Abstract: Background: Recent U.S. reports have urged undergraduate engineering programs to develop graduates who will be successful in a competitive future workforce. Similarly, Engineers Australia has identified an array of competency standards that graduates should possess to be prepared to be Professional Engineers. The U.S. National Academy of Engineering (2004) outlined a strategy emphasizing a set of learning outcomes that will prepare engineering graduates for work in a dynamic and global workplace. Engineers still will need to exhibit strong analytical skills, but they also will need to be proficient in an array of other abilities, including professional skills, interdisciplinary competence, and contextual competence. Prior large-scale engineering education research (e.g., Sheppard et al., 2010) has focused on whether and how students develop a particular skill (e.g., fundamental skills or teamwork skills or design skills), but it is a multidimensional array of skills (e.g., fundamental skills and teamwork skills and design skills) that students will need to meet the expectations of engineering employers. Purpose: This paper develops an outcomes-based typology to: 1) identify empirically distinct groupings of fourth- and fifth-year undergraduate engineers when assessed and categorised on a variety of selfreported learning outcomes, and 2) determine how engineering students' individual characteristics and educational experiences are related to this set of student-reported outcomes. Design/Method: Using a nationally representative sample of 120 U.S. engineering programs from 31 institutions, this study drew on survey data from engineering students who provided information on their pre-university characteristics, curricular and co-curricular experiences in their engineering programs, and self-ratings of engineering-related competencies. This paper used cluster analysis to produce a typology of engineering students based on nine self-reported learning outcomes. Multinomial logistic regression next used student characteristics and university experience variables to predict cluster membership. Results: Analyses using a multidimensional set of learning outcomes produced a meaningful typology that distinguished between groupings of students. A subset of students reported high skills and abilities on the full array of learning outcomes and are the "model" graduates that programs seek to develop. Though clusters only took into account data related to the outcomes, distinctive curricular and cocurricular experiences distinguished the highly proficient students from other clusters of students. Emphasizing broad and systems perspectives in the curriculum most consistently distinguished these "model" students from other students. Other distinguishing variables differed across clusters, as findings demonstrated great variation in the balance of reported learning outcomes across students. Conclusions: The study identifies a technique to create an outcomes-based typology that can be applied to any set of learning outcomes, such as those prioritised by Engineers Australia. Rather than disaggregating knowledge and skills of individual students, this technique allows educators to understand how an individual's skills vary holistically (i.e., which skills are well developed, and which must be strengthened) and links outcomes to students' characteristics and educational experiences. This new approach responds to the needs of academic programs, which seek to cultivate an array of abilities in their graduates rather than just one.
To cite this article: Knight, David B. An approach for studying multiple learning outcomes of undergraduate engineers [online]. In: Mann, Llewellyn (Editor); Daniel, Scott (Editor). 23rd Annual Conference of the Australasian Association for Engineering Education 2012: Profession of Engineering Education: Advancing Teaching, Research and Careers, The. [Melbourne, Vic.]: Engineers Australia, 2012: 351-359.
[cited 27 Jun 17].