Walking the Walk, Talking the Talk: A Learning-Centered Approach to the Design of a Workshop on Teaching for McGill Librarians
Bibliographic record
Abstract
McGill Library, in partnership with the University's Teaching and Learning Services unit, offers a 1.5 day workshop, Designing and Delivering Effective Information Skills Sessions.This workshop is designed to expose librarians to teaching theory and practice.It provides an opportunity for liaison librarians to (re)design their information literacy instruction according to course context, content, desired learning outcomes, and strategies that facilitate and assess learning.Library literature, and data collected within the McGill Library, indicates that teaching theory and practice are typically not covered in formal MLIS education programs or in on-the-job training.In order to facilitate staff development on learner-centered instructional design, active learning techniques, and assessment, a project team consisting of members from the Library and the Teaching and Learning Services unit at McGill took a learning-centered approach to design the workshop, which incorporates a variety of active learning exercises, and provides opportunities for reflection, assessment, and information literacy instruction session (re)design.In this article, the authors describe the preparation, planning, construction, and presentation of the workshop that resulted from the collaboration.
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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 | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".