Using student questions to direct information literacy workshops
Bibliographic record
Abstract
Purpose – This article aims to discuss an innovative, student‐centered method for engaging students in one‐shot information literacy workshops. By using student‐generated questions to find out what students want to know about the library, the authors examine how the students' questions are used both as an ice breaker activity and as a means to orient the workshop's content. Design/methodology/approach – A literature review discusses various approaches to active learning activities in one‐shot information literacy workshops as well as methods for assessing students' library knowledge prior to workshops. The authors' own case study identifies best practices for implementing the activity. Finally, the authors discuss the types of student questions they collected from students over the course of two semesters. Findings – The activity outlined in this article provides an engaging method for interacting with students during one‐shot information literacy workshops. The activity acts as an effective method for obtaining a basic understanding of students' library knowledge. Analyses of the questions collected by the authors suggest that librarians should tailor their workshop content depending on the time of year in which their workshops take place. Originality/value – The activity described in this article is discussed sparingly in the literature. As such, this article outlines best practices for a student‐centered activity that librarians can add to their information literacy toolkit. This article is valuable to librarians with instruction responsibilities.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| 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".