Bibliographic record
Abstract
While the text-matching tool, Turnitin®, has traditionally been used to deter and detect plagiarism, more recently, instructors have started to use this tool for formative self-assessment. To describe Turnitin®’s use in practice and to explore perceptions of this tool, we surveyed 940 students, teaching assistants, and instructors at a Canadian university. Our findings indicate that Turnitin® was more commonly used for plagiarism detection than for formative self-assessment. The majority of respondents had positive views of Turnitin®, and 70% of students stated that they had no concerns about using this tool. Despite these positive findings, content analysis of open-ended responses indicate that students experience increased anxiety of being falsely accused of plagiarism and have concerns about their work being stored in the Turnitin® database. Our findings lead us to conclude that there is a need for more information and improved communication about Turnitin® for all three groups. Bien que l’outil Turnitin® de mise en correspondance de texte ait été traditionnellement utilisé pour prévenir et détecter le plagiat, plus récemment, les instructeurs ont commencé à utiliser cet outil pour l’auto-évaluation formative. Afin de décrire l’emploi de Turnitin® dans la pratique et d’explorer les perceptions de cet outil, nous avons réalisé une enquête par sondage d’opinion auprès de 940 étudiants, assistants pédagogiques et instructeurs d’une université canadienne. Nos résultats indiquent que l’outil Turnitin® est davantage utilisé pour détecter le plagiat que pour l’auto-évaluation formative. La majorité des répondants avaient une opinion positive concernant Turnitit® et 70 % des étudiants ont déclaré qu’ils n’avaient aucune inquiétude concernant l’emploi de cet outil. Malgré ces résultats positifs, l’analyse du contenu des réponses aux questions ouvertes indique que les étudiants avaient peur d’être accusés à tort de plagiat et étaient inquiets que leurs travaux soient sauvegardés dans la base de données de Turnitin®. Nos résultats nous conduisent à conclure que pour ces trois groupes, il faudrait avoir davantage d’information et améliorer la communication concernant Turnitin®.
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.
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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.026 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".