Gathering Evidence of Learning in Library Curriculum Center Spaces with Web GIS
Bibliographic record
Abstract
Abstract Objective –This article reports on a pilot research project that gathered usage statistics in specifically designated library learning spaces using a Web-based Geographic Information System (GIS). These learning spaces were then mapped to expected learning activities that would occur in these areas based on its intention or design. In this way, the library could begin to associate the usage of a space with different types of learning. The researchers then mapped these learning activities to campus learning outcomes to create learning impact statements. Methods – The researchers used observation data gathered with a Web GIS tool to examine space usage within the library’s curriculum center. Results – The pilot study found that student usage of the curriculum center was mainly associated with two campus learning outcomes: (1) Communicate and (2) Learning and Integrate. The evidence also indicated possible design improvements that may make the curriculum centers spaces more functional for students. Conclusions – The Web GIS tool proved to be a useful tool to gather evidence of student space usage within the library environment. The mapping of individual spaces to learning activities further enhanced the usefulness in interpreting how students are using library spaces. Leveraging the space usage data within learning outcomes statements created another means for the library to communicate its learning impact with campus stakeholders.
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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 | 0.029 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".