Teaching research skills through embedded librarianship
Bibliographic record
Abstract
Purpose This pilot aims to study a way of integrating research and writing support into a university course along with content. Research and writing skills are not taught explicitly in most university courses, yet these skills are increasingly required both in and outside of the classroom. Design/methodology/approach An embedded, collaborative instructional team comprising the instructor, librarians and writing specialists re-designed a first-year inquiry-based learning course, incorporating research and writing instruction throughout, formative and summative assessments and a flipped classroom model. At the end of the course, each member of the team reflected on their collaborative and individual experiences. The team also surveyed students to gauge their perceptions of the research and writing sessions. Findings The team learned from this experience and noted a large, but rewarding, time commitment. The flipped classroom model allowed the tailoring of instruction to students’ needs but required more work by librarians to prepare content and to grade. Students indicated appreciation for repeated interactions with librarians and reported confidence to use the skills taught. Originality/value Embedding librarians throughout the course with a writing specialist, as well as involvement in grading, is novel – this may be the first example in the literature of “deep integration”. The concept of “embedded librarianship” can be enhanced by expanding librarian and other support roles in a course.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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 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".