The Numbers Tell It All: Students Don't Like Numbers!
Bibliographic record
Abstract
Undergraduate Students' interest in taking quantitative vs. non quantitative courses has received limited attention even though it has important consequences for higher education. Previous studies have collected course interest ratings at the end of the courses as part of student evaluation of teaching (SET) ratings, which may confound prior interest in taking these courses with students' actual experience in taking them. This study is the first to examine undergraduate students' interest in quantitative vs. non quantitative courses in their first year of studies before they have taken any quantitative courses. Three hundred and forty students were presented with descriptions of 44 psychology courses and asked to rate their interest in taking each course. Student interest in taking quantitative vs non quantitative courses was very low; the mean interest in statistics courses was nearly 6 SDs below the mean interest in non quantitative courses. Moreover, women were less interested in taking quantitative courses than men. Our findings have several far-reaching implications. First, evaluating professors teaching quantitative vs. non quantitative courses against the same SET standard may be inappropriate. Second, if the same SET standard is used for the evaluation of faculty teaching quantitative vs. non quantitative courses, faculty are likely to teach to SETs rather than focus on student learning. Third, universities interested primarily in student satisfaction may want to expunge quantitative courses from their curricula. In contrast, universities interested in student learning may want to abandon SETs as a primary measure of faculty teaching effectiveness. Fourth, undergraduate students who are not interested in taking quantitative courses are unlikely to pursue graduate studies in quantitative psychology and unlikely to be able to competently analyze data independently.
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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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".