The Flipped Classroom in Introductory Statistics: Early Evidence From a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The flipped classroom (FC) inverts the traditional classroom by having students participate in passive aspects of learning at home and active aspects of learning in class with the guide of an instructor. The introductory statistics course for nonmath majors may be especially suited to the FC model given its unique challenges as a required course for students with varying mathematical skills and background. For example, these students often have low interest and high statistics-related anxiety. Recent studies suggest the FC for introductory statistics courses leads to increased performance relative to a traditional lecture-based classroom (LC). This meta-analysis compared the academic performance of students in introductory statistics courses for nonmath majors who were taught in a FC versus those taught in a LC. Results indicate that students in the FC had statistically discernibly higher final performance outcomes compared to the LC delivery with an average difference of 6.9% in performance (Hedge’s g = 0.43), though there was evidence of moderation by the presence of weekly in-class quizzes. These findings suggest that implementing the FC within the introductory statistics classroom at the undergraduate level may improve learning achievement, but more research is needed to explore the role of regular class quizzes. Supplementary materials for this article are available online.
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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 | 0.027 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".