Detecting Gender Bias to Enhance Inclusivity in Software Engineering Education
Bibliographic record
Abstract
Educational environments and course materials—such as textbooks, notes, slides, and examinations—are foundational elements that can either encourage or discourage students from pursuing studies and careers in STEM fields. Detecting gender bias in these materials is essential for fostering inclusivity and diversity in the field. Research shows that early exposure to inclusive and relatable course content significantly influences students’ interest and persistence in STEM fields, highlighting a direct connection between educational experiences and career choices. Building on this foundation, this study investigates whether course materials—with the focus on software engineering—exhibit a male, female, or neutral orientation through an automated approach that incorporates keyword extraction, word analysis, and classification. To ensure our findings accurately reflect the content’s gender orientation, we also consider the subject matter of the materials. This approach helps to distinguish between gendered terms tied to specific contexts and broader gender bias. By offering this analysis of gender bias, our approach supports efforts to create more inclusive and equitable learning environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".