consisting of eight steps:
1. Define the Problem: Students articulated the core problem-determining how a pump should know when to stop watering a tomato plant based on appropriate soil moisture levels.
2. Determine Objectives and Deliverables: The primary objective was to find the correct soil moisture level that would signal when to stop watering tomatoes.
The activity concluded with a demonstration of a functional water pump system. Students were shown the Arduino code controlling the irrigation system, as indicated in Figure 3: functioning automated system using programmable microcontrollers and sensor technology. Throughout the activity, students engaged with authentic engineering practices in a context directly relevant to their rural community, connecting classroom learning to agricultural applications while developing a deeper understanding of how engineering addresses real-world challenges in their local environment. The diverse research team provided role models that represented different genders and engineering disciplines,
This study employed a mixed methods approach to investigate the influence of place-based engineering learning experiences on rural middle school students' career aspirations. The selection of mixed methods was guided by the pragmatic view that research methods should align with research questions to obtain the most useful answers (Johnson & Onwuegbuzie, 2004). As defined by Creswell et al. (2003), a mixed methods study involves collecting and analyzing both quantitative and qualitative data, with integration occurring at one or more stages of the research process.
The choice of a mixed methods approach was particularly appropriate for this study as it allows for a comprehensive understanding of how Place-based learning influences career aspirations through both measurable outcomes and rich, contextual insights. Following Creswell et al. (2003) framework, this study employed a triangulation design where:
1. Quantitative data from pre-and post-activity surveys provided measurable evidence of changes in career aspirations and related socio-cognitive factors 2. Qualitative data from student reflections offers deeper insights into how these experiences shape students' understanding of engineering and its relevance to their lives 3. The integration of both data types enables a more complete understanding of the phenomenon than either approach alone could provide
The study's design follows Creswell et al. (2003) concurrent triangulation approach, where quantitative and qualitative data were collected simultaneously and integrated during analysis. This methodology was selected to provide a comprehensive understanding of students' experiences through complementary data sources: surveys capturing broader patterns and trends across the participant group, while video reflections revealed nuanced personal insights and explanations. The simultaneous collection of different data types allows for a more holistic analysis of students' responses to the Place-based learning activities, with each method compensating for limitations in the other and strengthening the overall validity of findings. And lastly, the integration of results during interpretation enabled the identification of convergent findings while also revealing complementary aspects of how Place-based learning influences career aspirations
The quantitative component was focused on measuring changes in career aspirations and related constructs through instruments with strong validity evidence, while the qualitative component explores the lived experiences and meaning-making processes of students as they engage with place-based engineering activities. This combination provided what (Tashakkori et al., 2010) describe as complementary data that offsets the weaknesses inherent in using either method alone.
Following the mixed methods literature (Borrego et al., 2009), the analysis strategy used in this study involved:
i. Separate analysis of quantitative and qualitative data using appropriate methodological techniques
ii.
Integration of findings to identify areas of convergence and complementarity
iii.
Use of qualitative findings to explain and elaborate on quantitative results, particularly in understanding the mechanisms through which Place-based learning influences career aspirations This mixed methods approach enabled the study to not only measure the effectiveness of Place-based learning in fostering engineering career aspirations but also understand how and why these experiences resonate with rural students' lived experiences and cultural contexts.
Prior to commencing the study, approval was obtained from the Institutional Review Board
To assess the various components of SCCT, survey instruments with strong validity evidence were utilized. Responses on a four-point Likert scale, where participants indicated their level of agreement from "Strongly Disagree" (1) to "Strongly Agree" (4) were coded or recoded so that higher scores would reflect more positive perceptions or attitudes from students. The questions corresponding to the core variables of this study are listed in appendices B and C.
Self-efficacy beliefs: Selected items from the Engineering Skills Self-Efficacy Scale (Fouad et al., 1996) and the scale from (Gibbons et al., 2004) were adapted to measure students' confidence in their abilities to succeed in engineering-related tasks. The original scales demonstrate strong internal consistency (Fouad: α = 0.87-0.92; Gibbons: α = 0.85) and construct validity through factor analysis. The language was modified to be ageappropriate for middle school students while maintaining the core constructs. These items were administered before and after the project to assess changes in students' engineering self-efficacy.
Outcome expectations: Adapted items from the Engineering Outcome Expectations Scale (Fouad et al., 1996) and the scale from (Gibbons et al., 2004) were used to explore how the activity shaped students' beliefs about the potential benefits, rewards, or consequences of pursuing an engineering pathway. The original instruments show good reliability (Fouad: α = 0.85; Gibbons: α = 0.84) as well as good internal consistency evidence. The wording was simplified to ensure comprehension by middle school participants. The wording was simplified to ensure comprehension by middle school participants.
Interests and goals: Selected items from The Engineering Career Interests and Goals Scales (Fouad et al., 1996) and the scales from (Gibbons et al., 2004) were adapted to investigate the impact of the activity on students' interests in engineering-related activities and their intentions to pursue engineering majors or careers. The original scales demonstrate strong internal consistency (α = 0.89) and predictive validity for STEM career choices.
Contextual support and barriers: Adapted items from the Contextual Support and Barriers Scale (Fouad et al., 1996) was used to assess students' perceptions of factors that may facilitate or impede their pursuit of engineering careers, such as parental support, financial resources, or access to role models. Background and context: A Background Questionnaire (Capobianco et al., 2011) was administered to gather information about students' personal and contextual factors, including gender, race/ethnicity, socioeconomic status, and prior exposure to engineering-related activities. The original scale shows good reliability across subscales (α = 0.82-0.86) and has been used with diverse adolescent populations. Language was modified for middle school comprehension.
Role models and learning experience: Selected items from (Siegel & Ranney, 2003) and (Schmidt et al., 2019) The internal consistency reliability of the survey instrument was assessed using Cronbach's
Alpha coefficients for both pre-test and post-test measures across seven constructs.
Cronbach's Alpha is widely used to evaluate the reliability of multi-item scales, with values ranging from 0 to 1 (Tavakol & Dennick, 2011). Generally, alpha values above 0. showed poor reliability in the pre-test (α = 0.363) but improved substantially to acceptable reliability in the post-test (α = 0.681). This significant increase suggested that the intervention may have clarified participants' goal-related perceptions.
Support and barriers (3 items) exhibited poor reliability in both pre-test (α = 0.381) and post-test (α = 0.190). The decrease in reliability for this construct was concerning and may have indicated that these items were not consistently measuring the intended construct of engineering support and barriers (Lance et al., 2006). Due to these consistently low reliability scores, the support, and barriers construct were excluded from further analysis.
This construct was removed from subsequent analyses to ensure the overall integrity and validity of the findings. Learning experiences (8 items) were only measured in the posttest, showing excellent reliability (α = 0.870). This suggests that the items effectively capture various aspects of participants' learning experiences. Overall, most constructs showed either improved or consistent reliability from pre-test to post-test, with the exception of the support and barriers construct, which was subsequently removed. Support and barriers (3 items) exhibited poor reliability in both pre-test (α = 0.381) and post-test (α = 0.190). The decrease in reliability for this construct was concerning and may have indicated that these items were not consistently measuring the intended construct of engineering support and barriers (Lance et al., 2006). Due to these consistently low reliability scores, the support, and barriers construct were excluded from further analysis.
This construct was removed from subsequent analyses to ensure the overall integrity and validity of the findings. Learning experiences (8 items) were only measured in the posttest, showing excellent reliability (α = 0.870). This suggests that the items effectively capture various aspects of participants' learning experiences. Overall, most constructs showed either improved or consistent reliability from pre-test to post-test, with the exception of the support and barriers construct, which was subsequently removed.
Table 2 presents descriptive statistics for the psychological variables measured before and after the intervention. The result of a Kolmogorov-Smirnov test for normality was conducted using SPSS software. This test is commonly used to assess whether data follows a normal distribution, so the Test Statistic is showing the K-S test statistic values. The sample consisted of 142 participants who provided survey data at both time points. This represents the total number of students who participated in the full study process. All the variables were measured on a scale from 1 to 4, with means ranging from 2.333 to 3.008, suggesting that participants' responses tended to cluster around or slightly above the midpoint of the scales. Standard deviations (ranging from 0.455 to 0.585) indicate moderate variability in responses across all measures. Examining changes from pre-to post-intervention, slight increases in mean scores were observed for several variables including self-efficacy, outcome expectations, interests, goals, and background/context measures. Prior to conducting parametric analyses, data was examined for normality. Skewness values were calculated for all variables to ensure the assumptions for parametric statistical tests were met.
The confirmatory factor analysis was performed using R programming language and relevant statistical packages (Appendix F). Six observed constructs were considered: pre and post measures of interest, goal, outcome expectations, background/context, and selfefficacy, plus a post-learning experience measure. As shown in
Table 3, the measurement models generally reached adequate levels of model fit, with most constructs showing CFI and TLI values above 0.90, RMSEA values below or close to 0.08, and SRMR values below 0.08. For example, the post-learning experience measure demonstrated good fit (CFI = 0.941, TLI = 0.917, RMSEA = 0.073 [0.042-0.103], SRMR = 0.055). Reliability and validity were assessed based on Fornell and Larcker's (1981) criterion which evaluates the extent to which a construct explains more variance in its indicator variables than it shares with other constructs. This approach helps determine whether the measurement items reliably represent the intended constructs and can be distinguished from other constructs in the model. Composite reliability (CR) values varied across constructs, with some meeting the recommended threshold of 0.70 (e.g., pre-interest: 0.75, post-interest: 0.79, post-Learning experience: 0.87) and others falling below (e.g., pre-Goal: 0.49, pre-Self Efficacy: 0.43). Factor loadings ranged from 0.099 to 0.797 across all constructs, with postlearning experience showing consistently strong loadings (0.605 to 0.744) (Table 4). While some measures, particularly post-intervention ones, demonstrated satisfactory reliability and validity, others may benefit from further refinement. Higher factor loadings indicate stronger relationships between the observed variables and their underlying constructs, with values above 0.5 generally considered acceptable. The inconsistent loadings for some preintervention constructs suggest that students may have had less coherent understanding of these concepts before participating in the place-based activity. In summary, the measurement model was generally strongest for assessing students' perceptions after participating in the place-based engineering activity, particularly regarding their learning experiences. This suggests that the intervention may have helped students develop more coherent understanding of engineering concepts, leading to more consistent responses.
Additionally, students likely gained a better understanding of the survey questions themselves after engaging with relevant engineering activities, allowing them to respond more consistently to related items. The weaker pre-intervention measurements may reflect students' limited exposure to engineering concepts before the activity, resulting in less consistent response patterns. Overall, the model provided a foundation for assessing the impact of the place-based engineering activity, with strongest support for the post-Learning experience measure. Results: Mixed-Method Analysis of the Place-based Engineering Activity 5.1. Descriptive Statistics Table 5 provides a detailed overview of the demographic data for the sample of 142 participants, categorized by school, area, gender, and grade level. The data is presented with frequencies and percentages for each category. Before proceeding with further analysis, it was noted that approximately 2% of all individual question responses were missing across the dataset. Specifically, out of the total possible responses (142 participants
× number of questions across both pre-and post-surveys), 2% of these individual data points were not completed by participants. These missing values were addressed through imputation using the mean scores for the corresponding questions. The decision to use imputation rather than case deletion was made to preserve the full sample size, as removing participants with any missing data would have substantially reduced statistical power and compromised our ability to detect meaningful effect sizes in the analysis.
The following results address the first objective of this study which was investigating the impact of the place-based engineering activity on students' engineering career aspiration scores.
To address my primary research question, to what extent does participation in place-based engineering activity influence rural middle school students' engineering career aspiration scores, I conducted a comprehensive analysis of our pre-and post-survey questionnaire.
Table 6 illustrates the results of pre and post test scores to evaluate the impact of the placebased engineering intervention across SCCT variables. All variables show improvement, indicating a positive overall effect of the intervention. Notably, the most substantial changes were observed in participants' goals and outcome expectations. All variables show improvement, indicating a positive overall effect of the intervention. Notably, the most substantial changes were observed in participants' goals and outcome expectations, as shown in Table 6. Other variables demonstrated more modest increases. Interests showed a slight uptick, potentially indicating a marginal enhancement in participants' engagement
or curiosity within the relevant domain. Self-efficacy, a crucial construct in the SCCT theory, exhibited a minor increase, hinting at a possible small improvement in participants' perceived capabilities. The background/context measure also saw a small rise, which could reflect a broader understanding or appreciation of the contextual factors surrounding the intervention's focus area. It is noteworthy that all measured variables demonstrated consistent, albeit small, increase from pre-to post-intervention.
However, it is crucial to emphasize that while these descriptive statistics provide valuable insights into the directionality and magnitude of changes, they do not in themselves confirm the statistical significance of these differences. To rigorously assess whether these observed changes represent meaningful effects of the intervention or are within the bounds of random variation, further statistical analyses were warranted.
To investigate the changes and differences in more detail, I conducted paired sample t-tests for each variable. The results of these analyses are presented in Table 6. As one can see, changes are observed between pre-and post-intervention measurements.
Statistically significant differences (p < 0.05) were found for two variables: Outcome expectations (p = 0.007) and Goals (p = 0.009). These results indicate that participants experienced meaningful improvements in their outcome expectations and goal-setting after the intervention. For the other variables (Self-efficacy, Background/context and Interests, the differences between pre-and post-intervention scores were not statistically significant (p > 0.05). This suggests that while there were slight improvements in these areas as shown in the bar chart, these changes were not substantial enough to be considered statistically significant. The largest mean difference was observed for Outcome expectations (0.1150), followed closely by Goals (0.1039), which aligns with the visual trends in the bar chart.
Overall, these results provide statistical support for the intervention's effectiveness in enhancing participants' outcome expectations and goals, while indicating that changes in other areas, though positive, were more modest. For the paired-sample t-tests, we calculated Cohen's d to assess the effect size for each measure. Cohen's d was used to quantify the magnitude of mean differences, which is commonly applied in paired sample analyses. As shown in Table 6, the results revealed small effect sizes across most measures, with outcome expectations and goals showing slightly larger effects than the other variables. This suggests that while there were statistically significant changes in some areas, the practical significance, as measured by Cohen's d, is generally limited.
I further investigated the effect of time (Pre vs. Post) using a repeated measures analysis to examine changes across multiple dependent variables, as shown in Table 7. This analysis aimed to determine whether significant changes occurred in the measured domains from before to after the intervention. To quantify the effect size, we additionally calculated eta squared to understand the proportion of variance explained by the time factor, providing insight into how much of the change was attributable to the intervention. By using both Cohen's d and eta squared, I was able to gain a more nuanced understanding of the effect sizes, capturing both standardized mean differences and explained variance. The results were evaluated through multivariate statistics, specifically Pillai's Trace, which assessed the overall impact of the time factor on the collective set of dependent variables.
Pillai's Trace, which is commonly used due to its robustness in multivariate testing, yielded a value of 0.127 with an associated F-statistic of 3.288, indicating statistical significance (p=0.005), as shown in Table 7. This result suggests that time has a significant multivariate effect on the set of dependent variables as a whole which means a significant overall effect of the intervention across all engineering-related variables with a moderate effect size (partial η² = 0.127), indicating that 12.7% of the variance was explained by the intervention when considering all engineering-related measures collectively.
In practical terms, this indicates a notable overall impact of the within-subject factor, warranting a closer look at each specific variable's change over time. To further understand where these changes occurred, we examined contrasts for each individual variable, focusing on whether there were significant Pre-to-Post differences as shown in
Table 8. I. Self-Efficacy: The analysis for Self-Efficacy revealed no significant change from Pre to Post (p=0.758). This indicates that the observed minor change in self-efficacy scores (2.968 to 2.981) did not reach the threshold for statistical significance. II. Background Context: Similarly, Background Context did not exhibit significant change (p = 0.141), indicating that this aspect of participants' experiences did not shift substantially from Pre to Post. III. Outcome Expectations: In contrast, Outcome Expectations demonstrated a statistically significant change (p = 0.007), with a Partial Eta Squared of 0.051. This effect size implies that approximately 5.1% of the variance in Outcome Expectations is explained by time. This significant finding indicates a moderate, meaningful increase in participants' outcome expectations from Pre to Post. IV. Interest: Interest did not show a statistically significant difference from Pre to Post (p = 0.324), suggesting that participants' interest levels were relatively consistent over time.
V. Goal: For Goal, the analysis also showed a significant change (p = 0.009), with a Partial Eta Squared of 0.047. This result indicates a moderate effect, with 4.7% of the variance in Goal scores attributable to time, suggesting that participants experienced a meaningful shift in their goal-related measures.
In summary, while the overall multivariate test indicates a significant effect of time across the measured domains, a more detailed look reveals that only specific areas such as Outcome Expectations and Goal showed statistically significant shifts. Both of these domains saw moderate effects with Pre-to-Post changes, as indicated by Partial Eta Squared values of 0.051 and 0.047, respectively.
These findings suggest that while the intervention was effective overall in influencing engineering-related career aspirations, its impact was most pronounced in shaping participants' expectations about engineering outcomes and their engineeringrelated goals. The moderate effect size for the overall intervention (12.7% variance explained) indicates meaningful change in students' engineering-related perspectives, despite the varying effectiveness across individual measures. The strongest effects were observed in areas related to future-oriented thinking about engineering (outcomes and goals), while more immediate or personal attributes like engineering self-efficacy and interest in engineering showed less responsiveness to the intervention.
This suggests that, while some aspects of participants' experiences remained unchanged, there were meaningful increases in their outcome expectations and goal orientation over time for their future in engineering, while suggesting that additional or modified approaches might be needed to effect change in areas such as engineering selfefficacy, interest, and background/context. The differential impact across variables provides valuable insights for future refinements of the engineering-focused intervention, particularly in areas where significant changes were not observed.
Table 2. Multivariate test (Pillai's Trace) results: significant changes across SCCT constructs of the same subjects over time Within subject effects Value F Hypothesis df Error df Sig. Partial Eta Squared Factor 1 Pillai's trace 0.127 3.288c 6.000 136.000 0.005 0.127 To further understand the nature of the area effect, I needed to conduct post-hoc analyses to determine which specific areas differed from each other and in what ways.
Additionally, the univariate results were examined for each dependent variable which provided more detailed insights into which specific aspects (specific SCCT constructs)
were most influenced by the demographic area. Using a post-hoc analysis using (Tukey's HSD) we compared the mean differences in change scores across town, farm, and other rural areas for each of our measured constructs. The most notable finding emerged in the "Outcome Expectations" construct. Here, a statistically significant difference between town and farm areas (mean difference = 0.2442, p = 0.025) was observed. Students from town areas showed a greater positive change in their outcome expectations compared to those from farm areas. To explore the potential reasons for this difference, we examined the pre-test scores for the town and farm students. Interestingly, I found that town students entered the activity with lower initial outcome expectations compared to farm students.
The mean pre-test score for outcome expectations among town students was 2.77 (SD = 0.45), while farm students had a mean pre-test score of 3.0171 (SD = 0.48). This initial difference suggests that town students had more room for growth in their outcome expectations after participating in the activity. These findings suggest that the place-based engineering activity appears to have brought town students' outcome expectations more in line with those of their farm counterparts, potentially equalizing perceptions about engineering career outcomes across these different rural contexts. It is worth noting that students from "other" category also showed a positive change in outcome expectations (mean change = 0.1481, SD = 0.50), though the small sample size (N = 9) for this group limits the conclusions we can draw. For the remaining constructs -Self-Efficacy, Background/Context, Interests, and Goals -I did not observe statistically significant differences between the geographical areas. This suggests that the impact of the placebased engineering activity on these aspects was relatively consistent across different rural setting recognizing the importance of considering pre-existing differences in students' perceptions when designing and implementing place-based engineering activities, I decided to investigate possible differences in pre-test scores.
To achieve this goal, (MANOVA) was conducted on the pre-test scores, this approach allowed me to examine all possible interactions between school, area, and gender in relation to students' initial perceptions and expectations before participating in the placebased engineering activity (Table 10). The most notable finding from this pre-test analysis is the significant interaction between area and gender. This suggests that prior to the placebased engineering activity, male and female students had different initial perceptions and expectations depending on whether they were from town, farm, or other rural areas and confirms our earlier observation that demographic context plays a crucial role in shaping students' initial perceptions and expectations.
Significant main effects of area were observed across multiple constructs, including self-efficacy (p = 0.001), background/context (p = 0.004), outcome expectations (p = 0.001), and interests (p = 0.023), highlighting the importance of local context in shaping students' initial views. However, the most striking finding was the consistent and strong interaction between area and gender across all constructs, suggesting that the influence of demographic context differs markedly between male and female students (Figure 6). This interaction was particularly pronounced for outcome expectations (p < 0.001) and interests (p = 0.001). Notably, while town areas showed gender parity, farm areas displayed significant gender gaps favoring male students across all measures, including self-efficacy The pre-test findings revealed intriguing gender differences across demographic areas in students' engineering career aspirations, as measured by SCCT variables. This led to a crucial question: How might the place-based engineering module influence these differences? I was particularly interested in whether the demographic area would continue to play a role in shaping gender differences after the intervention, and if my module could help narrow any existing gaps. To explore this, I delved into a post-intervention analysis. I wanted to see if the gender differences I observed initially would persist, shrink, or perhaps even disappear after students participated in the engineering module. More importantly, I was curious about whether the impact of our intervention would vary depending on where students lived -in town, on farms, or in other rural areas. This analysis wasn't just about measuring change; it was about understanding how our educational approach might differently affect boys and girls across various rural settings.
Encouragingly, the post-intervention analysis revealed that our place-based engineering module was indeed effective in significantly reducing the gender gap across different demographic areas. The interaction effect between area and gender (Pillai's Trace = 0.132, F = 1.233, df = 14, 244, p = 0.251) was no longer statistically significant, suggesting that this place-based intervention had successfully mitigated the pre-existing gender disparities across various rural settings. Notably, after participation in the activity, there were no significant differences observed in any of the measured variables across school, area, or gender (all p > 0.05), as shown in Table 11.
In response to my second research question, which explores the added value of This approach enabled me to simultaneously evaluate multiple hypothesized relationships, providing a comprehensive view of how various factors interact to shape career goals. My This multi-pathway approach captures the nuanced interactions between experiences, cognitive factors, and career aspirations. Place-based learning experiences serve as the primary independent variable, while engineering career goals remain the main dependent variable. Personal factors (gender) and background context function as control variables, affecting both learning experiences and other SCCT constructs. Student demographics, specifically whether they originate from a farm or town environment, act as the moderator variable in this model. This approach enables me to quantify both direct and indirect effects, offering insights into the complex factors influencing career decisionmaking process. The indirect pathways from Place-based learning experiences to career goals provide further insights. The strongest indirect path operates through self-efficacy and interest (β = 0.1668, p < .001), closely followed by the path through self-efficacy alone (β = 0.1651, p = .017). Additional significant indirect paths include those through outcome expectations and interest (β = 0.0569, p = .046) and through self-efficacy, outcome expectations, and interest (β = 0.0421, p = .013) (Table 13). These findings paint a nuanced picture of how Place-based learning experiences influence engineering career goals. While Place-based learning experiences have a direct effect on career goals, their influence is primarily mediated through self-efficacy and interest. This suggests that Place-based learning experiences enhance students' self-efficacy, which in turn increases their interest in engineering, ultimately leading to stronger career goals.
Although outcome expectations are positively influenced by Place-based learning experiences and self-efficacy, their impact on career goals appears to be indirect, primarily through their effect on interest.
The total effects of Place-based learning experiences on engineering career goals, combining both direct and indirect pathways, are positive and significant across all paths.
• What belief, understanding and practices about engineering emerged for the students as a result of this experience?
To engineering activity. The video prompt was carefully crafted to guide students' reflections while allowing for open-ended responses. It included four main sections:
• Personal Introduction: Name, grade, and an interesting fact about themselves.
• Reflections on Engineering: How the smart garden activity influenced their perceptions of engineering.
• Future Engineering Activities: Their interest in further engineering activities and specific areas of interest.
• Career Aspirations: Whether they envision engineering as part of their future career.
I employed thematic analysis (TA) as described by Braun & Clarke (2024) to examine the qualitative data collected from open-ended survey questions and video reflections. This method was chosen for its accessibility, flexibility, and ability to identify patterns of meaning across diverse datasets, aligning well with our research goals of understanding students' collective experiences with place-based engineering activities. I followed Braun and Clarke's six-phase approach, which involved familiarization with the data, generating initial codes, searching for themes, reviewing themes, defining and naming themes, and producing the final report. An inductive approach was employed as depicted in Braun & Clarke (2024). Our code development process closely followed the team-based coding method; two researchers independently immersed themselves in the data, identifying codes and labels without preconceived notions. This approach allowed the data to drive the development of our codebook, ensuring that our analysis was grounded in the students' experiences rather than existing constructs.
After completing independent coding, the researchers held regular meetings to review and agree on the identified codes, aligning with the process of evaluating inter-rater reliability and reconciliation. This iterative process of independent coding followed by collaborative discussion continued until a comprehensive and agreed-upon codebook was established. The researchers then independently coded the entire dataset using this refined codebook. Throughout this process, I remained flexible in revising our codebook as new insights emerged, following the feedback loops described in (Stuart et al., 2002). This rigorous, data-driven approach enabled me to capture the nuances of students' responses and develop a rich, contextualized understanding of their experiences with place-based engineering activity. The two researchers then analyzed the data to identify emerging themes, discussing their findings to ensure a comprehensive understanding. They reviewed, defined, and refined these themes, assigning clear names to each. This collaborative process ultimately led to the production of the final report, which captured the key themes and insights from the data.
Sub-research question 1: What elements of the activity do the students feel contributed to their interest and engagement in the activity?
Theme 1: Hands-on engagement and real time feedback through technology.
This theme is concerned with how the engineering activity presented to the students captured their interest and engagement through key elements that resonated strongly with their learning preferences and curiosity. This theme encapsulates the students' enthusiastic responses to the hands-on nature of the task and the incorporation of technology that provided immediate feedback on their actions. Many participants, including Stella, appreciated that the activity didn't involve much talking from the teachers or instructors.
As Stella put it, "There wasn't a lot of talking." Throughout the activity, students repeatedly expressed their enjoyment and high levels of engagement, attributing these positive experiences to the "interactive" and "hands-on" aspects of the task.
time cause-and-effect relationship seemed to resonate strongly with the students, as
evidenced by examples of comments mentioned by David, Laure, Bob and Jack.
Saha: "I think engineering is cool and I like how I thought I kind of want to know more about how the sensor like detects how much water is in or how much moisture is in soil".
Saha's expression of interest in understanding the underlying mechanism of the sensor, particularly its ability to "detect moisture, indicates that the sensor technology sparked curiosity beyond the immediate task at hand.
And what I really liked yesterday was how the sensor detected the moisture in the soil" Sara: "I did not know that they electronics that could measure moisture"
This curiosity indicates a deeper level of engagement with technology and suggests that the activity successfully piqued their interest in the scientific principles behind the engineering application. This reveals that students were not just passive users of technology but were actively questioning and wondering about its functionality. This intellectual curiosity and desire to learn more about sensor functionality, is a valuable outcome of the activity, as it demonstrates that students were engaging with the material at a conceptual level, trying to understand the how and why behind the technology they were using.
Theme 2: Connection to place and real-life application.
The engineering activity not only engaged students through its hands-on technological approach but also resonated deeply by connecting to their sense of place and lived experiences, sparking meaningful realizations that engineering is not just an abstract concept, but a practical discipline that is deeply connected to their everyday lives. Through the activity, students began to see the relevance of engineering in familiar settings, especially within the context of agriculture and farming, which resonated strongly with many of them.
Zara shared, "I learned how farmers detect how much water their crops have and need,"
emphasizing how engineering plays a role in the agricultural processes they witness regularly. Eric mentioned this sentiment: "I learned several things, and I would do it again! I never really thought of scaling something down rather than the big picture. I work on the farm during the weekend, and it is cool to apply it to school and learn more about it!".
This highlights a key aspect of the learning experience-breaking down complex systems into smaller, manageable parts that students could directly relate to their lived experiences.
The connection between farming and engineering surprised many students, like Joe, who Theme 3-1: Engineering is not just taking things apart: Demonstrates students' evolving perception of engineering as a multifaceted discipline that extends beyond simply taking things apart (Ema: "engineering is not just taking things apart"). Through their experiences, students recognized the "power of engineering" in building and improving systems that "impact" their everyday lives. They learned that "engineering is a lot of coding and hands-on work (Araz)," solving real-world problems like "measuring soil moisture"
and "watering plants with sensors". Through their hands-on experiences, students began to grasp the "power of engineering" in building, designing, and improving systems that directly impact their everyday lives.
classroom projects, as an important field that "makes our world better," and "can impact everyone."
Aysan: "Made me change my thinking a little, because made me think how good engineering will work in like life, how important it is."
Another student noted, "I think engineering's cool because you're going to build things.
You build stuff, and you make our world better."
Melisa: "And we were in the lab yesterday where we learned about engineering and how it impacts our everyday lives with like our lifestyle, our health, pretty much all of the above."
Warisa: "And how being an engineer could impact everyone."
Theme 3-2: Engineering takes place everywhere: As Ashley points out "engineering takes place everywhere", it highlights students' recognition that "engineering is like in your life all the time" (Brandon), permeating every aspect of daily life, regardless of career or setting. One student noted, "It does not matter what job you have, 'cause engineering will always be involved" (Jay). Through the hands-on activity, students developed a deeper and broader understanding of the engineering profession and its role in everyday life.
Students expressed surprise at the realization that "farming is engineering." Many had not previously considered the link between agriculture and engineering, with one student stating, "I had no idea that farming involved engineering," while another echoed,
"We also learned it takes a lot of engineering to work on a farm." These discoveries opened their eyes to the unexpected places where engineering plays a key role, particularly in their own community, which is closely tied to farming and agriculture.
This experience made students aware of the many ways engineers contribute to the world. One student reflected, "I learned how vast the field of engineering is and how many things engineers do for the world." (Bradely). They began to perceive engineering as a discipline that touches everyday life in multiple ways, from "watering plants with sensors"
(Emily) to creating systems that "set any program to make something work." (Ross).
Another student remarked, "Engineering is used in everyday activities. Farmers use engineering to water plants easier," showing how this experience made them see engineering in practical, accessible ways that connected directly to their environment.
The activity also broadened students' perceptions of the diverse roles within engineering. They now have a better appreciation for "the different types of engineering"
(Joe) and the various "jobs that involve engineering" (Eric). For example, one student noted, "I learned more about what jobs have engineering," and another added, "Engineering can be in many different ways," emphasizing their growing awareness of the breadth and flexibility of engineering as a career path.
Ultimately, this hands-on activity expanded students' understanding of engineering and its omnipresence in daily life. Whether through designing irrigation systems for farming or tackling challenges in other industries, students came to appreciate that engineers play a crucial role in solving real-world problems and improving the world around them. As one student summarized, "Engineering relates to life," encapsulating their newfound understanding that engineering is an essential part of shaping the world they live in.
Theme 4: We want more engineering, but the future feels uncertain.
This theme highlights the students' enthusiasm and "curiosity" for engaging in more engineering activities, while also reflecting their uncertainty about whether engineering will be part of their future. The hands-on experience expanded their perspective on engineering as a field; many students hadn't previously associated farming with
engineering, but now they understand how closely the two are linked. One student, Emma, shared, "I had no idea that farming involved engineering," illustrating how their awareness grew during the activity. This newfound understanding sparked interest, with many students expressing a desire to explore "more engineering activities in school." For many, the activity offered a more engaging and interactive experience than their "normal science
classes." Daniel said, "It was better than our normal science class," while Sophie added, "The smart gardening experiment made us a little more curious about engineering." These comments underscore how hands-on Place-based learning made engineering feel more accessible and exciting to the students. One student noted, "I would like to do a lot more activities in school involving engineering, more about building things," showing a clear desire for more practical experiences. Despite this enthusiasm, some students felt unsure about how engineering might fit into their future careers. Keylee reflected, "I haven't really done anything involving engineering, and I wasn't looking into anything engineering," revealing that even though the activity sparked interest, it didn't necessarily clarify whether engineering would be part of their long-term plans. Another student, Lily, shared, "I feel like I want to do more activities like this, depending on what it involves. And then I don't think there's going to be engineering in my future, but maybe." This sentiment shows both interest and hesitation, indicating that while the activity piqued their curiosity, it didn't fully dispel uncertainties about pursuing engineering. Some students also expressed a desire for more support and resources to continue exploring engineering at school. Mia commented, "It was very cool, and it would be even better if other schools got taught the same thing," suggesting that broader access to such hands-on learning experiences could foster more interest in engineering across different schools.
Ultimately, while the hands-on place-based experience made students "more curious about engineering" and left them wanting more, they still expressed uncertainty about whether engineering would play a role in their future careers. The activity was a step in the right direction, engaging students and helping them see engineering in a new light, but it also highlighted the need for more consistent exposure to engineering concepts and activities to help students confidently envision a future in the field.
increased their enjoyment of the activity but also helped them build confidence in their engineering skills, consistent with research showing that hands-on experiences can enhance students' confidence and interest in STEM fields (Freeman et al., 2014).
The incorporation of real-time feedback from sensor readings also allowed students to connect abstract engineering concepts with tangible outcomes, which is crucial for developing both interest and competence in engineering. The students' curiosity about how the sensor detected moisture and their desire to "know more" about the technology demonstrates deeper engagement, reinforcing the positive link between self-efficacy and interest that is central to the social cognitive career theory. This aligns with research that highlights the role of immediate feedback and hands-on learning in improving STEM education outcomes, especially for underrepresented groups (Bevan et al., 2015;Vossoughi et al., 2016).
Our quantitative analysis revealed a significant improvement in students' outcome expectations (β = 0.387, p < .001), which aligns with prior research emphasizing the importance of contextually relevant learning experiences in enhancing students' career aspirations (Aikenhead & Ogawa, 2007;Chinn & Duncan, 2021). The theme, "Connection to lived experiences and real-life application", reveals how students connected the engineering activity to their everyday experiences, particularly in farming. For many students, recognizing how sensor technology could be used to "detect moisture levels in crops" and "design irrigation systems" deepened their understanding of the relevance of engineering in their lives. This reflects findings from the literature that place-based and contextualized learning can help students see STEM fields as accessible and relevant to their own lives, particularly for rural students (Garii et al., 2009;Howley et al., 2011).
The connection between engineering and students' real-life contexts also supports research that suggests students are more likely to develop positive outcome expectations when they can see the practical benefits of the field in their own communities (Hulleman & Harackiewicz, 2009). In this study, students not only saw the relevance of engineering to tasks such as "detecting moisture" in crops but also began to understand how engineering could benefit their families and neighbors, reinforcing the idea that place-based education can foster a sense of community and social responsibility (Smith, 2002). This deep connection between personal experience and learning likely contributed to the increased outcome expectations observed in the quantitative data.
Prior to the intervention, female students, particularly those from farming backgrounds, reported lower self-efficacy and interest in engineering compared to their male peers. Postintervention, these gender differences disappeared, suggesting that the activity played a key role in equalizing self-efficacy across genders. This is supported by research showing that hands-on, problem-based learning can be particularly beneficial for female students, who often face gendered stereotypes that discourage their participation in STEM (Eccles, 2011;Wang & Degol, 2017). The qualitative theme, "Engineering is not just taking things apart", illustrates how students' perceptions of engineering shifted during the activity, with many students, especially girls realizing that engineering involved more than just mechanical tasks. By recognizing that engineering is about problem-solving and improving systems, students began to see the field as more accessible and relevant to their lives.
This change in perception is significant, as it aligns with studies that show how broadening the definition of engineering and showcasing its diverse applications can help to close the gender gap in STEM (Diekman et al., 2017;Master et al., 2017). The handson activity allowed female students to engage directly with engineering tasks such as "measuring soil moisture" and "watering plants with sensors," which shifted their views of engineering as a valuable and creative field. These findings underscore the importance of inclusive, hands-on learning experiences that can help female students build confidence and interest in engineering careers (Barker-Collo et al., 2015).
While the intervention had a significant impact on students' self-efficacy and outcome expectations, its direct effect on career goals was marginal (β = 0.135, p = .091). This reflects findings from previous studies, which suggest that a single exposure to engineering is often insufficient to solidify students' career aspirations (Lent et al., 2008;Schunk et al., 2005). The qualitative theme, "We want more engineering, but the future feels uncertain", further explains this marginal effect. Although students expressed excitement about the hands-on activity and a desire for more engineering opportunities, they remained unsure about how engineering might fit into their future careers. This aligns with research showing that while short-term interventions can spark initial interest, sustained experiences are necessary to build long-term career aspirations in STEM fields (Maltese et al., 2010).
Several students indicated that their curiosity was piqued by the activity, but they were uncertain about the resources and opportunities available to explore engineering further, particularly in rural schools. As one student noted, "It was very cool and would be if other school got thought the same thing." This reflects broader findings in the literature that rural students often face limited access to STEM opportunities, which can hinder their ability to pursue STEM careers (Howley et al., 2011). The marginal effect on career goals suggests that while place-based, hands-on activities can spark initial interest, more sustained exposure to engineering activities is needed to translate this interest into solid career aspirations.
This study, while providing valuable insights into the impact of place-based engineering activities on rural middle school students' career aspirations, has several limitations that should be acknowledged.
This study was conducted with 142 participants from rural Nebraska schools, which limits the generalizability of findings to other geographical contexts. The recruitment of teachers relied primarily on email invitations, which may have resulted in a selection bias toward educators already interested in STEM integration. The unique agricultural context of Nebraska may influence students' receptiveness to the irrigation-focused engineering activity in ways that might differ in urban or suburban settings or in rural areas with different economic bases.
The pre-post design employed in this study, while appropriate for measuring immediate changes in students' perceptions, does not allow for assessment of long-term impacts on career aspirations and educational choices. The timing between the activity and post-survey administration was inconsistent across schools, with some students completing assessments immediately following the activity and others doing so up to one week later.
This inconsistency may have affected the measurement of immediate impacts and introduced variability in the data that could not be fully controlled for in the analysis.
The adaptation of SCCT measurement instruments for middle school students, while necessary, introduced potential validity concerns. Though expert review was conducted, the modified instruments may not capture the constructs with the same precision as the original validated measures designed for older populations. This was evident in the lower reliability coefficients observed for some pre-intervention measures.
The qualitative data collection through videos had limitations related to the depth and authenticity of student responses. Some students may have simply echoed phrases from the presentation rather than expressing original thoughts. The video format, while engaging for many students, may have been intimidating for others, potentially limiting the diversity of voices captured. Additionally, technical challenges with the Flipgrid platform and variations in school technology access created inconsistencies in the quantity of qualitative data collected across sites.
While designed as a Place-based learning experience, the intervention may have emphasized certain aspects of place-based pedagogy more than others. The activity focused primarily on the agricultural application of engineering principles without fully exploring the cultural, historical, and community dimensions that comprehensive place-based education typically encompasses. This narrow focus potentially limited the depth of connection students could make between the engineering concepts and their broader sense of place and identity.
It is difficult to isolate which aspects of the student experience contributed most significantly to the observed outcomes. The intervention included multiple potentially impactful elements beyond its place-based nature, including graduate student involvement, hands-on learning, problem-solving activities, and connections to real-world issues. The relative contribution of each of these elements to the observed changes in students' perceptions and aspirations could not be determined within the study design.
The study's analysis may have overemphasized positive findings, particularly in the qualitative data analysis. Although efforts were made to implement a systematic coding process, instances of student disinterest, confusion, or deviation from the topic may have been underrepresented in the analysis. Some students may have provided socially desirable responses knowing they were participating in a special university-led activity, potentially inflating positive reactions.
As engineering educators conducting research on engineering education, the research team may have brought inherent biases to the study design, implementation, and analysis. My enthusiasm for engineering and belief in its value as a career path may have influenced how activities were presented and how student responses were interpreted.
Future studies should consider longitudinal designs to track students' engineering career development over time, providing insights into the sustained impact of place-based activities. Expanding to more diverse rural contexts with more systematic recruitment strategies would enhance generalizability, while developing more robust middle schoolspecific instruments would strengthen measurement validity.
Implementation fidelity could be improved through more standardized protocols and consistent timing of assessments. Future research would benefit from a more comprehensive place-based approach that integrates deeper connections to local community contexts and histories. Additionally, comparative designs that isolate different aspects of the intervention (hands-on components, graduate student involvement, placebased elements) would help identify the most impactful components.
This study demonstrated the significant impact of place-based engineering activities on middle school students' engineering career development through the lens of social cognitive career theory. The integration of hands-on activities with real-time feedback mechanisms proved particularly effective in enhancing students' self-efficacy and interest in engineering. The immediate feedback provided through sensor technology created a powerful learning loop that allowed students to build confidence through mastery experiences while maintaining engagement with engineering concepts.
A key finding was the intervention's success in connecting engineering to students' lived experiences, particularly in agricultural contexts. Students' improved outcome expectations reflected their growing understanding of engineering's relevance to their communities and daily lives. This contextualization of engineering within familiar settings helped students, especially those from farming backgrounds, recognize the practical applications and value of engineering in their communities.
Notably, the intervention showed promising results in addressing gender disparities in engineering education. The elimination of pre-existing gender differences in postintervention suggests that hands-on, place-based activities can create more inclusive learning environments. Students, particularly girls, developed broader perspectives of engineering beyond mechanical tasks, recognizing it as a field centered on creative problem-solving and system improvement.
However, the study also revealed important limitations and areas for future development. While the intervention significantly improved self-efficacy and outcome expectations, its impact on career goals was modest. This finding suggests that while single exposure to engineering activities can spark initial interest, sustained engagement opportunities are necessary to solidify career aspirations, particularly in rural settings where STEM resources may be limited.
These findings have important implications for engineering education and career development programs. They highlight the effectiveness of place-based, hands-on approaches in making engineering more accessible and relevant to rural students. The study also emphasizes the need for continued support and multiple engagement opportunities to transform initial interest into sustained career aspirations. Future initiatives should focus on creating sustained engineering exposure opportunities, particularly in rural schools, and maintaining the connection between engineering concepts and local contexts. For educational practice, these results suggest that incorporating place-based elements and real-time feedback mechanisms in engineering education can significantly enhance student engagement and interests. The success in reducing gender gaps also provides valuable insights for developing more inclusive STEM education approaches. Moving forward, educators and policymakers should consider ways to provide more consistent engineering exposure opportunities, particularly in rural areas, to build upon the initial interest and confidence gained through such interventions. Part 1: please answer the following questions by checking the box next to the response that best applies to you. How would you describe the area where you live: ☐I live in a town. ☐I live on a farm. ☐Other (please specify): _______ What is your gender identity: ☐Male ☐Female ☐Other (please specify): _______ ☐Prefer not to answer. What grade are you in? ☐6 th grade ☐7 th grade ☐8 th grade ☐Other (please specify) Instruction: for each statement, check the box to show how much you agree. Part 2: Strongly Disagree Disagree Agree Strongly Agree 1.I am able to do well in activities that involve engineering. ☐ ☐ ☐ ☐ 2.I plan to design things that make a difference in the world. ☐ ☐ ☐ ☐ 3.If I were an engineer, I would be able to do many different jobs. ☐ ☐ ☐ ☐ 4.I am good at putting things together. ☐ ☐ ☐ ☐ Name: School ID: 5.I have experience doing engineering activities. ☐ ☐ ☐ ☐ 6.I like activities that involve engineering. ☐ ☐ ☐ ☐ 7.I built and made something from my own ideas. ☐ ☐ ☐ ☐ 8.My family would like it if I chose to be an engineer. ☐ ☐ ☐ ☐ 9.I do not know what engineers do. ☐ ☐ ☐ ☐ Part 3: Strongly Disagree Disagree Agree Strongly Agree 10.I plan to be an engineer. ☐ ☐ ☐ ☐ 11.I am interested in knowing about engineering. ☐ ☐ ☐ ☐ 12.I am good at problems that can be solved in many ways. ☐ ☐ ☐ ☐ 13.I plan to use science in my future career. ☐ ☐ ☐ ☐ 14.I know someone who is an engineer. ☐ ☐ ☐ ☐ 15.If I want to be an engineer, my friends would make negative comments. ☐ ☐ ☐ ☐ 16.I like creating new and better ways of doing things. ☐ ☐ ☐ ☐ 17.If I were an engineer, people would look up to me. ☐ ☐ ☐ ☐ 18.I like figuring out how things work. ☐ ☐ ☐ ☐ Part 4: Strongly Disagree Disagree Agree Strongly Agree 19.I am able to complete activities that involve engineering. ☐ ☐ ☐ ☐ 20.I think that having a job in engineering would be fun. ☐ ☐ ☐ ☐ 21.If I were an engineer, I would make good money. ☐ ☐ ☐ ☐ 22.I like using science to better understand my world. ☐ ☐ ☐ ☐ 23.I have figured out how to fix something. ☐ ☐ ☐ ☐ 24.I like using math to solve problems. ☐ ☐ ☐ ☐ 25.I plan to use math in my future career. ☐ ☐ ☐ ☐ 26.I have taken things apart. ☐ ☐ ☐ ☐ Self-efficacy=Q1+Q4+Q12+Q19 Background/context=Q5+Q7+Q9+Q20+Q23+Q26 Outcome expectations=Q3+Q17+Q21 Interests=Q11+Q16+Q18+Q22+Q24+Q6 Goals=Q2+Q10+Q13+Q25 Support and barriers=Q8+Q14+Q15 Part 5: Strongly Disagree Disagree Agree Strongly Agree 27.I found the smart garden activity interesting. ☐ ☐ ☐ ☐ 28.I can use what I learned from the smart garden activity outside of school. ☐ ☐ ☐ ☐ 29.After doing the "smart garden" activity, I am interested in learning more about engineering. ☐ ☐ ☐ ☐ 30.I am interested in classroom activities that teach skills I can use outside of school. ☐ ☐ ☐ ☐ 31.I would like to see more activities at my school that are like things my family and friends do. ☐ ☐ ☐ ☐ 32.The smart garden activity reminds me of things my family and friends do. ☐ ☐ ☐ ☐ 33.My family and friends would be happy to hear about what I learned in the "smart garden" activity. ☐ ☐ ☐ ☐ 34.I can see how what I learn from engineering applies to life. ☐ ☐ ☐ ☐ Part 6: What were the main things you learned from this engineering activity? (There are no right or wrong answers. Feel free to share your thoughts based on your own experience trying the hands-on engineering project.) Multivariate Tests a Effect Value F Hypothesis df Error df Sig. School Pillai's Trace 0.081 0.862 12.000 246.000 0.587 Area Pillai's Trace 0.163 1.814 12.000 246.000 0.046 Gender Pillai's Trace 0.034 .720 b 6.000 122.000 0.634 School * Area Pillai's Trace 0.130 0.936 18.000 372.000 0.535 School * Gender Pillai's Trace 0.098 1.059 12.000 246.000 0.395 Area * Gender Pillai's Trace 0.079 0.844 12.000 246.000 0.605 School * Area * Gender Pillai's Trace 0.094 1.016 12.000 246.000 0.434 a. Design: Intercept + School + Area + Gender + School * Area + School * Gender + Area * Gender + School * Area * Gender b. Exact statistic c. The statistic is an upper bound on F that yields a lower bound on the significance level. Multiple Comparisons Tukey HSD Dependent Variable (I) Area (J) Area Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Change_ SELF EFFICACY Town Farm 0.1077 0.09037 0.460 -0.1066 Other -0.0739 0.16555 0.896 -0.4665 Farm Town -0.1077 0.09037 0.460 -0.3221 Other -0.1816 0.17545 0.556 -0.5977 Other Town 0.0739 0.16555 0.896 -0.3187 Farm 0.1816 0.17545 0.556 -0.2345 Change_ BACKGROUND/ CONTEXT Town Farm 0.0332 0.07928 0.908 -0.1548 Other 0.1044 0.14523 0.753 -0.2400 Farm Town -0.0332 0.07928 0.908 -0.2212 Other 0.0712 0.15392 0.889 -0.2938 Other Town -0.1044 0.14523 0.753 -0.4488 Farm -0.0712 0.15392 0.889 -0.4362 Change_ OUTOCME EXPECTATIONS Town Farm .2442 * 0.09246 0.025 0.0250 Other 0.0362 0.16938 0.975 -0.3654 Farm Town -.2442 * 0.09246 0.025 -0.4635 Other -0.2080 0.17951 0.480 -0.6337 Other Town -0.0362 0.16938 0.975 -0.4379 Farm 0.2080 0.17951 0.480 -0.2177 Change_ INTERESTS Town Farm 0.0222 0.09447 0.970 -0.2018 Other 0.0294 0.17305 0.984 -0.3810 Farm Town -0.0222 0.09447 0.970 -0.2463 Other 0.0071 0.18341 0.999 -0.4278 Other Town -0.0294 0.17305 0.984 -0.4398 Farm -0.0071 0.18341 0.999 -0.4421 Change_GOALS Town Farm -0.0619 0.09111 0.776 -0.2779 Other 0.1817 0.16690 0.523 -0.2141 Farm Town 0.0619 0.09111 0.776 -0.1542 Other 0.2436 0.17689 0.356 -0.1759
pre_back_cont_model <-sem(model = pre_back_cont, data = data, missing = "ML", estimator = "MLR") summary(pre_back_cont_model, fit.measures = T, standardized = T) ``` ```{r} post_back_cont <-" post_back_cont =~ Post_Q5 + Post_Q7 + Post_Q9 + Post_Q20 + Post_Q23 + Post_Q26 " post_back_cont_model <-sem(model = post_back_cont, data = data, missing = "ML", estimator = "MLR") summary(post_back_cont_model, fit.measures = T, standardized = T) ``` ```{r} pre_outcome_expect <-" pre_oe =~ Pre_Q3+ Pre_Q17 + Pre_Q21 " pre_oe_model <-sem(model = pre_outcome_expect, data = data, missing = "ML", estimator = "MLR") summary(pre_oe_model, fit.measures = T, standardized = T) ``` ```{r} post_outcome_expect <-" post_oe =~ Post_Q3+ Post_Q17 + Post_Q21 " post_oe_model <-sem(model = post_outcome_expect, data = data, missing = "ML", estimator = "MLR") summary(post_oe_model, fit.measures = T, standardized = T) ``` ```{r} pre_goals <-" pre_goal =~ Pre_Q2 + Pre_Q10 + Pre_Q13 + Pre_Q25 " ``` ```{r} post_supp_barrier <-" post_supp_barrier =~ NA*Post_Q8 + 1*Post_Q14 + Post_Q15 " data |> select(Post_Q8, Post_Q14, Post_Q15) |> cor() post_supp_barrier_model <-sem(model = post_supp_barrier, data = data, missing = "ML", estimator = "MLR") summary(post_supp_barrier_model, fit.measures = T, standardized = T) ``` ```{r} post_learn_exp <-" learn_exp =~ Post_Q27 + Post_Q28 + Post_Q29 + Post_Q30 + Post_Q31 + Post_Q32 + Post_Q33 + Post_Q34 "