Active Learning in Post-Secondary Statistics and Data Sciences Teaching: Lesson-Level Moments and Course-Level Alternative Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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