“It Took a Village” - Stories from Students in the Social Sciences About Learning Quantitative Methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it