Active Learning in Post-Secondary Statistics and Data Sciences Teaching: Lesson-Level Moments and Course-Level Alternative Models
Bibliographic record
Abstract
:Statistics and data science post-secondary education have relied heavily on traditional lectures (“chalk and talk” or “slides”). However, newer pedagogies could increase student engagement and learning. Using thematic analysis, we provide a scholarly review of the literature to summarize and synthesize research recommendations related to active learning in this area. We focus on recent research (2011-2022), exploring the ways active learning supplements or replaces traditional classroom instructional practices and its subsequent implications on learning. We found a distinguishing feature between models of active learning: instructors employ either a “lesson-level moments” model where segments of active learning are integrated within traditional instruction, or a “course-level alternative” model where active learning replaces a traditional approach; these two models can be viewed as representing two points on the active learning continuum. Rather than any one model being viewed as superior, there was a strong consensus that the simple implementation of any form of active learning may have positive impacts. Despite these benefits, resources may be lacking to support instructors in implementing and evaluating active learning strategies. Consequently, we conclude by discussing general considerations for active learning and assessment practices in statistics and data sciences education, implications for classroom instruction, and further research opportunities.
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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 | 0.020 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".