Students Experience Cognitive and Emotional Responses to Academic Library Spaces
Bibliographic record
Abstract
A Review of: Mei, X. Y., Aas, E., & Eide, O. (2020). Applying the servicescape model to understand student experiences of a Norwegian academic library. Library & Information Science Research, 42(4), Article 101051. https://doi.org/10.1016/j.lisr.2020.101051 Abstract Objective – To understand how the physical environment of an academic library influences user behaviour. Design – Qualitative explorative. Setting – An academic library at a large university in Norway. Subjects – Twelve bachelor’s and master’s students at a business school. Methods – The researchers used a two-step approach, with the servicescape model from the marketing discipline serving as a theoretical framework. Subjects completed several tasks involving drawing and elaborating on their usage of the library space, utilizing a bird’s-eye floor plan. This was followed by semi-structured interviews to explore how subjects use and experience the library facilities. Main Results – Students found it important to be physically comfortable and to have enough room for the items they need while studying. The library in this study was seen more as a place for studying than for social interactions, and while some subjects reported being motivated by seeing students around them studying, others said they are distracted by having other students in their sightline. The time of the semester appeared to influence user experience and satisfaction with the library space, with spaces conducive to group work desired at some points in the semester and with single seating preferred when individual exams are taking place. Conclusion – The library’s physical environment triggers cognitive and emotional responses in users. These responses influence how frequently they visit the library and how well they are able to concentrate while there. Because academic library spaces have an impact on student learning, it is important to design libraries with user comfort in mind. Libraries should accommodate the different ways students work throughout the semester by providing flexible study space configurations.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".