MacEwan University Library’s pedagogical shift: Using active learning activities during first-year information literacy sessions
Bibliographic record
Abstract
A rising from K-12 education, the peda- gogical concept of active learning is becoming more and more commonplace in face-to-face library Information Literacy (IL) sessions.MacEwan University Library decided to update IL sessions to incorporate active learning activities, a decision which not only benefitted the engagement of students and faculty, but the librarians as well. Active learningActive learning refers to a student-centered instruction method that focuses on having students actively participate in the learning process through activities such as group discussion, investigation, experimentation, or role play.This pedagogical technique helps to increase student interest, engagement, and learning by allowing them to express their questions, idea, and opinions. 1 With active learning, the librarian acts less like a lecturer dispensing information and more like a facilitator of critical thinking and reflective learning, helping to develop students' IL skills while promoting essential collaboration between the library and faculty.The librarian becomes less of a focal point, and is able to move through the classroom and assist students, who are given greater opportunity to participate and exercise their skills.Active learning is an approach that recognizes a variety of learning styles and offers instructors multiple ways of reaching learners
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".