Learning Outcomes in a Laboratory Environment vs. Classroom for Statistics Instruction: An Alternative Approach Using Statistical Software
Bibliographic record
Abstract
The role of any statistics course is to increase the understanding and comprehension of statistical concepts and those goals can be achieved via both theoretical instruction and statistical software training. However, many introductory courses either forego advanced software usage, or leave its use to the student as a peripheral activity. The purpose of this study was to determine if there was instructional value in replacing classroom time with laboratory time dedicated to statistical software usage. The first approach used classroom lecture presentations, while the second replaced one classroom period per week with statistical software laboratories. It was hypothesized that replacing classroom time with software based laboratories would increase the level of statistics knowledge as compared to an otherwise identical class with no lab based component. Both pre-course and end-of course surveys were used, as well as identical examination questions. Comparisons within a time point, and longitudinal performance over the course were both evaluated. Survey results indicated that students would recommend lab based instruction significantly more than a primarily lecture based instruction (32% more, p=.020). Additionally, the performance improvement over the course of the semester was significantly higher for those students participating in laboratories (19.2% increase, p=.011). These findings indicate that sacrificing classroom time for a laboratory period improves the educational experience in an introductory statistics course and may help with the understanding and retention of difficult topics.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".