Using student questions to direct information literacy workshops
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.034 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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 it