Information literacy courses in LIS schools: Emerging perspectives for future education
Bibliographic record
Abstract
This study investigates how information literacy courses have been taught in American Library Association-accredited library and information science programmes. Using a content analysis approach, we compared information literacy course syllabi from Master of Library Science (MLS) programmes collect ed in 2005 and 2009. In addition, courses in school library media (SLM) programmes were analysed and compared with MLS syllabi. It was found that the goals of information literacy courses in both types of programme are to educate librarians who can facilitate the development of information literacy skills in the communities they serve. However, our analysis revealed different approaches: information literacy courses in MLS programmes focused on instructional techniques while SLM courses focused on integrating information literacy in the larger educational context. Although instructional techniques are still important, recent literature suggests that effective information literacy education requires integrating skills in learners' unique contexts. We suggest that MLS courses need to go beyond instructional methods and emphasise more collaboration and integration of information literacy practices in their users' communities.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".