“It Took a Village” - Stories from Students in the Social Sciences About Learning Quantitative Methods
Bibliographic record
Abstract
For most undergraduate students studying in fields without a focus on statistics or data science (i.e., non-majors), their only opportunity to acquire these in-demand data analysis skills is in their required quantitative methods course. These courses generally have a bad reputation among students who do not see how the course fits within their program. There have recently been improvements to these courses; however, the negative perceptions persist. The objective of this research was to examine the experiences of non-major students during their introductory quantitative methods course with the goal of understanding how these courses are experienced and can continue to be improved. A narrative-based approach was used with 11 non-major undergraduate students at the end of their studies (third, fourth and sixth year) who participated in semi-structured interviews where they told stories about their quantitative methods course. A thematic analysis which identified six main themes was conducted, and the results are presented using 4 turning-points (before the class, before the middle of the course, before the final, and after the class). The results provided insight about how these courses are experienced and the findings are discussed in terms of potential opportunities for improvement in these courses moving forward.
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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 | 0.014 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".