Syllabus Mining for Information Literacy Instruction: A Scoping Review
Bibliographic record
Abstract
Background - The course syllabus is a roadmap to curriculum development and student learning objectives providing valuable information to assist library instruction. This scoping review examines research that uses syllabus mining to track Information Literacy concepts and skills in academic settings. Objectives - The present study uses a scoping methodology to examine syllabus mining of Information Literacy with the focus of analysis on the methodologies employed in syllabus review and the recommendations from the studies. Design - Searches of databases of literature from librarianship and education, as well as a multidisciplinary database, yielded 325 journal articles. Inclusion criteria specified peer-reviewed articles from any year, and excluded grey literature. After removing duplicates, 2 reviewers screened titles and abstracts and reviewed full text, yielding 17 studies to analyze. Results - Characteristics of the included studies, methodology, and recommendations were charted by two reviewers. All studies reported retrieving information that increased opportunities for collaboration with instructors and targeted engagement with students, and seven themes were identified. Conclusions - Instructional librarians should be encouraged to conduct syllabus studies to increase collaboration with faculty to develop coursework, to meet student information needs in a strategic manner, and to identify discipline-specific Information Literacy concepts.
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.
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 | 0.019 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".