Piloting a blended model for sustainable IL programming
Bibliographic record
Abstract
Purpose – This paper aims to describe the MacEwan University Library’s successful pilot of a fully blended information literacy (IL) instruction program for first-year English courses. Development, implementation and assessment of the pilot prior to full implementation are discussed. Design/methodology/approach – The new sustainable blended model for the English Library Instruction Program reduced duplication of content and effort, incorporated online and in-person instruction and promoted self-directed learning opportunities through a new Learning Commons. This model places essential instruction online while maintaining personal relationships for students with the English Librarian and the Library through multiple points of interaction. Face-to-face instruction efforts were concentrated on developing critical thinking skills through a hands-on source evaluation activity and on providing point-of-need support. Librarians worked closely with English faculty to encourage early voluntary adoption of the new model for the Fall 2013 pilot. Findings – The voluntary early-adopter model worked well for garnering and maintaining support from the English department: the authors had 42 per cent of English sessions piloting the new model for Fall 2013, surpassing the initial target of 25 per cent. Students scored well on an assessment of their ability to identify scholarly sources. Librarian preparation time has been greatly reduced. Originality/value – Many academic libraries are looking to asynchronous online tutorials as a more sustainable model for delivering IL instruction. This case study demonstrates that it is possible to move some instruction online while maintaining the personal relationships librarians have forged with students and faculty.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".