Understanding Diverse Pathways: Disciplinary Trajectories of Engineering Students: Year 3- NSF REE Grant 1129383

Engineering as a whole continues to suffer from a low participation of women of all races andBlack, Hispanic, and Native American men. To diversify pathways for students to and throughengineering and to improve student success, we must first know how to measure success andprovide baseline data describing the current situation for all students. Our previous work hasshown that persistence or success varies by race and gender, and how we measure persistencematters in understanding this variation. Once women matriculate in engineering, they graduate insix-years at the same or better rates than their male counterparts of all races. This finding,however, shows considerable variation by engineering subdiscipline. Aggregating allengineering disciplines tends to produce a skewed view of the field given the large numbers ofstudents in Electrical and Mechanical engineering. Disaggregation by race and gender isimperative because not all populations respond the same way to similar conditions. Building onearlier findings that trajectories of engineering persistence are non-linear, gendered, andracialized as a whole and for electrical and computer engineering, we are extending theseanalyses to other engineering disciplines. Using an existing dataset that includes wholepopulation data from eleven institutions throughout the U.S. spanning more than 20 years, wehave an unprecedented opportunity to conduct analyses of student persistence disaggregated byrace, gender, and engineering discipline. This gives us a unique opportunity to paint a morecomplete picture of the current situation for students in engineering and to identify successes andareas of concern. Our research question is How do the trajectories of engineering students indifferent engineering disciplines vary by race and gender? Trajectories are measured atmatriculation, four years later, and six-year graduation for matriculants to the disciplines as wellas all students in the major, including first-time-in-college (FTIC) and transfer students. Theimpact of first-year engineering (FYE) programs is also considered. We focus on the mostpopular disciplines of engineering: Chemical, Civil, Electrical, Mechanical, and Industrial. Inaddition, we have considered Aerospace Engineering given its similarity in curriculum toMechanical and Computer Engineering given its similar curriculum to Electrical. We have begunto work on comparisons of the five most popular engineering disciplines

Board # 114 : Progress toward Optimizing Student Team Skill Development using Evidence-Based Strategies

The broad goal of this work is to study the effectiveness of various teamwork training interventions. This research requires the use of a common model of teamwork and a system for training, collecting ratings data, and providing feedback. We will leverage the NSF’s prior investment in the CATME system, which meets the research criteria and automates some of the data collection and feedback, which will aid in executing the research protocol consistently. Seven empirical studies will determine the effect sizes of training, practice in teams, practice rating, and feedback interventions on cognitive development (improvement of team skills) and metacognitive development (improvement of self- and peer-evaluation skills). Outcomes. We focus both on cognitive skills related to team-member effectiveness and on metacognitive skills that enable competent self- and peer-evaluation of team members’ effectiveness. An intermediate knowledge-level outcome affects both—developing an improved cognitive model of teamwork. Students must learn what skills are necessary for effective teamwork to be able to develop and evaluate them. Strategies. To achieve these outcomes, we have several strategies. Frame-of-reference training, which is well-established and empirically supported, will align students’ cognitive model of teamwork with ours by teaching students the ways team members can contribute effectively to teams in the five key areas summarized earlier. Experience working in teams and evaluating teamwork will improve team skills and self- and peer-evaluation skills. Experience in teams increases as students work on multiple teams. Rating practice will be accomplished by showing students descriptions or videotapes of fictitious team members and having them rate the contributions these fictitious team members make, in addition to rating themselves and their real teammates following work in teams. Finally, we will examine how the degree to which and manner in which feedback on team skills is provided affect student outcomes. This presentation (Executive Summary and Poster) will provide a valuable update on this project, share various lessons for classroom practice, and provide guidance to other faculty who seek to use CATME in their research

Characterizing and Modeling the Experience of Transfer Students in Engineering—Progress on NSF Award 0969474

Characterizing and Modeling the Experience of Transfer Students in Engineering— Progress on NSF Award 0969474Quantitative analysis of MIDFIELD databaseOur analysis used records for 94,732 undergraduate students from the Multiple-InstitutionDatabase for Investigating Engineering Longitudinal Development (MIDFIELD). MIDFIELDcomprises a census of undergraduate students who attended 11 public institutions between 1988and 2008. MIDFIELD institutions represent public universities that educate large numbers ofengineering students.From the 977,950 records available, we restricted our sample to those who (1) were domesticstudents (927,350), (2) were in the data set early enough for us to observe the possibility ofgraduation within six years (677,691), and (3) declared a major in engineering or otherwiseexpressed the intent to study engineering in the fifth semester of their programs (94,732). Fortransfer students, we estimated placement using transfer hours, assuming that 15 credit hoursequals one semester; we also used the fifth semester as the reference point to capture mosttransfer students at the point of matriculation to ensure a valid comparison of transfers to non-transfers. This approach resulted in a sample of 21,542 transfer and 73,190 non-transferengineering students included in this analysis.Semi-structured interviewsCampus representatives at two MIDFIELD institutions sent an invitation to all engineeringstudents who had transferred into the institution in the two semesters preceding the semester ofthe interview. Interested students completed a survey to provide demographic and schedulinginformation. Participants were chosen from six engineering majors - civil, chemical, computer,electrical, industrial, and mechanical - and were diverse with respect to gender and ethnicity.Selected students were interviewed in Fall 2011 and in Spring 2012.We used a semi-structured interview protocol to learn more about student experiences with thetransfer process. We used a constant comparative coding method, whereby emerging conceptswere constantly compared to data that had already been coded.Overview of Progress Identifying and Describing the Entry Points into Engineering Transfer Pathways: A preliminary study relied on 52 of the 86 students who were interviewed across five campuses to understand their reasons for choosing engineering as a field of studies and the transfer pathway to enter the field. Studying the Motivations and Experiences of Older Transfer Students in Engineering: Of the 86 students who were interviewed on the five campuses, the 15 students who were 25 years of age or older at the time of the interview were selected for this study. Studying the Performance of Black transfer students: based on a logistic regression model refined to include transfer pathway (2-year vs. 4-year), we learned that: Studying the Mean Grade Differential by Course Discipline: For engineering transfer and first-time-in-college (FTIC) students, we computed average grades in STEM courses by discipline, and by institution

Returning Students in Engineering Education: Making a Case for “Experience Capital”

Students returning to college are not generally studied, where most of the research on non-traditional students is focused on individuals returning to earn their undergraduate degree. There are, however, many students returning to receive graduate degrees as they pursue new directions in life by interest or economic necessity. Undergraduate students with experience have clear educational related goals, practical approaches to problem-solving, and high learning motivation.Returning graduate students are expected to model similar behaviors. These individuals bring a lifetime of personal and professional expertise, which we identify as “experience capital.”A review of the literature reveals that capital has been pondered since early western philosophers considered the concept of social capital in terms of „community governance‟. Others credit Dewey with the first use of the term „social capital‟. Since then, development of other capitals include human, cultural, and symbolic. Human capital is viewed as knowledge, skills, and attributes; cultural capital as an indicator of class position acquired by family and education ; and symbolic as the prestige, recognition, and fame. Today, social capital is viewed as the networks,relationships, and connections of influence and support. Experience capital is the partial union of social, human, cultural, and symbolic capital, which individuals develop from their persona land professional experiences as they progress through life.This is an exploratory study capturing the perceptions of “experience capital” of individuals with several years of professional experience in their discipline returning for a doctoral degree in engineering education. The research question this study addresses is: what “experience capital”do returning students bring to an engineering education doctoral program? The participants will be interviewed; open coding will be used to identify common themes. The results of this qualitative study will position the experiences of the participants at the partial union of social,human, cultural, and symbolic capital, in a space called experience capital

AfrOBIS: a marine biogeographic information system for sub-Saharan Africa

AfrOBIS is one of 11 global nodes of the Ocean Biogeographic Information System (OBIS), a freely accessible network of databases collating marine data in support of the Census of Marine Life. Versatile graphic products, provided by OBIS, can be used to display the data. To date, AfrOBIS has loaded about3.2 million records of more than 23 000 species located mainly in the seas around southern Africa. This forms part of the 13.2 million records of more than 80 000 species currently stored in OBIS. Scouting for South African data has been successful, whereas locating records in other African countries has been much less so

AfrOBIS: a marine biogeographic information system for sub-Saharan Africa

AfrOBIS is one of 11 global nodes of the Ocean Biogeographic Information System (OBIS), a freely accessible network of databases collating marine data in support of the Census of Marine Life. Versatile graphic products, provided by OBIS, can be used to display the data. To date, AfrOBIS has loaded about 3.2 million records of more than 23 000 species located mainly in the seas around southern Africa. This forms part of the 13.2 million records of more than 80 000 species currently stored in OBIS. Scouting for South African data has been successful, whereas locating records in other African countries has been much less so

Improving the assessment of transferable skills in chemistry through evaluation of current practice

The development and assessment of transferable skills acquired by students, such as communication and teamwork, within undergraduate degrees is being increas-ingly emphasised. Many instructors have designed and implemented assessment tasks with the aim to provide students with opportunities to acquire and demon-strate these skills. We have now applied our previously published tool to evaluate whether assessment tasks allow students to demonstrate achievement of these transferable skills. The tool allows detailed evaluation of the alignment of any as-sessment item against the claimed set of learning outcomes. We present here two examples in which use of the tool provides evidence for the level of achievement of transferable skills and a further example of use of the tool to inform curricu-lum design and pedagogy, with the goal of increasing achievement of communi-cation and teamwork bench marks. Implications for practice in assessment design for learning are presented

The Danish High Risk and Resilience Study-VIA 11: Study Protocol for the First Follow-Up of the VIA 7 Cohort -522 Children Born to Parents With Schizophrenia Spectrum Disorders or Bipolar Disorder and Controls Being Re-examined for the First Time at Age 11.

Introduction: Offspring of parents with severe mental illness have an increased risk of developing mental illnesses themselves. Familial high risk cohorts give a unique opportunity for studying the development over time, both the illness that the individual is predisposed for and any other diagnoses. These studies can also increase our knowledge of etiology of severe mental illness and provide knowledge about the underlying mechanisms before illness develops. Interventions targeting this group are often proposed due to the potential possibility of prevention, but evidence about timing and content is lacking. Method: A large, representative cohort of 522 7-year old children born to parents with schizophrenia, bipolar disorder or controls was established based on Danish registers. A comprehensive baseline assessment including neurocognition, motor functioning, psychopathology, home environment, sociodemographic data, and genetic information was conducted from January 1, 2013 to January 31, 2016. This study is the first follow-up of the cohort, carried out when the children turn 11 years of age. By assessing the cohort at this age, we will evaluate the children twice before puberty. All instruments have been selected with a longitudinal perspective and most of them are identical to those used at inclusion into the study at age 7. A diagnostic interview, motor tests, and a large cognitive battery are conducted along with home visits and information from teachers. This time we examine the children's brains by magnetic resonance scans and electroencephalograms. Measures of physical activity and sleep are captured by a chip placed on the body, while we obtain biological assays by collecting blood samples from the children. Discussion: Findings from the VIA 7 study revealed large variations across domains between children born to parents with schizophrenia, bipolar and controls, respectively. This study will further determine whether the children at familial risk reveal delayed developmental courses, but catch up at age 11, or whether the discrepancies between the groups have grown even larger. We will compare subgroups within each of the familial high risk groups in order to investigate aspects of resilience. Data on brain structure and physical parameters will add a neurobiological dimension to the study