Applying Kolb’s Learning Theory to Library Instruction: An Observational Study
Bibliographic record
Abstract
Abstract Objective – This article answers the following questions: does applying Kolb’s Learning theory to library instruction enhance student engagement and will it improve librarian teaching practices? Methods – This observational study analyzed four forms of qualitative data to examine the learning experience of first year nursing students and the teaching experience of two Faculty Librarians. The four forms of data collected were: (1) post-class qualitative feedback to assess the students’ engagement; (2) library instructors’ shared teaching observations; (3) librarian peer feedback after observing each other teach; and (4) feedback from an instructional facilitator on the individual librarian’s teaching skills. Two distinct lesson plans were developed: Lesson Plan One was the first attempt at incorporating Kolb’s theory into practice and Lesson Plan Two was a refinement of Lesson Plan One. Teaching strategies were altered from one lesson plan to the next based on the instructional facilitator’s feedback. The role of the instructional facilitator was to guide the professional development of new instructors by providing them with information and feedback on their teaching skills. Results – There were perceived improvements in student engagement and teaching practice from Lesson Plan One to Two. Although the students’ reported experience remained similar from one to the next, both the librarians and instructional facilitator felt the students were more engaged and the environment seemed more collaborative when following Lesson Plan Two. With the second lesson plan, librarian instructors experienced a positive transformation as teachers, becoming facilitators of learning rather than lecturers. Conclusion – Incorporating Kolb’s theory into instructional practice resulted in librarian instructors perceiving a positive effect on both instruction and on student engagement in the teaching-learning process.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".