Collaboration between Librarians and Teaching Faculty to Teach Information Literacy at one Ontario University: Experiences and Outcomes
Bibliographic record
Abstract
Purpose: In this study, we sought to describe information literacy success outcomes for students who participated in a university course where university librarians and teaching faculty collaborated in all aspects of the course including; curricular development, assignment development, in-class teaching, office hours for individual student development, and assessment activities. The authors wanted to examine student success in attaining information literacy skills following this one semester course. Further, the authors wanted to determine what difficulties in achieving expected information literacy levels persist even after intensive collaborative instruction. Finally, the authors wished to describe the challenges of these collaborations. Methodology: The focus of this study was to determine changes in first-year university students’ information literacy knowledge and skill following a thirteen week university preparation course that was developed through strong collaboration between university librarians and teaching faculty. Students entering their first semester of university were tested on their information literacy skills without feedback. They then took part in the required course and were post tested in the last week of the semester. Findings: Student showed strong increases in information literacy from this collaborative approach. In addition, teaching faculty and librarians felt positive about the collaborative experience. However, some students showed misunderstandings about information literacy that requires further research. Originality and Practical Implications: Our unique contribution here is our description, experiences and detailed outcomes with a collaborative process to teach information literacy. Based on our experiences here, we believe that collaboration will work best if it is planned at a curricular level, if the librarians are truly integrated into the classroom, if the librarians provide input on assignments and help with student feedback, and if targeted information literacy knowledge is tested. This planning takes time, but the librarians offer unique contributions and insight into issues surrounding information literacy that may not be obvious to faculty instructors. In our study, we also found that students confuse assignment requirements with general information literacy standards and those teaching information literacy need to be aware of these confusions. Finally, integration of librarians into college/university courses has benefits in terms of increases in student information literacy and increases in librarian knowledge of faculty expectations.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".