Reframing Sustainable Informal Learning Environments: Integrating Multi-Domain Environmental Elements, Spatial Usage Patterns, and Student Experience
Abstract
1. Introduction
- 1.
- Which multi-domain indoor environmental and supportive elements within ILS collaboratively influence student experience (SE) and student satisfaction (SS) in university academic buildings?1.1. How can the key elements of ILS that impact SE outcomes be identified?1.2. How can these elements be continuously evaluated through SS?
- 2.
- How does the SE process affect the construction quality of ILS (CQILS) in university academic buildings?2.1. How does the frequency of potential space use (FPS) by students affect CQILS?2.2. Based on the FPS, how do ways in which students use space (WUS) influence CQILS?
2. Materials and Methods
2.1. Material
- Comprehensive coverage: The selected buildings include general academic buildings and specialized buildings from the humanities, sciences, and arts.
- Representative design: All five buildings were either newly constructed or renovated between 2019 and 2021, making them the most recent facilities available at the time of the fieldwork in 2022. The ILSs were designed as part of the original building plan or optimized during renovation. These buildings offer more modern and innovative designs than traditional academic buildings, reflecting the latest trends in ILS design at these two leading universities. For ease of reference, the five buildings have been numbered, as shown in Table 1.
2.2. Measures
2.2.1. Space–SE
Key Factors of Informal Learning Spaces | Literature Sources |
---|---|
Indoor Environmental Comfort (Lighting, Ventilation, Noise, Temperature, Material, Color, Furniture Comfort) | [10,39,40,41,42,43,44,45,46] |
Flexibility and Adaptability of Spatial Layout (Furniture Flexibility, Spatial Diversity, Openness and Privacy) | [9,10,34,39,40,41,42,46,47] |
Spatial Autonomy and Sense of Belonging (Autonomy, Belonging, Interactivity, Sense of Community) | [5,39,40,41,42,48] |
Availability of Supporting Facilities (WIFI, Power Outlets, Food and Beverages, Whiteboards, Computers) | [5,10,39,40,41,42,47,49] |
2.2.2. SE–Space
- Frequency of Potential Space Use: The FPS can be objectively measured using spatial syntactic analysis (SSA). SSA is a tool widely applied in urban planning and architectural studies to analyze spatial configurations and their impact on human behavior. One of its core metrics is integration, which describes the connectivity or accessibility of a given location in relation to other locations. Areas with higher integration are generally easier to access and, thus, more likely to become frequently used spaces by students. By measuring both global and local integration, SSA can reveal spatial characteristics within academic buildings, helping to identify areas more likely to be used by students. These data provide key quantitative support for understanding the FPS [50,51,52,53].
- Ways in which Students Use Space: WUS is determined through field observations, where observers record whether students are engaged in individual study, group collaboration, or both, in the ILSs of each academic building.
- Construction Quality of ILS: The CQILS in academic buildings is assessed using the results of PCA from the initial questionnaire. Each building’s CQILS scores are determined by averaging ratings from two independent observers on a scale of 1 to 5, where 1 represents very poor conditions and 5 represents excellent conditions.
2.2.3. Procedure
2.2.4. Analysis
- Analysis of Space–SE
- Analysis of SE–Space
3. Results
3.1. Space–SE
3.1.1. Identification of Multi-Domian Indoor Environmental and Supportive Elements Affecting SE
- Reliability Analysis of the Questionnaire
- Validity Analysis and Factor Analysis on Factors Influencing SE
3.1.2. Assessment of ILS Factors Affecting SE Based on SS
3.1.3. Summary of Results in Space–SE
- PCA revealed five dimensions influencing SE, highlighting complex interactions between multi-domain indoor environmental and supportive elements within ILS.
- SAFF and SSSA do not have a significant impact on SS.
- The three key factors—SDO, ATCLA, and LVWV—were found to significantly influence SS (see Figure 3).
3.2. SE–Space
3.2.1. ILSs Screening Through Integration Scores (FPS) for Each Building
3.2.2. Comparative Analysis of CQILS Based on FPS
- W1 (LVWV): High-FPS ILSs averaged 3.7, while low-FPS ILSs averaged 4.3.
- W2 (ATCLA): High-FPS ILSs averaged 2.4, compared to 3.3 for low-FPS ILSs.
- W3 (SDO): High-FPS ILSs averaged 2.5, compared to 3.0 for low-FPS ILSs.
- W4 (SAFF): High-FPS ILSs averaged 3.3, while low-FPS ILSs averaged 3.9.
- W5 (SSSA): High-FPS ILSs averaged 1.3, slightly lower than 1.7 for low-FPS ILSs, with overall low scores observed in both categories.
- W6 (CQILS): The average score for high-FPS ILSs was 2.6, while low-FPS ILSs averaged 3.2.
3.2.3. Comparative Analysis of WUS Based on FPS and CQILS
- Group Collaboration vs. Individual Learning—High-FPS ILS
- Group Collaboration vs. Individual Learning—Low-FPS ILS
- High FPS vs. Low FPS—Group Collaboration ILS
- High FPS vs. Low FPS—Individual Learning ILS
- Spontaneously Formed ILS
3.2.4. Summary of Results for SE–Space
- ILSs with higher FPS tend to have lower CQILS compared to those with lower FPS.
- Group collaboration spaces generally exhibit better CQILS than individual learning spaces, and individual learning spaces exhibit a larger range of score fluctuations.
- ILSs that are spontaneously formed by students tend to have lower CQILS.
4. Discussion
- 1.
- PCA revealed five dimensions influencing SE, highlighting complex interactions between multi-domain indoor environmental and supportive elements within ILS.
- 2.
- SAFF and SSSA do not have a significant impact on SS.
- 3.
- The three key factors—SDO, ATCLA, and LVWV—were found to significantly influence SS.
- 4.
- ILSs with higher FPS tend to have lower CQILS compared to those with lower FPS.
- 5.
- Group collaboration spaces generally exhibit better CQILS than individual learning spaces, and individual learning spaces exhibit a larger range of score fluctuations.
- 6.
- ILSs that are spontaneously formed by students tend to have lower CQILS.
4.1. Space–SE
- Result 1: PCA revealed five dimensions influencing SE, highlighting complex interactions between multi-domain indoor environmental and supportive elements within ILSs
- Result 2: SAFF and SSSA do not have a significant impact on SS
- Result 3: SDO, ATCLA, and LVWV were found to significantly influence SS
4.2. SE–Space
- Result 4: ILSs with higher FPS tend to have lower CQILS compared to those with lower FPS
- Result 5: Group collaboration spaces generally exhibit better CQILS than individual learning spaces, and individual learning spaces exhibit a larger range of score fluctuations
- Result 6: ILSs that are spontaneously formed by students tend to have lower CQILS
4.3. Actionable Recommendations for Campus Planners and Designers
- Recognize the multidimensional nature of student experience (SE): Planning and design of ILS should account for the interplay between environmental and supportive elements—including layout, furniture, and atmosphere—as SE is not shaped by single factors alone. (Related to Result 1)
- Focus investment on elements with the greatest impact: Prioritize interventions targeting spatial diversity and openness (SDO), thermal/acoustic comfort and atmosphere (ATCLA), and lighting/ventilation/window views (LVWV), as these dimensions most significantly influence student satisfaction. (Related to Result 3)
- Avoid overinvestment in underperforming elements: While supportive facilities and spatial autonomy (SAFF and SSSA) are often highlighted in the literature, they may not always align with students’ actual priorities or satisfaction outcomes. Design resources should be reallocated accordingly. (Related to Result 2)
- Reinforce the quality of highly used spaces: Since high-FPS ILSs tend to have lower perceived quality, these should become focal points for environmental and spatial enhancement to maximize their impact. (Related to Result 4)
- Support both formal and spontaneous spatial formations: Planners should not only design for structured collaboration and individual learning but also empower students to appropriate and adapt informal spaces. This includes providing movable furniture and environmental support to strengthen CQILS in self-initiated ILSs. (Related to Result 5 & Result 6)
5. Future Research Implications
5.1. Deepening the Quantification of Student Experience: Beyond Generalized Models to Multidimensional Research
5.2. Acoustic and Thermal Control and Learning Atmosphere: Combined Effects of Multi-Domain Physical Environmental and Supportive Elements on Student Experience
5.3. Window Views, Daylighting, Ventilation, and Spatial Diversity: Combined Effects and Pathways to Enhance Student Experience and Sustainable Design
5.4. Spontaneous Use and Dynamic Utilization of Informal Learning Spaces
6. Cross-Cultural Comparisons Limitations
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Glossary
SE | Student Experience: the overall process of perception, interaction, and |
co-creation between a particular student or group of students and a | |
space or spaces within a higher education setting. | |
SS | Student Satisfaction: a measure of how well students’ |
expectations and needs are met within the learning environment. | |
Space–SE | Student Experience of Space: how the multi-domain |
environmental and supportive elements of a space (e.g., layout, | |
facilities, atmosphere) influence SE and SS. | |
SE–Space | Space of Student Experience: how students shape and define |
space through their usage patterns and learning activities. | |
Focuses on FPS and WUS in this study. | |
ILS | Informal Learning Space: any space outside the formal classroom that |
is used for knowledge sharing and learning activities. | |
FPS | Frequency of Potential Space Use: refers to how often students |
might utilize a particular space, based on SSA. | |
WUS | Ways in which Students Use Space: the different modes of |
interaction students have with a learning space, including | |
group work, individual study, or relaxation. | |
SSA | Spatial Syntactic Analysis: a method for analyzing spatial configurations |
and their influence on human behavior, focusing on metrics | |
like integration to assess accessibility and connectivity. Provides | |
quantitative support for understanding FPS. | |
CQILS | Construction Quality of Informal Learning Space: assessed based on |
a five-part framework from principal component analysis with | |
ratings from 1 to 5, where 1 is poor and 5 is excellent. | |
From Literature Review | |
(Analytical Framework): | |
IEC | Indoor Environmental Comfort: refers to how comfortable |
the multi-domain indoor environmental elements are, | |
including temperature, lighting, sound, and air quality. | |
FASL | Flexibility and Adaptability of Spatial Layout: describes how |
easily the layout of a space can be adjusted to support differen | |
activities like individual study or group work. | |
ASF | Availability of Supporting Facilities: refers to the presence |
of key resources, such as power outlets, Wi-Fi, and seating, | |
that aid learning activities. | |
SASB | Spatial Autonomy and Sense of Belonging: reflects how much |
control students have over a space and the sense of connection. | |
they feel toward it. | |
From Questionnaire | |
Analysis (Principal | |
Component Analysis): | |
SSSA | Supporting Services and Spatial Autonomy: focuses on |
available services and student control over space. | |
SAFF | Spatial Availability and Furniture Flexibility: refers to space |
availability and adaptable, comfortable furniture. | |
LVWV | Lighting, Ventilation, and Window View: concerns lighting |
quality, air flow, and visibility. | |
ATCLA | Acoustic and Thermal Control and Learning Atmosphere: covers |
sound, temperature control, and conducive learning environments. | |
SDO | Spatial Diversity and Openness: reflects space variety and |
openness for different learning experiences. |
Appendix A. Spatial Syntactic Analysis Progress
Step | Description | Software and Format |
---|---|---|
1. Creating Floor Plans | Floor plans of the five academic buildings were drawn with attention to detail. Saved for later use. | AutoCAD (.dwg) |
2. Dividing Convex Spaces | Research areas were divided into convex spaces defined by walls and partitions. Saved for import. | AutoCAD (.dxf) |
3. Generating Convex Space Maps | .dxf files were imported into depthmapX to generate maps with nodes and connecting pathways. | depthmapX |
4. Calculating Integration | Analyzed the convex space maps to compute Global Integration (HH) and Local Integration (HHR3, HHR5). | depthmapX |
5. Exporting Integration Data | Integration metrics for each building were exported for further analysis. | depthmapX |
Appendix B. SPSS Analysis Results and Questionnaire
Component | |||||
---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | |
Availability of printers, copiers, etc. | 0.795 | ||||
Availability of computers, projectors, whiteboards, etc. | 0.767 | ||||
Availability of beverages and light snacks | 0.699 | ||||
Adjustable lighting, temperature, and ventilation | 0.561 | ||||
Adjustable sound and external visibility | 0.514 | ||||
High comfort of furniture | 0.689 | ||||
Sufficient number of furniture | 0.609 | ||||
High flexibility of furniture for easy rearrangement | 0.605 | ||||
Refined interior design | 0.597 | ||||
High-quality WiFi signal | 0.584 | ||||
Sufficient spaciousness | 0.532 | ||||
Sufficient power outlets | 0.512 | ||||
Adequate natural lighting | 0.803 | ||||
Spacious window view | 0.802 | ||||
Good ventilation | 0.731 | ||||
Appropriate artificial lighting | 0.636 | ||||
Low noise level | 0.763 | ||||
Suitable temperature | 0.721 | ||||
Strong learning atmosphere | 0.646 | ||||
Adequate openness | 0.791 | ||||
Diverse spatial types | 0.701 | ||||
Positive communication atmosphere | 0.584 | ||||
High level of privacy | 0.501 |
Model | Unstandardized Coefficients | Standard Coefficients | t | Sig. | Collinearity | |
---|---|---|---|---|---|---|
B | Standard Error | Beta | VIF | |||
(Constant) | 0.378 | 0.183 | 2.064 | 0.039 * | ||
Independent Variables | ||||||
SSSA | 0.022 | 0.043 | 0.020 | 0.506 | 0.613 | 2.741 |
SAFF | 0.086 | 0.073 | 0.068 | 1.182 | 0.238 | 5.584 |
LVWV | 0.160 | 0.044 | 0.137 | 3.618 | 0.000 *** | 2.416 |
ATCLA | 0.313 | 0.049 | 0.275 | 6.349 | 0.000 *** | 3.143 |
SDO | 0.370 | 0.057 | 0.319 | 6.536 | 0.000 *** | 3.989 |
Control Variables | ||||||
Teaching Building | ||||||
A-1 | 0.029 | 0.079 | 0.013 | 0.367 | 0.714 | 2.110 |
A-2 | −0.396 | 0.098 | −0.175 | −4.042 | 0.000 *** | 3.159 |
A-3 | −0.372 | 0.100 | −0.156 | −3.739 | 0.000 *** | 2.934 |
B-1 | −0.156 | 0.088 | −0.067 | −1.778 | 0.076 | 2.353 |
B-2 | 0 | |||||
Gender | ||||||
Male | −0.167 | 0.047 | −0.091 | −3.508 | 0.000 *** | 1.117 |
Female | 0 | |||||
Major | ||||||
Philosophy, Economics, Law | 0.069 | 0.082 | 0.030 | 0.849 | 0.396 | 2.146 |
Military Science, Management, Arts | 0.219 | 0.113 | 0.059 | 1.935 | 0.053 | 1.549 |
Science, Engineering, Agriculture, Medicine | 0.138 | 0.084 | 0.074 | 1.644 | 0.101 | 3.372 |
Education, Literature, History | 0 | |||||
Usage Frequency | ||||||
Daily | −0.158 | 0.092 | −0.069 | −1.717 | 0.086 | 2.747 |
Weekly | −0.240 | 0.088 | −0.129 | −2.735 | 0.006 ** | 3.714 |
Monthly | −0.232 | 0.097 | −0.096 | −2.392 | 0.017 * | 2.711 |
Quarterly | −0.107 | 0.114 | −0.030 | −0.943 | 0.346 | 1.753 |
Rarely | 0 | |||||
Duration of Use | ||||||
Less than 30 min | 0.208 | 0.087 | 0.086 | 2.399 | 0.017 * | 2.176 |
30 min–1 h | 0.066 | 0.073 | 0.028 | 0.905 | 0.366 | 1.651 |
1 h–3 h | 0.129 | 0.061 | 0.068 | 2.109 | 0.035 * | 1.769 |
More than 3 h | 0 |
Model | Sum of Squares | df | Mean Square | F | Sig. | |
---|---|---|---|---|---|---|
1 | Regression | 353.470 | 20 | 17.674 | 49.680 | 0.000 |
Residual | 243.330 | 684 | 0.356 | |||
Total | 596.800 | 704 |
Appendix C. ILS Distribution and SSA Data
Appendix D. CQILS of Each Selected ILS
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School | Huazhong University of Science and Technology (HUST) | ||
Code | A-1 | A-2 | A-3 |
Type | Public Teaching Building | Science and Engineering + Arts | Science and Engineering |
Name | Yifu Teaching Building (East Ninth Building) Source: https://www.hust.edu.cn/zjhkd/xyfg/jzdb.htm (accessed on 9 June 2025) | Caijian Building (School of Architecture and Urban Planning) Source: http://jjc.hust.edu.cn/ (accessed on 9 June 2025) | Faculty of Optics and Electronic Information Building Source: https://www.hust.edu.cn/zjhkd/xyfg/jzdb.htm (accessed on 9 June 2025) |
School | Wuhan University (WHU) | ||
Code | B-1 | B-2 | |
Type | Public Teaching Building | Humanities | |
Name | Main Teaching Building (Engineering Department) Source: https://edf.whu.edu.cn/info/1336/3601.htm (accessed on 9 June 2025) | Zhenhua Building (Comprehensive Building of Liberal Arts) Source: https://philosophy.whu.edu.cn/info/1468/17154.htm (accessed on 9 June 2025) |
HUST | WHU | Overall | |
---|---|---|---|
Cronbach’s Alpha | 0.909 | 0.928 | 0.919 |
Number of Items | 24 | 24 | 24 |
Number of Cases | 116 | 125 | 241 |
Initial Eigenvalues | Sum of Squared Loadings for Extraction | Sum of Squared Loadings for Rotation | |||||||
---|---|---|---|---|---|---|---|---|---|
Com. | Tot. | Var.% | Cum.% | Tot. | Var.% | Cum.% | Tot. | Var.% | Cum.% |
1 | 8.402 | 36.530 | 36.530 | 8.402 | 36.530 | 36.530 | 3.265 | 14.194 | 14.194 |
2 | 2.054 | 8.932 | 45.462 | 2.054 | 8.932 | 45.462 | 3.250 | 14.129 | 28.323 |
3 | 1.482 | 6.443 | 51.905 | 1.482 | 6.443 | 51.905 | 2.902 | 12.619 | 40.942 |
4 | 1.351 | 5.874 | 57.779 | 1.351 | 5.874 | 57.779 | 2.651 | 11.524 | 52.467 |
5 | 1.201 | 5.220 | 62.999 | 1.201 | 5.220 | 62.999 | 2.422 | 10.532 | 62.999 |
HUST | WHU | Overall | ||||
---|---|---|---|---|---|---|
A-1 | A-2 | A-3 | B-1 | B-2 | ||
KMO Value | 0.925 | 0.785 | 0.813 | 0.745 | 0.898 | 0.927 |
Bartlett’s Test Sig. | 0.000 *** | 0.000 *** | 0.000 *** | 0.000 *** | 0.000 *** | 0.000 *** |
Model | R | R Square | Adjusted R Square | Errors in Standard Estimates |
---|---|---|---|---|
1 | 0.770 | 0.592 | 0.580 | 0.596 |
Building | Global Integration (HH) | Local Integration (HHR3) | Local Integration (HHR5) | High Integration ILS | Low Integration ILS |
---|---|---|---|---|---|
A-1 | 0.607–1.077 | 0.690–7.795 | 0.896–2.110 | 3F-4, 4F-5 | 2F-3 |
A-2 | 0.492–1.412 | 0.333–4.046 | 0.422–2.042 | 4F-6, 4F-7, 5F-10 | 1F-1-1, 1F-1-2, 1F-1-3, 2F-4-1, 2F-4-2, 3F-5-1, 3F-5-2, 3F-5-3 |
A-3 | 0.129–1.007 | 0.333–8.216 | 0.458–2.190 | 4F-3, 5F-3, 5F-12, 5F-16, 5F-20, 6F-14, 6F-17, 6F-21, 7F-13, 7F-18, 7F-22, 8F-19 | 2F-2 |
B-1 | 0.685–1.219 | 0.637–2.703 | 0.726–1.681 | 2F-1, 2F-2, 4F-6 | |
B-2 | 0.443–1.062 | 0.333–2.945 | 0.574–1.667 | 2F-4, 3F-5, 3F-6, 3F-7, 3F-8, 3F-9, 4F-5, 4F-7, 4F-8, 4F-9, 4F-10, 4F-11, 5F-8, 5F-11 | 5F-17, 6F-18 |
Building | ILS Number | FPS Category | WUS | CQILS Category |
---|---|---|---|---|
A-1 | 3F-4 | High | Individual | High (3.1) |
A-2 | 4F-6 | High | Group | High (3.7) |
A-2 | 5F-10 | High | Individual | High (3.0) |
A-3 | 5F-20 | High | Individual | High (3.4) |
A-3 | 6F-14 | High | Individual | High (3.6) |
B-2 | 3F-6 | High | Individual | High (3.5) |
A-2 | 4F-7 | High | Individual | Low (2.1) |
A-3 | 4F-3/5F-3 | High | Spontaneously Formed | Low (2.0) |
A-3 | 7F-13 | High | Individual | Low (2.2) |
B-1 | 2F-1/2F-2 | High | Individual | Low (2.1) |
B-1 | 4F-6 | High | Individual | Low (2.4) |
B-2 | 2F-4 | High | Spontaneously Formed | Low (1.8) |
B-2 | 4F-10 | High | Spontaneously Formed | Low (2.1) |
B-2 | 4F-11/5F-11 | High | Individual | Low (2.2) |
A-1 | 1F-1-1 | Low | Individual | High (3.4) |
A-2 | 3F-5-1 | Low | Group | High (4.2) |
A-2 | 3F-5-2 | Low | Group | High (4.3) |
B-2 | 5F-17 | Low | Individual | High (3.7) |
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Yin, J.; Fan, W.; Peng, L. Reframing Sustainable Informal Learning Environments: Integrating Multi-Domain Environmental Elements, Spatial Usage Patterns, and Student Experience. Buildings 2025, 15, 2203. https://doi.org/10.3390/buildings15132203
Yin J, Fan W, Peng L. Reframing Sustainable Informal Learning Environments: Integrating Multi-Domain Environmental Elements, Spatial Usage Patterns, and Student Experience. Buildings. 2025; 15(13):2203. https://doi.org/10.3390/buildings15132203
Chicago/Turabian StyleYin, Jiachen, Wenyi Fan, and Lei Peng. 2025. "Reframing Sustainable Informal Learning Environments: Integrating Multi-Domain Environmental Elements, Spatial Usage Patterns, and Student Experience" Buildings 15, no. 13: 2203. https://doi.org/10.3390/buildings15132203
APA StyleYin, J., Fan, W., & Peng, L. (2025). Reframing Sustainable Informal Learning Environments: Integrating Multi-Domain Environmental Elements, Spatial Usage Patterns, and Student Experience. Buildings, 15(13), 2203. https://doi.org/10.3390/buildings15132203