Replication: A Pedagogical Tool for Teaching Ethical Practices to Future Software Engineers
Bibliographic record
Abstract
Plagiarism activities such as copying code, algorithms, or documentation without consent and lack of attribution are rising in industry and academia. While this can be attributed to the rise of generative AI, a lack of awareness about plagiarism and its implications among soon-to-be software engineers and practitioners raises serious concerns about academic integrity and adds another dimension to this challenge. This research proposes exploring “replicating a study” as a pedagogical tool to impart ethical considerations to software engineering undergraduate students. Replicating a study involves recreating and validating existing research findings utilizing datasets from the original study, contributing to a deeper understanding of engineering concepts. Thus, while working on a replication study, students can be prompted to explore and understand professional ethics such as obtaining informed consent, permission to reuse data, and giving credit to original authors. Using preliminary results from such an experiment with two undergraduate student groups, in this ongoing research, we explore and solicit input to modify the methodology for a more extensive study.
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How this classification was reachedexpand
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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".