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Drawing from social exchange theory and the relational approach to social exchange relationships, we examine liking and competence judgments as predictors of dyadic viability, a new, complementary, operationalization of team viability. We also consider team-level task interdependence as a moderator of these dyadic relationships. Based on data from dyads nested within project teams, we found that both liking and competence significantly relate to teammates’ dyadic viability. Additionally, task interdependence at the team level significantly moderates the dyadic-level effects of liking and competence on dyadic viability, such that the effect of liking judgments is stronger when team task interdependence is high, and the effect of competence judgments is stronger when team task interdependence is low. We also show that aggregated (across team members) measures of dyadic viability are highly similar to proxies that have been classified as team viability in the past—team satisfaction and cohesion. However, the moderating effect of task interdependence was not found with these team-level measures.

This study focuses on quantitative analyses of international and domestic students pursuing undergraduate degrees at institutions in the USA. Metrics used include representation at start of university studies, representation at graduation and six-year graduation rate. Results are disaggregated by origin (domestic or international), sex (female and male), and major (engineering or non-engineering). Results show that more international students choose engineering than other majors. There are more men than women in engineering and this is more pronounced for international students. International students graduate at higher rates in engineering than domestic students by about 5%. This may reflect a tension between their higher academic qualifications but challenges of adjusting to studying in another country. These insights can be used to support student success.

This paper focuses on how a small sample of people with disabilities perceives the usability of web interfaces designed to meet current standards for people with disabilities. Developing accessible interfaces for online educational tools is necessary in providing an inclusive learning environment. Seeking the perceptions of people with disabilities is a logical design step, but it is even more critical due to the emerging evidence on the limitations of Web Content Accessibility Guidelines (WCAG), the standard currently used in the design process. In this work, we report the perceptions of people with disabilities on an example web interface designed according to WCAG standards. In this work-in-progress, we employed 43 workers from Amazon Mechanical Turk to compare the existing interface for CATME (an educational platform for team formation and peer evaluation) to an interface implemented according to web content accessibility guidelines while giving why they might prefer the features of either interface. The majority of the participants preferred the WCAG-compliant designs, a few participants provided useful reflections on their preference for the original interface. For example, WCAG criteria 1.4.11 require that hovering over a button results in a color change of the button from blue to white. Approximately half of the participants supported this feature, but the rest preferred a different method of highlighting a target button. We infer that WCAG criteria should be followed with continued reflections on user-centered design, particularly given that several WCAG criteria apply on a case-by-case basis according to the overall design of the web interface. In this paper, we perform a detailed analysis on the feedbacks and reasons given by the participants on the implementation of various accessibility criteria and examine their reasoning to understand the rationale of this small sample of people with disabilities in web designs. The input from this study will guide further exploration with a larger sample of participants. We hope that our research findings will provide further insights into the development of accessible educational tools.

This work-in-progress paper builds on research that has found disciplinary differences in culture, demographics at initial enrollment ("starters"), demographics of students migrating later, and various student outcomes for both groups. An earlier longitudinal study using the Multiple-Institution Database for Investigating Longitudinal Development (MIDFIELD) showed no disciplinary stratification of grades in common early engineering courses based on the discipline in which a student matriculates (or first enrolls after a first-year engineering program). In contrast, this study finds that disciplinary differences in introductory courses emerge on the path to graduation. This can only be explained by a bias with respect to introductory course grades of students entering and leaving each major– students with lower grades in introductory courses are more likely to leave or be attracted by some majors than others. Citing prior research regarding cultural differences among the engineering disciplines, the authors propose that disciplines establish and enforce cultural norms regarding student performance in these introductory courses.

This work-in-progress presents an overview of current research with the Multiple-Institution Database for Investigating Longitudinal Development (MIDFIELD) and strategies for inviting and supporting other researchers to use this database in their own research. MIDFIELD is a resource for the study of students that includes longitudinal, de-identified, whole population data for multiple institutions. This enables researchers to examine student characteristics (such as race/ethnicity, sex, socioeconomic indicators) and curricular pathways (including coursework) by institution and over time. Because the dataset contains records of all students matriculating over a period of time, researchers can study students across all disciplines, not just engineering.The MIDFIELD team aims to educate the broader research community, expand the network of researchers capable of conducting this research, and share innovative research methods in addition to the actual data. We have offered workshops at several conferences as well as the MIDFIELD Institute. The inaugural MIDFIELD Institute brought together researchers from across the USA for two days in 2019. In an evaluation at the end of the Institute, participants were pleased with the Institute and rated it highly. There was high agreement about the value of the content and delivery. Most participants reported that the instituted lived up to their expectations and was a good way to learn about how to use MIDFIELD data. Areas of improvement suggested by participants included making it longer and more challenging. Several of the MIDFIELD Institute attendees are presenting works-in-progress together at FIE 2020 to provide attendees with a range of examples of research that is possible with MIDFIELD.

This Work-In-Progress (WIP) research paper summarizes and compares institutional demographic and financial trends among five Southeastern engineering institutions through 2000-2015. This WIP is part of a larger study investigating the effect of institutional characteristics on first- to second- year retention among undergraduate engineering students. Using two databases, we compared institution demographics, number of degrees granted, and institution expenditure both at the university level and engineering college level. The authors will use the results of this WIP to further investigate the effects of these variables on engineering students’ academic performance by using these results and applying additional quantitative models to a longitudinal and multi-institutional database.

Emergent states are team-level attributes that reflect team members’ collective attitudes, values, cognitions, and motivations and influence team effectiveness. When measuring emergent states (e.g., cohesion, conflict, satisfaction), researchers frequently collect ratings from individual group members and aggregate them to the team level. After aggregating to the team level, researchers typically focus on mean differences across teams and ignore variability within teams. Rather than focusing on the mean level of emergent states, this study draws on recent advances in multilevel theory and describes an approach for examining the specific patterns of dispersion (i.e., disagreement) across five emergent states. Our findings suggest that teams reliably demonstrate different patterns of rating dispersion that are consistent with existing theoretical frameworks and typologies of dispersion, yet have not previously been empirically demonstrated. We also present evidence that the different patterns of dispersion in emergent states are significantly related to key team outcomes, even after controlling for the mean levels of those emergent states. These findings underscore the importance of exploring additional forms of team-level constructs and highlight ways of extending our understanding of group-level phenomena.

This article describes the data related to co-enrollment density (CD), a new network clustering index, that can predict persistence and graduation. The data hold the raw results and charts obtained with the algorithm for CD introduced in ``Co-Enrollment Density Predicts Engineering Students' Persistence and Graduation: College Networks and Logistic Regression Analysis.'' There are data for eight institutions that show CD as a predictor for graduation at four years, graduation at six years, and ever graduated. The files were processed using R to estimate CD at one, two, three, and four years. Logistic regression models, receiver operating characteristic curves, specificity, sensitivity, and cut-off points were estimated for each model. The R code to reproduce the metanalysis for the summary data is included. The displays for the logistic regression models, receiver operating characteristic curves, density curves for classes, models, and parameters are included.

This study extends research on peer evaluations by examining the impact of Frame-of-Reference rater training on team members’ understanding of a specific model of teamwork, as well as on the qualit...

College retention is a concern for educational institutions and researchers. This concern is particularly acute in engineering for reasons including workforce shortages, economic competitiveness, social justice, and socioeconomic equity. This study presents the evaluation of co-enrollment density (CeD) for engineering students at eight medium and large American public universities over 24 years. CeD is a novel metric estimated using enrollment records that may predict retention in 4-year bachelor of science programs in engineering. Graduation and persistence were fitted to CeD with logistic regression. Students in denser co-enrollment clusters—high CeD—tend to graduate more than their classmates in less dense neighborhoods—low CeD. The regression models predict graduation with odds ratio intervals 95 % CIs [3.24, 4.81] and area under the receiver operating curve [0.76, 0.80]. CeD is more sensitive to students who do not persist, particularly after the first year, so CeD’s cut-off points may be indicators for dropouts' risk.

The IEEE Education Society, the IEEE Computer Society, and the American Society for Engineering Education Educational Research and Methods Division (ASEE ERM) sponsored the 46th Frontiers in Education (FIE) Conference in Erie, PA, USA, held October 12–15, 2016. James Sluss, President of the Society, Russell Meier, President of the ASEE ECE Division, and Elizabeth Eschenbach, Chair, FIE Steering Committee, presented awards sponsored by HP Enterprise, the FIE Conference, the Education Society of IEEE, and the ECE Division of ASEE to the 2016 recipients of these awards.

The use of science and engineering skills to address the novel challenges of modern society through innovative solutions is regarded as an essential strategy around the world. Many of the studies on innovators, however, are not specific to engineers. To address this gap, this study explored engineers’ views of innovation and the attributes of engineers who create, develop, and implement innovations through in-depth interviews with expert engineering innovators. The study was set in an interpretivist framework and developed a socially constructed description of engineering innovativeness. Forty-five US-based expert engineering innovators were interviewed; these interviews were coded using a grounded-theory approach until categorical saturation was achieved. The results include the identification of 20 characteristics of engineering innovativeness, as well as an ‘engineer’s definition’ of an innovation and several general descriptors of ‘non-innovative’ engineers. The significance of this study come...

This full paper aims to investigate students’ academic outcomes based on their rurality classification. Despite the large number of studies investigating students’ demographics across engineering disciplines, few studies consider how rurality - coming from a rural area - influences students’ academic performance. This study also aims to study the variety of demographic variables and pre-college characteristics including home zip code at matriculation as a proxy for point of origin among engineering undergraduate students. Moreover, this study investigates the effects of these characteristics, on undergraduate students’ likelihood of graduation and academic success across seven institutions.This is a quantitative study using a longitudinal data from multiple institutions. Students’ home zip code is used to classify students to four rurality classifications using the urban-centric locale codes defined by U.S. Census Bureau. The study applies a logit regression on a dataset that includes 30,763 students to investigate the likelihood of graduation of engineering undergraduate students from seven institution. The study then applies an ordinary least squares linear regression model to a subset of 21,311 students who managed to graduate from the same institutions to investigate students’ graduation GPA as a proxy of student success based on students’ rurality classification. This study also analysis the results disaggregating students based on their institutions. Although the result show that there are different patterns among institutions, but generally, rural students seem at a disadvantage for both GPA and graduation at most institutions.

This workshop, similar to our 2018 FIE workshop but revised to reflect participant feedback, introduces data and tools for investigating undergraduate persistence metrics using R. Student record data are from MIDFIELD, a database of registrars’ data from US institutions. The stratified data sample includes demographic, term, course, and degree information for 98,000 students from 1987 to 2016. The midfieldr package provides functions for determining persistence metrics such as graduation rates and for grouping findings by institution, program, sex, and race/ethnicity. The goal of the workshop is to share our data, methods, and metrics for intersectional research in student persistence. The workshop is designed for R beginners.

Background and purpose: Billions of dollars are spent helping new employees transition into the workforce every year. A type of onboarding process that is popular in the engineering practice is the rotational onboarding program where new engineering graduates spend time rotating through different departments within a company to gain knowledge of the departments and get acclimated to the company business. This practice is becoming increasingly popular as more than 50% of employers with more than 5000 employees have rotational onboarding training programs in the USA. Because studies on rotational onboarding programs in engineering practice are scarce, this paper aims to first explore current programs in the engineering sector by conducting a document analysis review of such programs to understand key factors and requirements for each program and also to understand the structure of the various companies and their business sector.

In this research-to-practice paper we provide the context for our research and review our process for conducting our engineering innovativeness factor analysis using Bayesian statistical approaches rather than using the traditional principal components analysis (PCA). Our purpose in discussing this analysis approach is to demonstrate that we found the Bayesian analysis approach to be a superior way to refine factors and reduce survey items when developing our instrument for assessment of engineering innovativeness characteristics.

How successful are undergraduate students who begin in another major and migrate into engineering disciplines after matriculation? In this work in progress, we present quantitative data on outcomes for engineering migrators disaggregated by discipline, race/ethnicity, and sex. The study includes over 73,000 engineering students from nine U.S. universities, including first-time-in-college and transfer students who ever majored in the most common engineering disciplines: Chemical, Civil, Electrical, Industrial, and Mechanical Engineering. Adopting an ecosystem mindset, we have developed metrics including the graduation rate of migrators and “migration yield” to uncover dynamic information, not afforded by the conventional pipeline model, about the successes of students who migrate among the top five engineering disciplines. Our data show that the graduation rates of migrators are typically higher than those of starters for all engineering majors studied. Migration yield varies by race/ethnicity-sex as well as discipline. Migration yield for Chemical, Electrical, Industrial and Mechanical Engineering shows a sex-based effect, whereas Civil shows a race/ethnicity-based effect.

This research-to-practice paper describes the results of 12 experiments using different combinations of peer evaluation trainings on the quality of peer evaluations of teamwork skills provided by first year engineering (FYE) students in an introduction to engineering course in a large Midwestern University. Our research question was: Which combination of peer evaluation trainings most significantly improves the quality of peer evaluations in this FYE course as measured by using data collected by the CATME (Comprehensive Assessment of Team Member Effectiveness) system’s peer evaluation exercise? The five training interventions combined in the 12 experiments were Frame-of-reference teamwork training, Video-based teamwork training materials, Rater practice, Rater accuracy training and In-class teamwork reflections. The training intervention combinations that included Video based training and Rater accuracy training had the most significant effects on improving rater and target variances or the quality of peer evaluations.

This work-in-progress research-to-practice analysis examines whether using a more game-like practice tool for learning a peer rating schema significantly improves the quality of peer evaluations in a team-based course. Peer evaluations in our study are defined as the peer evaluations of teamwork behavior as measured by the CATME peer evaluation system. Data samples in this study include students in teams from introduction to engineering courses in a large Midwest university’s engineering program who used the previous version and the current more game-like version of the rater practice tool. We found significant differences in the mean ratings of our intervention group and in the distribution of variances versus our control group in the first peer evaluation of the semester.

student’s ability to work in a team setting is vital to their college career as well as their work life and therefore it is often a significant factor in a corporate hiring process. Recognizing this need, a number of U.S. undergraduate programs as well as many K-12 instructors, particularly the ones in Project Lead The Way (PLTW) courses use team or project based courses in their curricula. Hundred’s of these programs, including K-12, also use a teamwork formation and assessment system, CATME (Comprehensive Assessment of Team Member Effectiveness), that asks students to assess the teamwork behavior of their peers. Much work has been done in the use of teams in K-12 but not on the analysis of teamwork behavior and the effective use of teams. The goal of our research was to determine whether or not K-12 students behave similarly to First Year Engineering (FYE) students, in particular, when they rate their peers as well as when they are being rated - is the quality of peer evaluations performed by K-12 students similar to the quality of peer evaluations performed by First Year Engineering (FYE) students?

This study aims to provide a nuanced profile of civil engineering students’ out-of-class engagement by exploring the supporting incentives and barriers to involvement and the related outcom...

Electrical Engineering (EE) and Computer Engineering (CpE) programs have similar curricula, but different demographics and student outcomes. This paper extends earlier longitudinal studies to a larger and more diverse dataset with 90 $\,$ 000 first-time-in-college and 26 $\,$ 000 transfer students who majored in engineering at USA institutions, including students who started in first-year engineering programs, those switching majors, and those transferring from other institutions. Black men and women and Asian men in engineering are strongly attracted to EE when they start in college. Black students and Asian and Hispanic men are attracted to CpE more than other engineering disciplines, but at lower rates than EE. Asian students have the highest graduation rates in EE. EE students are much more likely to graduate than CpE students. Compared to other engineering disciplines, CpE graduation rates are low for women of all races/ethnicities and Black men. Both EE and CpE lose many of those starting the programs, but switchers and transfers compensate for some of the loss. Considering Asian students and White men, switching to EE accounts for the high attrition rate from CpE, but attrition in other populations cannot be explained so easily. Trajectories of student enrollment differ by race/ethnicity. The approach used here could serve as a model for other fields studying their own demographic distributions.

Management researchers often use consensus-based composition models to examine the antecedents and effects of higher-level constructs. Typically, researchers present three indices, rwg, ICC(1), and ICC(2), to demonstrate agreement and consistency among lower-level units when justifying aggregation. Nevertheless, researchers debate what values for these indices are sufficient. This study examines the distributional characteristics of ICCs and rwg values from three sources: the multilevel literature, a large multinational sample of student teams, and a large sample of randomly generated “pseudo teams.” Our results support existing cutoff criteria for ICCs but suggest that generally accepted values for rwg may, under certain circumstances, reflect pseudo-agreement (i.e., agreement observed among two raters not attributable to the same target). Thus, when there is minimal between-group variance (i.e., low ICCs), it is difficult to determine whether high rwg values reflect agreement or pseudo-agreement. Based on these findings, we provide recommendations to help researchers interpret aggregation indices.

This longitudinal multi-institution study examines student outcomes and demographics in aerospace engineering in the United States over the period of 1987 to 2010. This large sample allows adoption of an intersectional framework to study race/ethnicity and gender together. In this paper, the demographics of students who choose aerospace engineering, their six-year graduation rates, trajectories of students entering and leaving aerospace engineering, and the “stickiness” of the discipline are examined. Hispanic men and women starting in engineering choose aerospace engineering at the highest rates (13.3 and 12.0%, respectively). Aerospace engineering graduation rates lag other disciplines, at best, by nine percentage points among Hispanic females and, at worst, by 24 percentage points among Black females. Retention in aerospace engineering is low for all students, but it is particularly so for Black men and women (both less than 12%). The result is an average of one Black woman graduate per program every 1...

Throughout Europe and the USA, Electrical Engineering (EE) suffers from an underrepresentation of women even when compared with most other engineering disciplines. This study uses a Varge multi-institution longitudinal dataset, the Multiple-Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD) to examine engineering student outcomes in the USA. This work considers undergraduate student outcomes for women and men in EE and compares them to those in the other four largest engineering disciphnes in the USA: Chemical (ChE), Civil (CVE)? Mechanical (MCE), and Industrial (ISE) Engineering. This research focuses on over 94,000 men and 24,000 women who ever enrolled in one of these five disciplines. This includes first-time-in-college as well as transfer students. Analysis shows that EE has fewer women than men at enrollment and graduation and the percentage of women is comparable to MCE but lower than the other engineering disciplines. Students graduate in EE in six years at lower rates than ISE, CVE, and MCE and comparable rates to ChE. Women in EE graduate at slightly higher rates than men. Stickiness is the number of students who graduate in a major divided by the number who ever declared that major. EE has the lowest stickiness of the disciplines studied. In comparing the net population change of students from start to graduation, EE loses about 22»/. of the women and men. Only ChE has higher percentage losses. MCE has comparable losses, CVE loses about 10% of its student population and ISE gains students from start to graduation. This work contributes to the literature by examining engineering students disaggregated by gender, discipline, and entry point. This study can inform studies in other countries.

Expanding access to engineering for underrepresented groups has by and large focused on ethnicity/race and gender, with little understanding of socioeconomic disadvantages. In this study, we use economic, human, and cultural capital theories to frame and then describe access to undergraduate engineering degree programs and bachelor’s degrees. Using individual student-level data from 10 universities from the Multiple-Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD) and aggregate school-level data (i.e., free-lunch status) from the Common Core of Data between 1994 and 2003, we first describe students who enter engineering programs by peer economic status (PES) with attention to gender, ethnicity/race, and SAT Math score. Second, a subset of the data is analyzed to describe access to bachelor’s degrees in engineering by PES using graduation rates. The findings show an increase in access to engineering degree programs by disadvantaged students, but that access to engineering bachelor’s degrees may be constrained, and especially for underrepresented ethnic/racial groups. The data highlight variable PES differences that accrue in engineering at entry and upon graduation (6 years later) across ethnic/racial groups; these differences have implications for broadening participation. Recommendations for future research and improving engineering access at the secondary and postsecondary levels are discussed.

Using a dataset from universities in the U.S. that includes over 90,000 first-time-in-college students and over 26,000 transfer students who majored in engineering, this work describes the demographics and outcomes for students starting in and transferring into Mechanical Engineering (ME). This aims to inform the decision making of faculty, department heads, and deans. Although men consistently outnumber women in ME, the rates of matriculation and six-year graduation vary by race/ethnicity and gender. Retention is higher in ME than in the aggregate of all engineering majors for Asian, White, and Black students, but not for Hispanic students. While about half of ME starters leave, most are replaced by switchers and transfers. Black males are noticeably absent from this ‘replacement’ population. Black males are also the least likely to stick with ME through graduation. Asian females are the most likely to graduate in ME. Pathways of ME starters and ME graduates are illustrated. Nearly half of all ME graduates started somewhere other than ME.

Instructors often incorporate self- and peer evaluations when they use teamwork in their classes, which is common in management education. However, the process is often time consuming and frequentl...

The Multiple Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD) is expanding to include 113 institutions and is being redesigned and archived to be more accessible to researchers. This special session will describe how researchers can better use or gain access to MIDFIELD. At the conclusion of the session participants should be able to: describe MIDFIELD including common data elements, discuss how new variables can be derived from MIDFIELD, understand what is necessary to access the data on the Interuniversity Consortium for Political and Social Research, and define quantitative and qualitative data types and structures and outline research questions and methods of personal interest to them.

An engineer's ability to work in teams is critical to their engineering career and often a significant factor in a corporate hiring process. Recognizing this need, and motivated to demonstrate outcomes required for accreditation by ABET, most U.S. undergraduate engineering programs include team-based courses in their curricula. Hundreds of these engineering programs use the CATME application (the Comprehensive Assessment of Team-Member Effectiveness), which asks students to assess the teamwork behaviors of their peers. Our goal was to determine how effectively engineering students rated their peers and whether a proven method for improving the use of a rating schema, Frame of Reference training, would improve engineering students' peer rating behavior. We discovered that engineering students do not materially change their peer ratings based on their teaming experiences and we verified that repeated Frame of Reference trainings have a significant impact on peer rating dispersion and potentially the quality of engineering students' peer ratings.

Cooperative education (co-op) is a program in which participating students typically alternate between full-time study and paid full-time employment. Choosing to participate in a co-op program usually delays the student’s graduation by up to one year, but may increase the student’s job market prospects. This paper attempts to estimate the response of co-op participation to the engineering field-specific average wage for recent graduates. A relationship between wage growth, student ability, and co-op participation is investigated. Predictive modeling was performed using data obtained from the Multiple-Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD), as well as the American Association of Engineering Societies (AAES) Annual Report of Engineering Salaries. Semistructured interviews were conducted with industry co-op representatives to address supply and demand related questions. The authors found that a $10,000 real increase in the average initial wage causes a ...

Background Industrial engineering (IE) draws in and graduates women at among the highest rates compared with most engineering majors in the U.S. Popular stereotypes suggest this is because IE is “easier” than other engineering majors Purpose (Hypothesis) This research interrogates prevailing assumptions about industrial engineering to explore why undergraduate women are drawn to industrial engineering over other engineering majors. Design/Method Our mixed method approach used three sources of data. Quantitative analyses of a large, longitudinal dataset allowed us to draw empirical generalizations about academic performance, attraction to, and persistence within industrial engineering among men and women. We triangulated this with qualitative focus group data among women majoring in IE. Finally, we used content analysis of university IE Web sites to understand context and discourse. Results In our dataset, industrial engineering is the only engineering major that gains women and men from the third semester through six-year graduation and among all race-gender combinations (except Black men). Women in focus groups reveal that they are drawn to IE for a myriad of social factors including: warmth, flexibility, a sense it is more feminine, and career opportunities, among others. Content analysis of Web sites reveals that IE emphasizes collegiality and leadership opportunities as intrinsic to the discipline. Conclusions Using a social capital framework, we showed that the context of IE, including prevailing norms and possibilities for networking, promotes ideologies of success that lead to greater attraction to and persistence within the major.

Electrical engineering (EE) is one of the largest engineering disciplines. Computer engineering (CpE) has a similar curriculum, but different demographics and student outcomes. Using a dataset from universities in the U.S. that includes over 70 000 students who majored in engineering, this paper describes the out comes for students matriculating in and migrating into EE and CpE so as to inform the decision making of faculty, department heads, and deans. Although men consistently outnumber women in EE and CpE, the rates of matriculation and six-year graduation vary by race and gender. EE is the most popular choice for Asian and Black students (males and females) at matriculation, but while Asians graduate at high rates, Blacks (particularly males) are not retained. Retention is higher in EE than in CpE despite the similarity of the curricula. Graduation rates are lower than expected for women of all races in CpE and for Hispanic women in EE. By the third semester, some students of all races and genders have left their matriculation major, but others have migrated in from other majors, compensating for some of this loss. CpE students are more likely than EE students to switch to another major. Trajectories of EE and CpE students are racialized and distinct. CpE loses more students and attracts fewer students than EE. These findings il lustrate the importance of disaggregating by engineering major as well as race and gender to improve recruitment and retention overall.

Using undergraduate student records from six large public universities from 1990 to 2003, the authors analyze the characteristics and performance of students by major in two economics courses: Principles of Microeconomics and Intermediate Microeconomics. This article documents important differences across students by major in the principles course and compares these students to those who graduate with a major in economics. The data indicate that about two thirds of students who graduate with a major in economics declared their major sometime after completing the Principles of Microeconomics course. The article documents differences in characteristics and performance for economics graduates who started as engineering, math, or physics majors as compared to business or economics majors. The authors also examine whether starting in one of the more math-intensive majors of engineering, math, or physics improves student performance in intermediate microeconomics if performance in the principles course was good.

Background Concern for workforce needs, social justice, and the diversification of the engineering profession make it critical to understand how different metrics may overestimate or underestimate the success of various race-gender populations in engineering. Purpose (Hypothesis) While earlier work found that women in nearly all racial groups persist to the eighth semester at rates comparable to men, results vary in studies that use other measures of success, providing an incentive to compare multiple measures of success in the same population. Design/Method The eight-semester persistence and six-year graduation rates are compared for various race-gender populations using a longitudinal, comprehensive dataset of more than 75,000 students matriculating in engineering at nine universities from 1988–1998. Results Gender differences in persistence of Asian, Black, Hispanic, Native American, and White students are far outweighed by institutional differences. Racial differences are more pronounced, however, revealing some patterns that transcend institutional differences. Conclusion Our work demonstrates that trajectories of persistence are non-linear, gendered, and racialized, and further that higher education has developed the way in which persistence is studied based on the behavior of the majority, specifically the White, male population. Even if institutions were to treat all students equally, the outcomes will not necessarily be the same because various populations respond differently to the same conditions. Using eight-semester persistence may result in a “systematic majority measurement bias.” Therefore, multiple measures may be needed to describe outcomes in diverse populations.

Background As presented by Willet and Singer (1991), survival analysis can sensitively reveal rich information about when students leave their majors. Although survival analysis has been used to investigate student and faculty retention, it has not been applied to undergraduate engineering student retention. Purpose (Hypothesis) The impact of cohort, gender, ethnicity, and SAT math and verbal scores on the loss rate of undergraduate engineering students was investigated to answer the questions: Does the profile of risk of dropout differ among groups with different backgrounds? When are students most likely to leave engineering? Which SAT scores better predict the risk of dropout? Design/Method Using a large longitudinal database that includes 100,179 engineering students from nine universities and spans 19 years, nonparametric survival analysis was adopted to obtain nonparametric estimates of survival and associated hazard functions, and complete rank tests for the association of variables. Results There are significant differences for early semesters: White or female students tend to leave engineering earlier than other populations. Engineering students leave engineering during the third semester the most, although students who have an SAT math score less than 550 tend to leave engineering during the second semester. SAT math score better predicts the risk of dropout than SAT verbal score. Conclusion The results of this study support using survival analysis to better understand factors that determine student success since student retention is a dynamic problem. Survival analysis allows characteristics such as risk to be evaluated by semester, giving insight to when interventions might be most effective.

Background Self-concept can influence how an individual learns, but is often overlooked when assessing student learning in engineering. Purpose (Hypothesis) To validate an instrument designed to measure individuals' self-concepts toward engineering design tasks, three research questions were investigated: (a) how well the items in the instrument represent the engineering design process in eliciting the task-specific self-concepts of self-efficacy, motivation, outcome expectancy, and anxiety, (b) how well the instrument predicts differences in the self-efficacy held by individuals with a range of engineering experiences, and (c) how well the responses to the instrument align with the relationships conceptualized in self-efficacy theory. Design/Method A 36-item online instrument was developed and administered to 202 respondents. Three types of validity evidence were obtained for (a) representativeness of multi-step engineering design processes in eliciting self-efficacy, (b) the instrument's ability to differentiate groups of individuals with different levels of engineering experience, and (c) relationships between self-efficacy, motivation, outcome expectancy, and anxiety as predicted by self-efficacy theory. Results Results indicate that the instrument can reliably identify individuals' engineering design self-efficacy (α = 0.967), motivation (α = 0.955), outcome expectancy (α = 0.967), and anxiety (α = 0.940). One-way ANOVA identified statistical differences in self-efficacy between high, intermediate, and low experience groups at the ρ < 0.05 level. Self-efficacy was also shown to be correlated to motivation (0.779), outcome expectancy (0.919), and anxiety (—0.593) at the ρ < 0.01 level. Conclusions The study showed that the instrument was capable of identifying individuals' self-concepts specific to the engineering design tasks.

BACKGROUND Applied ethics plays a critical role in engineering, health, business, and law. Applied ethics is currently a required component of the pre-practice education for these professions, yet the literature suggests that challenges remain in how we define, instruct, and assess professions-based ethics education. PURPOSE Based on the on going debate associated with the instruction and assessment of applied engineering ethics, an exploratory investigation was performed to determine what could be learned by looking across professions.

One major challenge in using group projects to assess student learning is accounting for the differences of contribution among group members so that the mark assigned to each individual actually reflects their performance. This research addresses the validity of grading group projects by evaluating different methods that derive individualized scores from group work. Both Monte Carlo simulation and real test data analyses were conducted. The four investigated methods are the within-group adjustment method, the partial adjustment method, the between-group adjustment method, and the expected contribution adjustment method. For all methods, a weighting factor is computed based on the peer and self ratings of contributions to the group project by group members. This study finds that individual differences have to be taken into account if group grades are going to be assigned and utilized for evaluating individual performance at all. Adjusting contribution differences based on peer and self ratings could be an ...

Interest in increasing the number of engineering graduates in the United States and promoting gender equality and diversifi cation of the profession has encouraged considerable research on women and minorities in engineering programs. Drawing on a framework of intersectionality theory, this study recognizes that women of different ethnic backgrounds warrant disaggregated analysis because they do not necessarily share a common experience in engineering education. Using a longitudinal, comprehensive dataset of more than 79,000 students who matriculated in engineering at nine universities, this research examines the question: How does the persistence of engineering students (measured as enrollment to the eighth semester) vary by disaggregated combinations of gender and race/ethnicity? Findings reveal that for Asian, Black, Hispanic, Native American, and White students, women who matriculate in engineering are most likely to persist in engineering compared to other eighth-semester destinations and, except for Native Americans, do so at rates comparable to those of men. Thus, contrary to considerable popular opinion that there is a gender gap in persistence, the low representation of women in the later years of engineering programs is primarily a refl ection of their low representation at matriculation.

Records from the Multiple-Institution Database for Investigating Engineering Longitudinal Development indicate that engineering students are typical of students in other majors with respect to: persistence in major; persistence by gender and ethnicity; racial/ethnic distribution; and grade distribution. Data from the National Survey of Student Engagement show that this similarity extends to engagement outcomes including course challenge, faculty interaction, satisfaction with institution, and overall satisfaction. Engineering differs from other majors most notably by a dearth of female students and a low rate of migration into the major. Noting the similarity of students of engineering and other majors with respect to persistence and engagement, we propose that engagement is a precursor to persistence. We explore this hypothesis using data from the Academic Pathways Study of the Center for the Advancement of Engineering Education. Further exploration reveals that although persistence and engagement do not vary as much as expected by discipline, there is significant institutional variation, and we assert a need to address persistence and engagement at the institutional level and throughout higher education. Finally, our findings highlight the potential of making the study of engineering more attractive to qualified students. Our findings suggest that a two-pronged approach holds the greatest potential for increasing the number of students graduating with engineering degrees: identify programming that retains the students who come to college committed to an engineering major, and develop programming and policies that allow other students to migrate in. There is already considerable discourse on persistence, so our findings suggest that more research focus is needed on the pathways into engineering, including pathways from other majors.