We investigated the use of concept maps to assess "knowledge integration," by developing a rubric to "score" maps using two samples of students. In 2000, a sample of sophomore, junior and senior industrial engineering students was asked to develop concept maps of that field. This exercise was repeated in 2002 with seniors, half of whom participated as sophomores. The maps were scored using "traditional" counting metrics proposed by other researchers and with a holistic approach developed for the study. The holistic approach indicated that students improved as they matriculated through the program, and also enabled faculty to identify programmatic weaknesses. Although the "traditional" methods provided quantifiable information, closer examination questioned whether that information was representative of students' true conceptual understanding. Consequently, a scoring rubric was developed and tested using attributes of the holistic approach.

The "new" Accreditation Board for Engineering and Technology criteria, EC-2000, has caused engineering educators to focus on 11 intentionally undefined outcomes as a necessary step in the accreditation process. As part of a large study sponsored by the National Science Foundation, a framework, based on Bloom's taxonomy, has been developed for better specifying these outcomes. Using this framework, each outcome has been expanded into a set of attributes that can then be used by engineering faculty in adapting the outcomes to their own program. Also discussed are two ways in which this characterization of outcomes can be used as part of an assessment and feedback process. These outcome definitions are considered to be in a dynamic state; i.e., they will continue to be modified and updated as more is learned about their specificity and use. Interested readers may download the most recent set of outcomes from the project website.

This research examines demographic, academic, attitudinal, and experiential data from the Cooperative Institutional Research Program (CIRP) for over 12,000 students at two universities to test a methodology for identifying variables showing significant differences between students intending to major in science, technology, engineering, or mathematics (STEM) versus non‐STEM subjects. The methodology utilizes basic statistical techniques to identify significant differences between STEM and non‐STEM students within seven population subgroups based upon school attended, race/ethnicity, and gender. The value of individual variables is assessed by how consistently significant differences are found across the subgroups. The variables found to be most valuable in identifying STEM students reflect both quantitative and qualitative measures. Quantitative measures of academic ability such as SAT mathematics score, high school grade point average, and to a lesser extent SAT verbal score are all indicators. Qualitative measures including self‐ratings of mathematical ability, computer skills, and academic ability are also good indicators.

Background Early identification of students who are potential candidates for achieving a degree in a Science, Technology, Engineering, or Mathematics (STEM) major would enable educators to offer programs designed to better enhance student interests and capabilities in those areas. Purpose (Hypothesis) This study uses an integrated model leveraging the strengths of multiple statistical techniques to analyze the educational process from pre‐high school through college and predict which students will achieve a STEM education. Design/Method The probability of earning a STEM degree is modeled using variables available as of the eighth grade as well as standardized test scores from high school. These include demographic, attitudinal, experiential, and academic performance measures derived from the National Education Longitudinal Study of 1988 (NELS:88) dataset. The integrated model combines logistic regression, survival analysis, and receiver operating characteristics (ROC) curve analysis to predict whether an individual is likely to obtain a STEM degree. Results Predicted results of the integrated model were compared to actual outcomes and those of a separate logistic regression model. The modeling process identified a set of significant predictive variables and achieved very good predictive accuracy. The integrated model and logistic regression model performed with comparable precision. Conclusions The modeling process was adept at identifying STEM students and a large pool of other degree students that might have been capable of pursuing a STEM degree. The results suggest that it is quite feasible to identify good STEM candidates for a pro‐STEM intervention to engage their interest in STEM and support stronger quantitative skill development.