The Integration of Information Literacy Skills into the Curriculum
Bibliographic record
Abstract
This paper will discuss the conclusions of the evaluation for the Information Literacy SkillsModules that were integrated into the curriculum of five online Early-European Historyundergraduate self-paced courses, offered at Athabasca University (AU). The Skills Moduleswere incorporated to address serious academic integrity issues students have been strugglingwith. The modules were designed with the view of improving students’ research skills,informing students how to avoid plagiarism, raising their awareness of the rigorous principlesof academic integrity, and complete their research assignments successfully. The SkillsModules are taken by students in parallel to the course content and are strategically linked toresearch assignments. Students’ knowledge acquired in the Modules is evaluated byautomated online quizzes. In the research assignments students are expected to transfer, applyand demonstrate the information literacy skills learned in the Skills Modules. Through thisexploratory survey we learned about students’ perceptions and how the Skills Modules assistthem on conducting and completing their research paper assignments. Last year, a poster withpreliminary findings was presented and discussed at the EDEN –Barcelona conference(Guadarrama, et al., 2015).
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".