Digital divide in quantitative methods: <scp>T</scp>he effects of computer‐assisted instruction and students' attitudes on knowledge acquisition
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Computer‐assisted instruction can change the way introductory statistics and quantitative methods courses are taught. Using a two‐group pretest–posttest design, we conducted an experiment using an undergraduate social science student sample to investigate whether the introduction of statistical software to teaching quantitative methods would improve knowledge acquisition and attitudes toward quantitative methods courses. Our project confirmed that implementing computer‐assisted instructional methods increased knowledge acquisition in quantitative methods courses compared with students' academic performance in other courses, measured by grade point average. We also found that student attitudes have weak and mostly nonsignificant influence on quantitative methods knowledge tests. Additionally, the paper suggests a curriculum‐level approach to teaching quantitative methods to undergraduate students.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 it