Bibliographic record
Abstract
Our research team in conjunction with the Teaching Centre investigated academic integrity on campus in order to better understand academic dishonesty within our institution. Before the SARS-CoV-2 pandemic, we surveyed the perceptions of, and engagements with, academic dishonesty on campus. We surveyed both student (n=1,142) and faculty (n=130) populations in order to get a broad sense of academic dishonesty at our university. These samples represented 13% of the student population at the time, and 22% of the faculty population. Overall, we found that the majority of students and faculty surveyed believed academic integrity was important, and that unlike many universities across Canada, the University of Lethbridge had low rates of academic dishonesty, though the possible reason for these low rates was unclear. Like many universities around the world, the University of Lethbridge transitioned to a remote teaching model in the spring semester of 2020 in response to the SARS-CoV-2 pandemic. Quickly, faculty and students became concerned with how assessments that were usually proctored in-person would translate to an online learning space. After a year of remote-delivery instruction, we again surveyed students (n=1,134; 13.9% of the student population) and faculty (n=94; 15% of the faculty population) in the spring of 2021, both to further explore why we experienced relatively low rates of academic dishonesty, but also to understand how remote teaching may have impacted perceptions of and engagements with academic dishonesty at our university. We found that despite the majority of student participants reporting that they felt the opportunity to engage in academic dishonesty had increased since the university transitioned to remote learning, they again reported that they engaged in low rates of academic dishonesty were still low compared to other Canadian institutions. Furthermore, faculty participants said they had reported incidents of academic dishonesty less often during this period. If selected, we plan to present the results of our surveys and highlight key differences that emerged, exploring that our low rates may be due to the academic culture of our institution. We also plan to comment on how the transition to online learning effected the perception of academic integrity at our university in comparison to pre-pandemic times. Additionally, we will present qualitative data from open-ended survey responses to illustrate the concerns of our students and faculty members about academic integrity throughout the transition to online learning.
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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 | 0.003 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 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".