Effects of Gender and Military Leave on the Academic Performance of Undergraduate Engineering Students
Bibliographic record
Abstract
Contribution: This article examines how military leaves of absence affect the academic performance of engineering students compared to those who did not take a military leave of absence or took only a general break. Gender-related differences were also analyzed since only male students take a military break. Background: Recognizing the effects of military breaks can help students make informed decisions and, in turn, enhance their academic performance. Consequently, it can help students make data-based decisions about when taking a military break would be least disruptive or, perhaps, even beneficial to their academic careers. Research Questions: How does taking a long-term academic break to complete military service in the middle of one’s undergraduate education impact the student’s performance? Does the gender of students affect their academic performance? Methodology: This research was conducted at the Korea University of Technology and Education (KOREATECH) with data from 1039 undergraduate students in the School of Electrical, Electronics, and Communication Engineering. The students were admitted between 1994 and 2013 and graduated before 2018. The Shapiro–Wilk test, along with the Kolmogorov–Smirnov test, assessed the normality of the distribution of the data. In addition, analysis of variances (ANOVA) and the Kruskal–Wallis tests were implemented for hypothesis testing. Findings: The research findings demonstrate that contrary to the assumption that having a break might be detrimental to academic success, returning to school after a military leave of absence positively affected students’ grade point averages (GPAs). Moreover, students with lower grades significantly increased their GPAs after returning to school from conscription.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".