A Text Analysis of Four Levels of Librarian Involvement and Impact on Students in an Inquiry-Based Learning Course
Bibliographic record
Abstract
Librarians at the University of Calgary collaborated with instructors on an inquiry-based learning course with varying involvement across four course sections. This study uses text analysis of student assignments to assess information literacy (IL) skill development across four levels of course participation: librarian as instructor-of-record, two levels of embeddedness, and a single ‘one-shot’ session. The methodology included the tracking of keywords generated using the ACRL Framework for Information Literacy and text analysis of student reflection assignments in an inquiry-based, research-focused first-year undergraduate course. The results suggest that the benefit to student IL skills is not related to amount of librarian instruction, but rather to the level of instructor buy-in with regard to library services and the importance of IL skills. We argue that the most impactful librarian involvement is as an IL course consultant rather than a full-time embedded librarian (which is surprising given the literature on the efficacy of embeddedness). Although further research is needed, the study results have significant implications for academic librarian instructional practices and collaborations on course content with faculty members.
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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 | 0.005 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".