Learning with students about academic integrity: poster
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
While academic integrity as a specialised profession in higher education is still emerging (Vogt and Eaton, 2022; Mackenzie, 2024), learning developers (LDers) perform many duties to teach and cultivate academic integrity at their institutions. As Bickle, Allen and Mayer (2023, p.1) highlight, many LDers ‘have designed and delivered courses, quizzes, tutorials, and events to promote academic integrity’ and encourage ethical scholarly practices. At their ALDCon23 session about the role of learning development (LD) in academic integrity, Bickle, Allen and Mayer posed questions that are still pressing today, such as, ‘what training do learning developers need?’ and ‘what forms of collaborative cross institutional research on academic integrity would be advantageous?’. This poster (see Figure 1) responds to Bickle, Allen and Mayer’s session by sharing reflections on a new service our LD team launched in 2023 in partnership with our student conduct office. At our Canadian institution, instructors who report academic misconduct must select one or more ‘resolutions’, and a one-to-one meeting with an LDer is now one option. As LDers have no impact on institutional decisions around misconduct, we have attempted to create a neutral and safe space in these meetings for students to share their experience, deepen their understanding of academic integrity, and develop strategies to help them move forward more confidently in their studies and in contexts beyond higher education. While this model of LDer support is not brand new (Bridgewater, Pounds and Morley, 2019), it remains uncommon in Canada and is worthy of further exploration.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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