Gender Diversity in Undergraduate Engineering: Understanding the Major Selection Process
Bibliographic record
Abstract
Abstract Literature shows two factors that affect students' selection of college major: academic aptitude and personal expectations. Some studies have found a difference in these variables based on gender. Malgwi, Howe, and Burnaby (2005) found that male students choose majors based on potential career options, while female students choose majors based on academic ability. Our study seeks to confirm whether this result is observed when examining engineering students' major selection, major selection influences, and the timing of a student's decision. To design the research survey, four previous studies and associated surveys were consulted: Kuechler & Simkin (2009); Arcidiacono & Kang (2012); Culpepper (2006); and Malgwi, Howe & Burnaby (2005). Our study targets undergraduate students to determine (1) When they began to gain an interest in their selected major, and (2) Who or what was influential in that process. The 52-question instrument was approved by consortial IRB from the authors' institutions, and the study was conducted at 3 separate institutions, each with at least one ABET accredited program. Preliminary data based on nearly 100 responses collected so far from students currently majoring in engineering suggests that: (1) female engineering students early in their undergraduate studies are already more likely to consider graduate degrees than their male counterparts, (2) female students tend to be more academically prepared than their male counterparts, and (3) though female students became interested in STEM majors throughout their educational careers, they did not choose engineering specifically until later than their male classmates. Due to the nature of the questions in the instrument, most responses analyzed in this study were collected as categorical or ordinal data; results are therefore presented primarily through visual representations using frequency distributions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".