What Competencies Should Undergraduate Engineering Programs Emphasize? A Dilemma of Curricular Design that Practitioners' Opinions Can Inform.

Designing a curriculum is a multifaceted challenge. Issues concerning implementation of the curriculum plan must be considered simultaneously with questions about what competencies students should have upon graduation and what the relative emphasis should be among those competencies. These questions were addressed with data from twelve studies in which 10,203 engineering graduates rated the importance of various competencies. All the Accreditation Board for Engineering and Technology (ABET) competencies are deemed important as well as additional competencies, including decision-making (highest importance), commitment to achieving goals, the ability to integrate theory and practice effectively in work settings, leadership skills, and project management (lowest importance). The pattern of importance ratings for respondents overall holds across sub-groups, with rare exceptions. Every statistically significant difference is for a subgroup based on work environment or academic discipline, not demographic, developmental, or time-related variables. This is consistent with Holland’s theory, which predicts that differences in the importance ratings of competencies will be based on undergraduate major and work environment. Engineering graduates perceive the competencies as professional competencies and technical competencies. In the meta-analysis, the sequence of professional competencies is: oral communication (most important), written communication, ethics, life-long learning, teamwork, contemporary issues, and understanding the impact of one’s work (least important). The sequence of technical competencies is: problem solving (most important), data analysis, engineering tools, design, “math, science, and engineering knowledge”, and experiments (least important). Engineering graduates across demographic groups and most majors and work environments consistently rate a top cluster of competencies – problem solving, communication, and data analysis – as significantly more important than a bottom cluster of competencies – contemporary issues, experiments, and understanding the impact of one’s work. Competencies in the intermediate cluster – ethics, life-long learning, teamwork, engineering tools, design, “math, science, and engineering knowledge” – may be statistically tied to the top or bottom clusters, depending on work environment or academic discipline. The few exceptions to this pattern pertain to repositioning of one or two competencies. Therefore, the clusters, the sequence for the professional competencies and the technical competencies are an excellent first approximation for curriculum design in any engineering major.Ph.D.EducationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60691/1/hpassow_1.pd

Exnovation of Low Value Care: A Decade of Prostate‐Specific Antigen Screening Practices

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146902/1/jgs15591-sup-0001-supinfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146902/2/jgs15591.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146902/3/jgs15591_am.pd

Serious Illness and End‐of‐Life Treatments for Nurses Compared with the General Population

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151285/1/jgs16044.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151285/2/jgs16044_am.pd

Engineering Students' Perceptions of and Attitudes Towards Cheating

Academic dishonesty has become a serious problem at institutions of higher learning. This is particularly true in engineering where, according to previous research, engineering undergraduates are among the most likely to cheat in college. To investigate this concern, the authors embarked on a research project whose goal was to develop a better understanding of what students and faculty perceive as cheating and to use this knowledge to help instructors and institutions increase the level of academic integrity among students. The primary instrument for this project was a seven‐page survey that was administered to 643 engineering and pre‐engineering undergraduates at eleven institutions, ranging from community colleges to large research universities. This manuscript provides an overview of the descriptive data from the PACES‐1 Survey organized around the following questions: what is student cheating and how often does it occur; why do students cheat; and what methods can be used to reduce or stop cheating?Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94862/1/j.2168-9830.2006.tb00891.x.pd

Factors influencing engineering students’ decisions to cheat by type of assessment

Academic dishonesty (cheating) has been prevalent on college campuses for decades, and the percentage of students reporting cheating varies by college major. This study, based on a survey of 643 undergraduate engineering majors at 11 institutions, used two parallel hierarchical multiple regression analyses to predict the frequency of cheating on exams and the frequency of cheating on homework based on eight blocks of independent variables: demographics, pre-college cheating behavior, co-curricular participation, plus five blocks organized around Ajzen’s Theory of Planned Behavior (moral obligation not to cheat, attitudes about cheating, evaluation of the costs and benefits of cheating, perceived social pressures to cheat or not to cheat, and perceived effectiveness of academic dishonesty policies). The final models significantly predict 36% of the variance in “frequency of cheating on exams” and 14% of the variance in “frequency of cheating on homework”. Students don’t see cheating as a single construct and their decisions to cheat or not to cheat are influenced differently depending on the type of assessment. Secondary findings are that a student’s conviction that cheating is wrong no matter what the circumstances is a strong deterrent to cheating across types of assessment and that a student who agrees that he/she would cheat in order to alleviate stressful situations is more likely to cheat on both exams and homework.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42694/1/11162_2006_Article_9010.pd

Engineering Students\u27 Perceptions of and Attitudes Towards Cheating

Academic dishonesty has become a serious problem at institutions of higher learning. This is particularly true in engineering where, according to previous research, engineering undergraduates are among the most likely to cheat in college. To investigate this concern, the authors embarked on a research project whose goal was to develop a better understanding of what students and faculty perceive as cheating and to use this knowledge to help instructors and institutions increase the level of academic integrity among students. The primary instrument for this project was a seven-page survey that was administered to 643 engineering and pre-engineering undergraduates at eleven institutions, ranging from community colleges to large research universities. This manuscript provides an overview of the descriptive data from the PACES-1 Survey organized around the following questions: what is student cheating and how often does it occur; why do students cheat; and what methods can be used to reduce or stop cheating

Factors Influencing Engineering Students\u27 Decisions to Cheat by Type of Assessment

Academic dishonesty (cheating) has been prevalent on college campuses for decades, and the percentage of students reporting cheating varies by college major. This study, based on a survey of 643 undergraduate engineering majors at 11 institutions, used two parallel hierarchical multiple regression analyses to predict the frequency of cheating on exams and the frequency of cheating on homework based on eight blocks of independent variables: demographics, pre-college cheating behavior, co-curricular participation, plus five blocks organized around Ajzen\u27s Theory of Planned Behavior (moral obligation not to cheat, attitudes about cheating, evaluation of the costs and benefits of cheating, perceived social pressures to cheat or not to cheat, and perceived effectiveness of academic dishonesty policies). The final models significantly predict 36% of the variance in “frequency of cheating on exams” and 14% of the variance in “frequency of cheating on homework”. Students don\u27t see cheating as a single construct and their decisions to cheat or not to cheat are influenced differently depending on the type of assessment. Secondary findings are that a student\u27s conviction that cheating is wrong no matter what the circumstances is a strong deterrent to cheating across types of assessment and that a student who agrees that he/she would cheat in order to alleviate stressful situations is more likely to cheat on both exams and homework