The Role of Credit-Bearing Information Literacy Courses as a Preventative Approach to Academic Integrity Education in First-Year University Students: A Case Study
Bibliographic record
Abstract
Recently, there has been an increasing focus on how librarians can better support student learning and the development of research and reference skills, particularly when it comes to academic integrity. While one-shot information literacy sessions could prove useful in introducing students to academic integrity skills, they often are limited due to lack of time to dive deeper into the content, lack of assessment opportunities, and, most importantly, lack of application of these skills in the context of a specific assignment. This chapter aims to understand the role of credit-bearing information literacy courses in fostering academic integrity skills of first-year students. By diving into the curriculum design and implementation of the first-year credit-bearing information literacy labs, the authors explore how credit-bearing labs can be used as a preventative measure to address academic dishonesty among undergraduate students. The authors uncover a unique contextual approach to academic integrity education by integrating it into the assignment-based academic integrity curriculum in the information literacy course. Furthermore, the chapter offers insights into students’ self-assessment of their academic integrity learning and examines how students understand and engage with academic integrity principles in the information literacy course.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.014 |
| 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; 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".