Identifying Exceptional Talent in Science, Technology, Engineering, and Mathematics: Increasing Diversity and Assessing Creative Problem-Solving

Abstract

In the Cultivating Diverse Talent in STEM project, funded by the National Science Foundation in the United States, new assessments were developed, field tested, used to identify students with exceptional talent in science, technology, engineering, and mathematics (STEM), and compared with existing methods (grade point average [GPA], letters of recommendation, self-statements). Students identified by both methods participated in an internship program in laboratories of scientists on the campus of an R1 university in the Southwest. Existing methods limited the diversity of students identified. Significant differences were found between students identified by the new methods (M2) and existing methods (M1) in GPA, ethnicity, and parent level of education. Ethnicity differences may be due to the ethnic makeup of the partner schools, but differences in GPA and parent level of education cannot be attributed to the location of schools. Although GPAs of M1 students were significantly higher (3.71) than those of M2 students (3.07) and M1 students came from higher income groups and schools in higher income areas, the M2 students scored higher on all the performance assessments of creative problem-solving and at similar levels on concept maps and mathematical problem-solving. Studies of the usefulness and psychometric properties of the new assessments are needed with different groups and in different contexts.

Culturally Responsive Assessments of Spatial Analytical Skills and Abilities: Development, Field Testing, and Implementation

“…Considering the fact that GPAs are on a 4-point scale, the difference between an average of 3.9 and an average of 3.0 is educationally and statistically significant. Maker (2020) found significant differences between students selected by the DISCOVER problem solving assessments and those selected by conventional methods in three areas: GPA, ethnicity, and parent level of education. She concluded that the differences in ethnicity could not be attributed to the selection methods because of the makeup of the partner schools (high percentages of Hispanic and American Indian students); however, GPA and parent level of education could be attributed to the differences in methods.…”

“…The conventional method, which had previously been used as the only method of selection, consisted of grade point average (GPA), teacher recommendation, and self-statement. Overall results are provided in another publication (Maker, 2020). Descriptions of the new performance assessments used in the selection process for M2 students are provided in other publicatons: concept maps (Maker & Zimmerman, 2020), math (Bahar & Maker, 2020), life science performance assessment (Zimmerman et al, 2020), physical science performance assessment (Alfaiz et al, 2020).…”

“…However, the smaller schools had none of these programs. A more detailed description of the schools in the CDTIS project is included in the first article in this issue (Maker, 2020).…”

“…Next were those who completed everything, but had a small number of specific clues, and then those who completed the large shape with many pieces and the six pages of the puzzle book, again without specific clues; proceeding from the highest number of puzzle pages completed without specific clues, then with specific clues, until all students were listed. The last step was to assign overall ratings of unknown, maybe, probably, definitely, and wow (see Maker, 2020, this issue, for an explanation of the ratings) using Jenks Natural Breaks system (Jenks, 1967); a method in which all scores are listed from highest to lowest, and the same rating is assigned for scores that are grouped together and are separated from other groups by at least three to five points. If no natural breaks could be easily identified, problem solving behaviors were reviewed to decide which rating to give to students close to the end of one rating and the beginning of another.…”

“…Task motivation was included mainly in the behaviors to be observed: continuously working, does not want to quit even when others have stopped, persists in solving difficult problems, and verbalizes enjoyment of the task. An indepth description of the components of the conceptual framework is included in another publication (Maker, 2020).…”