Predictors of Latina/o community college student vocational choice of STEM fields: Testing of the STEM-vocational choice model

This study confirmed appropriate measurement model fit for a theoretical model, the STEM vocational choice (STEM-VC) model. This model identifies exogenous factors that successfully predicted, at a statistically significant level, a student\u27s vocational choice decision to pursue a STEM degree at transfer. The student population examined for this study was Latina/o community college students enrolled at a 2-year college in Florida. Latina/o community college students were selected as a target population, as they represent the largest underrepresented demographic in the United States. Increasing student degree attainment, particularly in STEM fields for this ethnic population, would provide economic opportunity for a dominant demographic of the United States and address STEM degree educational pathway challenges. Latina/o students select predominantly community colleges as their higher education pathway, thus findings from this study will inform research, literature, and practice for community colleges serving the country\u27s Hispanic population. The STEM-VC model is an adaptation of the theoretical framework of social cognitive career theory (SCCT). This study built upon the original SCCT model through the addition of new posited exogenous variables for examination. As such, research conducted was not a direct or complete application of the SCCT model. Exogenous factors introduced for examination included academic achievement, transfer capital, and student validation. These constructs were informed by several resources in the literature including Laanan\u27s (2007) Transfer Student Questionnaire (L-TSQ) and his transfer student capital constructs (Laanan, 2004, Laanan, Starobin, & Eggleston, 2011). The work of Kraemer\u27s (1995) model of transfer intention supported academic achievement. The qualitative work of Laura Rendón (1993) provided support to examine student validation. Finally, recent studies by Lent et al. (2011), Byars-Winston et al. (2010), and Xueli Wang (2013 directly informed the methodology for this study. The analysis for this study was conducted on aggregate data of community college students enrolled spring 2013 at a college in Florida. SSSL Sample College (pseudonym) is a large community college with an average enrollment of 42,000 students, eight branch campuses, and a Latina/o enrollment of 30%. The methodology of this study included a combination of statistical analyses. A review of direct group comparisons between Latina/o students and White students on key factors were examined to understand differences within the aggregate sample data. A logistic regression was conducted to understand the level of prediction vocational goal associated variables predicted STEM vocational choice. Confirmatory techniques, confirmatory factor analysis and structural equation modeling, were conducted to analyze and confirm the model fit for Latina/o students and to confirm specific factors predicted to influence Latina/o community college students\u27 STEM vocational choice at transfer. In addition, a secondary model was fitted to White students as a comparison. Finally, a structural equation modeling multigroup analysis was conducted utilizing a third unified model to explore unique differences between Latina/o and White students

Premedical Students' Orientation toward Education: Challenges for the Pipeline into Medical School

This longitudinal study examined whether premedical students' educational orientation contributed to their chances of completing a STEM degree. Two different orientations were tested; a narrow one grounded by interests in obtaining status and a more expansive orientation grounded by interests in engaging with a broader set of learning goals and communities. The primary source of data came from the Cooperative Institutional Research Program's 2004 Freshman Survey (TFS) and 2008 College Senior Survey (CSS). The main analyses included 613 students who reported interest in pursuing a medical degree and identified physician as their probable career. The findings indicate that students who identified with a more expansive educational orientation were significantly less likely to persist in a STEM major four years after entering college, even after controlling for relevant student background characteristics and college experiences. Implications for the medical profession are discussed.Educatio

Can a five minute, three question survey foretell first-year engineering student performance and retention?

This research paper examines first-year student performance and retention within engineering. A considerable body of literature has reported factors influencing performance and retention, including high school GPA and SAT scores,1,2,3 gender,4 self-efficacy,1,5 social status,2,6,7 hobbies,4 and social integration.6,7 Although these factors can help explain and even partially predict student outcomes, they can be difficult to measure; typical survey instruments are lengthy and can be invasive of student privacy. To address this limitation, the present paper examines whether a much simpler survey can be used to understand student motivations and anticipate student outcomes. The survey was administered to 347 students in an introductory Engineering Graphics and Design course. At the beginning of the first day of class, students were given a three-question, open-ended questionnaire that asked: “In your own words, what do engineers do?”, “Why did you choose engineering?”, and “Was there any particular person or experience that influenced your decision?” Two investigators independently coded the responses, identifying dozens of codes for both motivations for pursuing engineering and understanding of what it is. Five hypotheses derived from Dweck’s mindset theory7 and others8,9 were tested to determine if particular codes were predictive of first-semester GPA or first-year retention in engineering. Codes that were positively and significantly associated with first-semester GPA included: explaining why engineers do engineering or how they do it, stating that engineers create ideas, visions, and theories, stating that engineers use math, science, physics or analysis, and expressing enjoyment of math and science, whereas expressing interest in specific technical applications or suggesting that engineers simplify and make life easier were negatively and significantly related to first-semester GPA. Codes positively and significantly associated with first-year retention in engineering included: stating that engineers use math or that engineers design or test things, expressing enjoyment of math, science, or problem solving, and indicating any influential person who is an engineer. Codes negatively and significantly associated with retention included: citing an extrinsic motivation for pursuing engineering, stating that they were motivated by hearing stories about engineering, and stating that parents or family pushed the student to become an engineer. Although many prior studies have suggested that student self-efficacy is related to retention,1,5 this study found that student interests were more strongly associated with retention. This finding is supported by Dweck’s mindset theory: students with a “growth” mindset (e.g., “I enjoy math”) would be expected to perform better and thus be retained at a higher rate than those with a “fixed” mindset (e.g., “I am good at math”).7 We were surprised that few students mentioned activities expressly designed to stimulate interest in engineering, such as robotics competitions and high school engineering classes. Rather, they cited general interests in math, problem solving, and creativity, as well as family influences, all factors that are challenging for the engineering education community to address. These findings demonstrate that relative to its ease of administration, a five minute survey can indeed help to anticipate student performance and retention. Its minimalism enables easy implementation in an introductory engineering course, where it serves not only as a research tool, but also as a pedagogical aid to help students and teacher discover student perceptions about engineering and customize the curriculum appropriately

STEM PIPELINE FOR STUDENTS WITH DISABILITIES: FROM HIGH SCHOOL TO INTENTIONS TO MAJOR IN STEM

This dissertation examined the science, technology, engineering, and math (STEM) major declaration intentions of students with disabilities as they graduated high school and entered college. I used data from the High School Longitudinal Study of 2009 (HSLS:09) because data collection began in high school and followed students into college, facilitating research focusing on access. Before investigating major declaration intentions, I critiqued the definition and measurement of disability in the HSLS:09, drawing from survey research methods literature. The two subsequent analyses focused on psychological and structural components, respectively. My focus on psychological components drew from Eccles and colleagues’ (1983) expectancy-value framework. This framework tapped into the valuation that students placed on math- and science-related concepts and their expectations to succeed in those fields. Structural components explored in the final analysis drew from human, cultural, and social capital theories. These three theories were at the core of Perna’s (2006) model of college choice, which I adapted to predict majoring in STEM. Both analyses utilized multiple logistic regression to create prediction models. Findings suggested that college-bound students with ADHD have higher odds of intending to pursue STEM majors, compared to students experiencing other forms of disability. Psychological and structural measures were also positively related with odds of pursuing these majors. Implications highlight avenues for enhancing STEM participation for students with disabilities, offer suggestions for improvements to future data collection efforts, and lend guidance for future researchers looking to study disability using the HSLS:09 or other secondary data

What Motivates First-generation College Students to Consider an IT Career? An Integrative Perspective

A career in information technology (IT) presents a viable source of economic advancement for college graduates, but ethnic minority students remain underrepresented in the IT workforce. Such underrepresentation is often exacerbated by their first-generation student (FGS) status. Yet, it remains unclear what leads to FGSs’ IT career choice compared to their counterparts. To address this gap, this study aims to reveal the factors motivating FGSs to consider an IT career and examines the association of influencing factors with personal and demographic factors (gender, race, ethnicity). This qualitative research overlays capital theory and social cognitive career theory to develop an integrated sensitizing framework and draws on individual difference theory in interpreting the findings. Our analysis of the open-ended narrative responses of 193 surveys collected from a minority-serving university revealed 10 key factors influencing IT career choice. A theoretical model incorporating individual differences, generational status, and environmental influences is proposed to advance the discussion of influencing factors in IT career choice toward further theory building and empirical testing. The paper concludes with implications for motivating the IT career choice of the ethnic minority, first-generation student population

The academic value of internships: benefits across disciplines and student backgrounds

While student benefits from internship experience have been frequently documented in research, the emphasis has been on internship effects on employment and career indicators. This work is concerned with effects on academic outcomes and focuses on the robustness of such effects across academic disciplines as well as for different achievement levels of students, student gender, and ethnicity. We present findings from a longitudinal sample (n > 15,000) that covers an extensive range of subjects and disciplines for large Undergraduate cohorts. Main effects and interactions for student background characteristics were investigated showing stable academic benefits for advantaged and disadvantaged students. Further, using ordinal logistic multi-level modelling, we explored the impact on the probability of attaining a higher degree classification for different student scenarios, thus illustrating the practical significance of these internship effects. Effects are less likely to stem from maturation or self-selection. Findings are therefore discussed against a background of motivational approaches suitable to integrate both direct and indirect paths from internship experience to academic outcomes to career indicators

Exploring the self-efficancy of engineering students : findings of a longitudinal study relating to student recruitment, development, retention and success

Published ArticleEngineering is regarded as a scarce and critical skill in South Africa, and the shortage of South African engineers represents a capacity and scare-skills crises for the country. A further problem facing the country is the shortage of woman engineers. Further to encouraging and supporting women entering the field of engineering, a South African university established the Women in Engineering Leadership Association (WELA) in 2011. In 2013, WELA embarked on a longitudinal study to establish the impact of the association on WELA members, and to determine the differences in self-efficacy between male and woman engineering students. The research instrument used for the purpose of the study was an adapted version of the Longitudinal Assessment of Engineering Self-Efficacy as developed by Marra and Bogue from the Assessing Women in Engineering (AWE) project. The findings presented in this article are the results of the first round of questionnaires, which highlighted findings relating to student recruitment, development, retention and success. The results of the first round of the study identified that co-curricular interventions were important to prepare students for the world of work, that international partnerships played a potentially powerful role in developing students, that role models were important especially to female engineering students and that technology was an important tool to recruit students. The aim of this article is to assist engineering faculties to understand possible gender differences and self-efficacy issues that could influence course selection, success, development and retention rates of male and woman engineering students. The study also envisions that other universities concerned with student development, success and retention would be able to duplicate some of the findings described

A Preliminary Study Investigating the Factors Influencing STEM Major Selection by African American Females

The purpose of this study was to investigate the significant factors influencing STEM major selection by African American females. A quantitative research design with a qualitative component was employed. Ex post facto survey research was conducted utilizing an online questionnaire to collect data from participants. African American undergraduate females that had declared a major in STEM comprised the target population for the study. As a basis for comparison, a second data collection ensued. All non-African American undergraduate females majoring in STEM also received the survey instrument to determine if there was a significant difference between factors that influence STEM major selection between the two groups. The Social Cognitive Career Choice Model comprised the conceptual framework for this study. Frequencies and percentages illustrated the demographic characteristics of the sample, as well as the average influence levels of each of the items without regard for level of significance. The researcher conducted an independent samples t-test to compare the mean scores for undergraduate African American females majoring in STEM and non-African American females majoring in STEM on each influential factor on the survey instrument. The researcher coded responses to open-ended questions to generate themes and descriptions. The data showed that African American female respondents were very influenced by the following items: specific interest in the subject, type of work, availability of career opportunities after graduation, parent/guardian, precollege coursework in science, and introductory college courses. In addition, the majority of respondents were very influenced by each of the confidence factors. African American females were overwhelmingly not influenced by aptitude tests. African American females were more influenced than their non-African American female counterparts for the following factors: reputation of the university, college or department, high level of compensation in fields, religious leaders, precollege coursework in mathematics, confidence in mathematics ability, confidence in ability to be successful in mathematics in college, confidence in science ability, and confidence in ability to be successful in science in college. Non-African American females were more influenced than African American females by the precollege coursework in technology and the precollege STEM experience factors. Four themes emerged regarding the items that most influenced success in STEM for African American females: high level of compensation in the field, parents/legal guardians and family members, specific interest in the subject, and confidence in science and math ability. One theme emerged regarding the items that least influenced success in STEM majors for African American females: personal interactions with individuals excluding family members

Investigation of factors related to performance and retention of engineering students.

This study was part of an ongoing effort to improve retention of engineering students at the J. B. Speed School of Engineering at the University of Louisville. The purpose of this study was twofold: (1) to gain a better understanding of the relationship among interest in engineering, performance and first-year retention in engineering, and whether this relationship is different for males and females, and (2) to better understand the relationship among self-control, academic ability and first semester GPA for engineering students. To address the first research question investigating retention, survey responses and data from student records were analyzed using logistic regression. Results of these analyses showed students who indicated they had very high interest in engineering were 43 times more likely to be retained than students who indicated very low interest, and 6 times more likely than a student who indicated they had low to medium interest, given the same GPA. There was not a significant difference in the probability of being retained for students who indicated they had high or very high interest, given the same GPA. Results also showed that a one point increase in GPA increased the likelihood of a student being retained by 4.6 times, given the same level of interest. Based on these results, the Step-outs to Stars engineering retention framework was created. Students were separated into four quadrants based on their level of interest and first semester GPA. The framework can be used as a mechanism to allocate resources targeted to improve engineering retention and to frame future research on engineering retention. Structural equation modeling was used to analyze survey and student data to answer the second research question related to first semester performance of engineering students. In the study academic ability was measured by algebra readiness test scores and ACT math, science, English and reading scores. Self-control was measured by self-reported scores on the Brief Self-Control Scale (Tangney, Baumeister, & Boone, 2004). Results confirmed prior research, which found a significant positive relationship between self-control and academic performance, and a lack of significance between self-control and standardized test scores. These results can be used to strengthen the argument for programs to help improve self-control in K-12 and post-secondary students. The results can also be used to help prospective and current engineering students understand that higher levels of self-control might improve their academic performance in engineering

Student Retention in Community College Engineering and Engineering Technology Programs

An ex-pos-facto non-experimental quantitative study was conducted to examine the academic, financial, and student background factors that influence first-to-second year retention of engineering and engineering technology students at U.S. community colleges. Analysis of the five research questions was done using a chi-square test and multiple logistic regressions. Data were obtained from the National Center for Education Statistics (NCES) Beginning Postsecondary Students 2012/2014 (BPS: 12/14) study. Computations were performed using PowerStats, a web-based statistical tool provided by the NCES, as well as IBM SPSS 25. The sample population consisted of students who entered postsecondary education for the first time in the 2011-2012 academic year and enrolled in an engineering or engineering technology program at a community college. Predictor variables were identified from the dataset and grouped into the categories of academic, financial, and student background variables. These groupings were used as individual models to predict first-to-second year retention of community college engineering and engineering technology students using logistic regressions. Finally, individual variables that displayed statistical significance were then combined and were used as a model to predict student retention with a logistic regression. Results indicate that community college engineering and engineering technology students are not retained at a significantly different rate than non-engineering and engineering technology majors. In addition, the groupings of academic and student background variables did not have a significant impact on the retention of community college engineering and engineering technology students, while the grouping of financial variables did have a significant impact on retention. The variables attendance pattern (academic), TRIO program eligibility criteria and total aid amount (financial), and dependency status (student background) were all statistically significant to their respective predictor models. Finally, the combination of these statistically significant academic, financial, and student background variables were significant predictors of retention