Information Literacy Beyond Librarians: A Data/Methods Triangulation Approach to Investigating Disciplinary IL Teaching Practices
Bibliographic record
Abstract
Abstract Objective – While library literature contains many studies examining faculty perceptions of the value of librarian-led information literacy (IL)instruction, there is little evidence regarding IL instruction practices of disciplinary faculty independent of librarians. In a climate of uncertain budgets, increasing student enrollment, and increased conversation around the need for IL, media, and digital literacy skills, this study aimed to investigate a little-researched area of the IL instruction, learning, and development milieu. Methods – In collaboration with the institutional research office, a data and methods triangulation approach was used. A survey of disciplinary faculty was administered and disciplinary faculty focus groups were also conducted. Student outcomes and annual assessment reports, documents that describe teaching and assessment methods for courses across the university, were analyzed. Voyant, a text-mining tool, was also used to determine key phrases and terms related to IL in these documents. Results – Results revealed that disciplinary faculty highly value skills and understandings affiliated with IL competency. Faculty provide the majority of IL learning opportunities independent of librarians, although these learning opportunities are generally provided through implicit, rather than explicit, methods. Pedagogical methods that may enable explicit practices, such as the use of standards and competencies, are infrequently used. Conclusion – Evidence and findings from this study are being used to inform several initiatives to work with disciplinary faculty for IL instruction, including new services, resources, and instruction models to support IL development in students.
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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 | 0.121 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".