Optimal Design and Verification of Informal Learning Spaces (ILS) in Chinese Universities Based on Visual Perception Analysis
Abstract
:1. Introduction
- To verify the effect of optimal design of ILS on visual perception experiment and quantitative analysis.
- To study the relationship between users’ visual perception and spatial elements for further optimal design methods.
- To propose practical recommendations to enhance the use of ILS in Chinese universities.
2. Methodology
2.1. Litterateurs and Investigation
2.1.1. Litterateurs and Classification
- (1)
- Private/Alone (Individual learning): closed and smaller private spaces with defined boundaries, usually found in the library, with students’ static behaviors.
- (2)
- Private/ Shared (Learning in groups): private spaces with defined boundaries and moderate scale, usually located inside academic buildings or libraries, with student groups’ relatively dynamic learning behaviors.
- (3)
- Public/Alone (Learning alone in the crowd): open spaces usually located in public areas, such as study rooms in libraries, gray spaces, etc., with students learning behaviors changing according to the atmosphere of the surroundings.
- (4)
- Public/Shared (Group learning): open and more functionally complex spaces usually located in cafes, normally with student groups’ dynamic learning behaviors, and some of them also connect with outdoor spaces.
2.1.2. Field Investigation
2.1.3. Spatial Elements Study
- (1)
- Physical Space: the material elements of space, including the location, size, enclosure, material, color, etc.
- (2)
- Facilities: the elements to help with space functions, often in the form of furniture and equipment, and linked with users’ behavior.
- (3)
- Environment: Including landscape and the physical environment. The landscape environment emphasizes the natural and artificial environments both indoor and outdoor. The physical environment refers to the thermal, optical, and acoustic environment, etc., influenced by material elements. Environment analysis based on visual perception in this study involves only the landscape environment.
2.2. Case Studies and Optimal Design
2.2.1. Case Studies
- (1)
- The first-floor cafe (Public/Shared):
- (2)
- The third-floor terrace (Public/Alone):
2.2.2. Optimal Design
2.3. Visual Perception Experiment
2.3.1. Data Collection and Processing
- (1)
- Collecting the typical images before and after optimal design in the case studies.
- (2)
- Making visual perception experiments by utilizing Tobii Pro Lab for quantitative analysis of the perception data of subjects before and after optimal design. Enough valid visual perception data are collected with a wearable eye-tracker and various indexes are selected.
- (3)
- Comparing the visualization results before and after the optimal design by the processing and analysis of eye-tracking data.
2.3.2. Visual Perception Experiment
- (1)
- Subject setting: refer to previous relevant experiment procedures, this experiment was performed a total of 18 times. According to the criteria with a sampling rate greater than or equal to 80% [24], finally 11 sets of valid data were obtained from six subjects.
- (2)
- Stimulus material: images of ILS before and after optimization.
- (3)
- Experiment procedure:
2.3.3. Eye-Tracking Index Selection
3. Results and Discussion
3.1. Optimal Design
3.1.1. Case 1: The First-Floor Cafe (Public/Shared)
3.1.2. Case 2: The Third-Floor Outdoor Terrace (Public/Alone)
3.2. Visual Perception Analysis and Verification
3.2.1. Case 1: The First-Floor Cafe (Public/Shared) for Open Group Learning
- (1)
- Visual observation (TFD, AFD, and TFF)
- (2)
- Visual search (TGD, GC, and AGD)
3.2.2. Case 2: Public/Alone: Learning Alone in the Crowd: The Third-Floor Terrace
- (1)
- Visual observation (TFD, AFD, and TFF)
- (2)
- Visual search (TGD, GC, and AGD)
4. Conclusions
- (1)
- For the optimization of ILS in universities, it is worth paying attention to the physical space, such as size, enclosure, richness, transparency, and other elements of the space. For example, the facade can be homogenized to lower the impact of visual interference of facade information to users. Removing the unrelating elements on facades can reduce the excessive visual attention of users. Hence, if the facade applies the materials with complicated context, it should be placed in a larger space to make the space more identified. Besides, adjusting the colors and materials on the facade is suitable to establish an active and vivid space. It is also helpful to optimize facilities’ number, location, and accessibility. Besides, facilities can be combined with landscape elements to create a natural atmosphere.
- (2)
- Visual perception experiments and quantitative analysis demonstrate that the quality of ILS after optimization is much improved. Comparisons of both visual observation factors (TFD, AFD, and TFF) and visual search factors (TGD, GC, and AGD) in the two case studies show that optimized space (exterior space with natural landscape) and facilities were more attractive.
- (1)
- It is also necessary to increase the sample size of subjects and to study two other types of spaces, namely the spaces of Private/Alone (Individual learning), and Private/Shared (Group learning), to further refine the experimental design and overall study content.
- (2)
- The verifying process can be supplemented with questionnaires and other methods to better understand users’ feelings.
- (3)
- The perceptual data in this study, especially for human visual perception, need more technical means and more types of human perceptual data, such as EEG, to obtain more comprehensive and integrated analysis results.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Eye-Tracking Concepts and Indicators | Indicator Abbreviations | Definition | Meaning | |
---|---|---|---|---|
Visual observation | The total fixation duration | TFD | Total time spent gazing within a given AOI | The longer the time, the more attractive the AOI is |
The average fixation duration | AFD | The average duration of all gazes within a given AOI | The longer the time, the more informative or difficult the AOI is to understand, or the more attractive the AOI is | |
The time to the first fixation | TFF | The time from the beginning of a time interval to a fixation on the first AOI | The shorter the time, the easier the AOI is to engage the subject at the beginning | |
Visual search | The total glance duration | TGD | The duration between the end of the last fixation before entering an AOI (including the entry saccade 2) and the end of the last fixation within that AOI (before the exit saccade 3) | The longer the time, the richer the information in the AOI is, the more difficult it is to be understood, or the higher the relevance of all parts in the AOI is |
The average Glance Duration | AGD | The average duration of all sweeps within a given AOI | The longer the time, the more invalid the search of the AOI is | |
The glance count | GC | Number of sweeps for an AOI occurring in a time interval | The higher the number, the longer the subject’s search process on that AOI |
TFD | AFD |
TFF | TGD |
GC | AGD |
TFD | AFD |
TFF | TGD |
GC | AGD |
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Chen, Y.; Wu, J.; Zou, Y.; Dong, W.; Zhou, X. Optimal Design and Verification of Informal Learning Spaces (ILS) in Chinese Universities Based on Visual Perception Analysis. Buildings 2022, 12, 1495. https://0-doi-org.brum.beds.ac.uk/10.3390/buildings12101495
Chen Y, Wu J, Zou Y, Dong W, Zhou X. Optimal Design and Verification of Informal Learning Spaces (ILS) in Chinese Universities Based on Visual Perception Analysis. Buildings. 2022; 12(10):1495. https://0-doi-org.brum.beds.ac.uk/10.3390/buildings12101495
Chicago/Turabian StyleChen, Yuzhen, Jinxiu Wu, Yamei Zou, Wei Dong, and Xin Zhou. 2022. "Optimal Design and Verification of Informal Learning Spaces (ILS) in Chinese Universities Based on Visual Perception Analysis" Buildings 12, no. 10: 1495. https://0-doi-org.brum.beds.ac.uk/10.3390/buildings12101495