Interests and Challenges in Machine Learning: Differences by Gender, Prior Experience, and First Generation Status
Bibliographic record
Abstract
Despite the rise of machine learning (ML) in computing, it is widely acknowledged by practitioners that the field lacks diversity. Prior studies have shown that ML is perceived as a challenging field, and students from underrepresented groups are less likely to take an ML course, possibly due to differing interests compared to their peers. This study explores the differences—by gender, prior experience in ML, and first-generation status—in student interests and challenges before and during an ML course. We build upon prior work that collected students’ self-reported interests and challenges from a sequence of 5 surveys conducted across 2 introductory ML courses. These survey responses had been qualitatively coded, and we examine the frequency of various interest and challenge themes and how they differ by gender, prior experience and first-generation status. We find that at the start of the ML course, women were more likely than men to report practical/implementation aspects of ML as both interesting and, separately, challenging. Those without prior ML experience showed more general curiosity about the course rather than specific interests. While these results are suggestive, they are no longer statistically significant after applying the Bonferroni correction to the Chi-squared tests. Since variations in student interests and challenges are largely minimized during the ML course, these differences can be targeted in future work to increase ML course uptake and broaden participation.
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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 | 0.003 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".