Enhancing statistics education through Project‐Based Learning ( <scp>PBL</scp> ) and the emergence of <scp>ChatGPT</scp>
Bibliographic record
Abstract
Abstract In the 1990s, educators advocated for projects in statistical courses to enrich student learning. Prior research showcases the positive impact of Project‐Based Learning (PBL), where students complete course‐driven projects. In agreement with this perspective, we implemented PBL methodologies within two statistical courses at a North American research‐intensive university: “Survey, Sampling, & Design” and “Experimental Design.” Students were invited to participate in an optional survey to share their opinions regarding the course project. Consistent with existing literature, our findings indicate that students hold favorable views towards course‐based projects, noticing benefits such as understanding real‐life applications, collaboration, and enhancing data analysis skills. Additionally, many students have incorporated the use of generative AI for their works, such as ChatGPT, and shared the advantages of such tools in their coursework. Drawing from our experiences, we propose strategies to enhance course projects and address concerns related to the overreliance of generative AI tools.
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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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".