Course-Integrated Learning Outcomes for Library Database Searching: Three Assessment Points on the Path of Evidence
Bibliographic record
Abstract
Objective - This study aims to assess student learning with respect to basic database searching at three different points within a required first year course. Methods - Three methods were employed at three different points to identify evidence of successful learning: 1. Analysis of in-class exercises from the initial library workshop, e.g. how many students showed evidence of satisfactorily achieving the stated learning outcomes. 2. Participant observation of student presentations, noting themes, strengths and weaknesses of student research strategy; written observation reports from librarians were coded and quantified to identify major themes. 3. Interviews with course instructors responsible for grading the final submitted projects, focusing on both student achievement and instructor perceptions of the impact of library involvement. Results - Though performance on in-class exercises showed evidence of successful learning in over 70% of students, observational data indicated that very few students showed evidence of applying new knowledge and new search skills to their own topics two weeks later. Instructor interviews revealed a perception of similar difficulties in final project submissions, and instructors suggested that students did not appreciate the need for library resources. Conclusion - In this study, students showed evidence of learning in a simulated environment, but were unable or unwilling to demonstrate this learning in authentic situations. Multiple assessment methods reveal a lack of student ability to apply search skills.
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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 | 0.033 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".