Development of a professional identity may contribute to persistence in an engineering major. Further, perceiving alignment of one’s career values to engineering as well as believing that one is enacting engineering during coursework are expected to support identity formation. Using mixed methods approaches, we explored data provided by 186 undergraduate students responding to four open-ended questions at the end of an introduction to engineering course. Our results indicated alignment of perceptions of engineering and personal values, such as when students who emphasised the role of engineers as helping others tended to have altruistic professional goals. When considering identification with engineering, we found that definitions focused on the role of math and science as well as altruistic definitions were less likely to co-occur with a belief that students were already practicing engineering. Findings suggest certain definitions of engineering inhibit perceptions that the student is already enacting engineering and may affect identification.

The recommendation of using “multiple measures” is common in policy guidelines for gifted and talented assessment systems. However, the integration of multiple test scores in a system that uses cut-scores requires choosing between different methods of combining quantitative scores. Past research has indicated that OR combination rules (where any one score must meet the cutoff) have been shown to identify the largest number of students compared with AND (where all scores must meet cutoff) and AVERAGE (where the numerical average is used) rules. We extended this work to a real data set (Grades K to 5 from the CogAT 7 national standardization data) and found that the different combination rules led to differences in the number and diversity of students identified. However, we found these diversity differences were due mainly to the identified pool size and not to the effects of the particular combination rule. This has important implications for practice.

Effective talent-identification procedures minimize the proportion of students whose subsequent performance indicates that they were mistakenly included in or excluded from the program. Classification errors occur when students who were predicted to excel subsequently do not excel or when students who were not predicted to excel do. Using a longitudinal sample, we assessed the accuracy of measures of verbal reasoning, quantitative reasoning, nonverbal reasoning, and current achievement for predicting later achievement. We found that seemingly small differences in predictive validity substantially changed the number of students erroneously included or excluded from the program. Surprisingly, nonverbal tests not only led to more classification errors but also failed to identify more English language learners and minority students. To increase equity and maintain fairness, practitioners should carefully evaluate claims that scores from alternative assessments are as valid as scores from conventional ability tests and verify that the use of these tests result in greater diversity.