Evidence map›Paper›PMID 41965899›Full record

ArticleScientific reports2026

Strategies for designing informal learning spaces in libraries based on comprehensive field measurements of light, noise, thermal, and air quality.

Li Wang, Weihong Guo, Xiao Liu, Xinyu Wang, Luca Caneparo, Guibin Zhang, Bao-Jie He

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Li WangState Key Laboratory of Subtropical Building and Urban Science, School of Architecture, South China University of Technology, Guangzhou, 510640, China.
Weihong GuoState Key Laboratory of Subtropical Building and Urban Science, School of Architecture, South China University of Technology, Guangzhou, 510640, China.
Xiao LiuState Key Laboratory of Subtropical Building and Urban Science, School of Architecture, South China University of Technology, Guangzhou, 510640, China. xiaoliu@scut.edu.cn.
Xinyu WangSchool of Fine Arts, South China Normal University, Guangzhou, 510640, China.
Luca CaneparoDepartment of Architecture and Design, Politecnico di Torino, 10125, Torino, Italy.
Guibin ZhangCollege of Engineering, Shantou University, Shantou, 515063, China.
Bao-Jie HeState Key Laboratory of Subtropical Building and Urban Science, School of Architecture, South China University of Technology, Guangzhou, 510640, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As higher education increasingly emphasizes quality enhancement and innovative development, the importance of learning spaces is being recognized. The swift progress in information technology has also significantly altered learning methodologies, with informal learning becoming increasingly vital. The variety of spatial types and the intricate factors influencing informal learning spaces (ILS) can often result in interference among different elements, thereby undoubtedly escalating the complexity of research. The ILS within libraries has emerged as a research subject due to its exemplariness. To address this challenge, our study employed ultra-wideband (UWB) indoor positioning technology to conduct comprehensive research. This was achieved by gathering and analyzing data related to the indoor physical environment and user seating preferences, among other factors. We then established correlations between different data sets, considering the dimensions of time and space. This study constructed a multi-factor model influencing ILS and calculated the proportions of four decision-making elements using a comprehensive approach that included SPSS analysis and comparative scoring. Among these, physical elements topped the list, accounting for nearly 40% of student preferences. Following that, facilities elements contributed to over 20% of preferences. Ontology elements (macro layout, functional zoning, and form composition) explained over 15% of preferences, and lastly, atmosphere elements (> 10%). The study identified 7 key bottom-level indicators and their respective weights for each ILS type, and proposed improvement methods for them, aiming to offer reference and support for future ILS design.

Indexed as

High-precision positioning technologyInformal learning space (ILS)Learning behaviorOptimization and improvementSpace environment quality

Identifiers

PMID41965899
PMCPMC13230572

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.