Maintaining Quality While Expanding Our Reach: Using Online Information Literacy Tutorials in the Sciences and Health Sciences
Bibliographic record
Abstract
Abstract Objective – This article aims to assess student achievement of higher-order information literacy learning outcomes from online tutorials as compared to in-person instruction in science and health science courses. Methods – Information literacy instruction via online tutorials or an in-person one-shot session was implemented in multiple sections of a biology (n=100) and a kinesiology course (n=54). After instruction, students in both instructional environments completed an identical library assignment to measure the achievement of higher-order learning outcomes and an anonymous student survey to measure the student experience of instruction. Results – The data collected from library assignments revealed no statistically significant differences between the two instructional groups in total assignment scores or scores on specific questions related to higher-order learning outcomes. Student survey results indicated the student experience is comparable between instruction groups in terms of clarity of instruction, student confidence in completing the course assignment after library instruction, and comfort in asking a librarian for help after instruction. Conclusions – This study demonstrates that it is possible to replace one-shot information literacy instruction sessions with asynchronous online tutorials with no significant reduction in student learning in undergraduate science and health science courses. Replacing in-person instruction with online tutorials will allow librarians at this university to reach a greater number of students and maintain contact with certain courses that are transitioning to completely online environments. While the creation of online tutorials is initially time-intensive, over time implementing online instruction could free up librarian time to allow for the strategic integration of information literacy instruction into other courses. Additional time savings could be realized by incorporating auto-grading into the online tutorials.
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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 | 0.003 | 0.013 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".