Shifting the Stories around Academic Integrity: How Critically Reflective Educators Create Empowered Student Learning
Bibliographic record
Abstract
Research shows that the underlying attitudes of educators not only influence instruction but also impact student outcomes themselves. As we begin to reflect on what the past year has taught us, we want to consider how we as educators can challenge our own attitudes around academic misconduct and empower our students to take ownership of their education. In this session, we will look at the ways in which your inherent biases about student integrity could be surfacing in your online and onboard classroom. We will consider practical shifts in the way you teach and provide feedback in order to influence student outcomes in meaningful ways. You will leave this session with applied evidence-based ideas for improved learning design by incorporating some of Turnitin’s new capabilities into the assessment workflow.
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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 | 0.070 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.030 | 0.077 |
| Scholarly communication | 0.041 | 0.023 |
| Open science | 0.005 | 0.027 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".