Bibliographic record
Abstract
Abstract Objective – This article evaluates the usage and user experience of the Herman B Wells Library’s Learning Commons, a newly renovated technology and learning centre that provides services and spaces tailored to undergraduates’ academic needs at Indiana University Bloomington (IUB). Methods – A mixed-method research protocol combining time-lapse photography, unobtrusive observation, and random-sample surveys was employed to construct and visualize a representative usage and activity profile for the Learning Commons space. Results – Usage of the Learning Commons by particular student groups varied considerably from expectations based on student enrollments. In particular, business, first and second year students, and international students used the Learning Commons to a higher degree than expected, while humanities students used it to a much lower degree. While users were satisfied with the services provided and the overall atmosphere of the space, they also experienced the negative effects of insufficient space and facilities due to the space often operating at or near its capacity. Demand for collaboration rooms and computer workstations was particularly high, while additional evidence suggests that the Learning Commons furniture mix may not adequately match users’ needs. Conclusions – This study presents a unique approach to space use evaluation that enables researchers to collect and visualize representative observational data. This study demonstrates a model for quickly and reliably assessing space use for open-plan and learning-centred academic environments and for evaluating how well these learning spaces fulfill their institutional mission.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".