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The purpose of this paper is to identify the role of spatial intelligence in predicting the web information searching behavior and performance of high school students.

The population of this study consisted of all female students who were studying in the eighth and ninth grades of Shiraz University School. The students searched an educational website by using keywords to find some answers to two tasks (one simple task and one complex task).

The findings of this study revealed that among the five components of spatial intelligence, the three components of spatial orientation, perceptual speed and flexibility of closure could better predict the web searching behavior and performance of the students. To elaborate, spatial orientation could predict the query length and the search time; perceptual speed was helpful in predicting the reformulation of the question, the number of results pages observed, the number of links viewed, and success; flexibility of closure also predicted the success in the search.

The results of this study can help researchers and others understand the searching behavior and performance of children and the cognitive factors which affect them. The results can also help teachers and school librarians to teach students the necessary skills and design interactive systems that take into account adolescents’ different cognitive approaches. While other studies have focused on adults’ and university students’ behavior, this study examined the adolescents’ behavior. Furthermore, although the previous studies have attempted to investigate just one dimension of the searching process, i.e., either behavior or performance, this study simultaneously focused on both dimensions and examined both searching behavior and performance through keyword searching strategy.

This study aims to investigate the differences in spatial ability between students with a math learning disability and their normal peers.

To investigate these differences two groups, (60 students with a math learning disability) and (60 normal students) from fifth grade with a mean age (10.6 years) were administered with spatial ability test along with an IQ test. Students with a math learning disability were chosen using measures of the following: math learning disability questionnaire developed from learning disability evaluation scale – renormed second edition (LDES-R²) (McCarney and Arthaud, 2007) and the Quick Neurological Screening Test (Mutti et al., 2012), in addition to their marks in formal math tests in school.

Comparison between the two groups in four aspects of spatial ability resulted in obvious differences in each aspect of spatial ability (spatial relations, mental rotation, spatial visualization and spatial orientation); these differences were clear, especially in mental rotation and spatial visualization.

This paper contributes to gain more insights into the characteristics of pupils with a math learning disability, the nature of spatial abilities and its effect on a math learning disability. Moreover, the results suggest spatial ability to be an important diagnose factor to distinguish and identify students with a math learning disability, and that spatial ability is strongly relevant to math achievement. The results have significant implications for success in the science, technology, engineering and mathematics domain.

Spatial ability is essential for engineers’ professional performance. Several studies describe it as a skill that can be enhanced using new technologies. Virtual reality (VR) is an emerging technology that is proving very useful for training different skills and improving spatial perception. In this chapter, the authors firstly present some previous works that use VR to train students, mainly in the area of engineering studies, and which demonstrate that VR can improve some aspects of the spatial perception. This study took a group of engineering students who used VR technologies to carry out learning activities designed to improve their spatial perception, which was measured with a widely used spatial ability test. The results obtained confirm that the use of VR technologies can improve students’ spatial perception. This proposal is easily transferable to other educational contexts. On the one hand, it could be implemented to improve spatial ability in other engineering studies, and on the other hand, with simple adaptation, it could be used to enhance other skills.

The purpose of this paper is to provide an insight into the relationship between students’ spatial ability and their university entrance score (Australian Tertiary Admissions Rank [ATAR]). The ATAR provides entry into university studies but does not necessary provide a good measure of students’ spatial skills. Spatial abilities are fundamental to success in many design courses. This paper aims to show whether the ATAR is a good predictor of spatial skills and considers the implications of this.

Students entering university design courses in architecture were tested three times during their first year using a three-dimensional (3D) Ability Test (3DAT), an online psychometric test of 3D spatial ability. The students’ results in 3DAT were then compared to students’ ATAR scores using a Pearson’s correlation test were also conducted to assess the relationship between ATAR and spatial performance.

There was no correlation between ATAR and spatial performance. Therefore, there was no relationship between an individual’s ATAR and their spatial performance upon entering university.

Participants were required to select their ATAR from ranges, i.e. 71-80, 81-90 and 91-100, which meant their exact ATAR was not recorded. This meant that the participants were clustered, making it difficult to establish a linear relationship that was a true reflection of the population.

Initiatives to support students entering design courses may be necessary to compensate for the range of spatial skills students possess when entering university because of their school experiences.

Individuals who have strong spatial skills are able to perform spatial problems faster and more efficiently than those with weak spatial skills. High spatial performance has been shown relate to performance in areas such as mathematics science technology and design.

This paper fulfils the need to better understand the diversity of spatial abilities students have on entering design courses.

This study aims to explore the impact of spatial configuration on behavioral patterns of visitors in the ground floor of health-care spaces.

In this study, the Space Syntax analysis was used to combine visibility graph analysis and axial line analysis with empirical observation of visitors’ activities. Two types of observation methods on visitors were conducted to discover the behavioral patterns of individuals, respectively, named “gate counts” and “people following.”

The outcomes of this research revealed that the spatial arrangements of pathways, public areas, vertical circulations, entrance space, lobby, emergency department, reception desk and pharmacy have a significant influence on the way that visitors perceive the health-care environment.

The current research is limited to two aspects of effective wayfinding (configuration of health care and geometry). Future work can investigate the other potential factors coupled with the current factor as an integrated research for enhancing wayfinding and sustaining accessibility. Another limitation is that the observation results for this study had been conducted before the COVID-19 pandemic and future studies can compare these results with the current COVID-19 situation within health care environments.

A large amount of research has focused on the needs of populations in developed countries. This topic has not been investigated thoroughly by professionals in developing countries such as Iran. Accordingly, this study benefits environmental psychologists and architects by revealing the effective characteristics of legible spaces in health-care environments.

This study aimed at the identification of perceptional environment properties in hospital public spaces that can affect salutogenic components and patients' overall satisfaction and suggested a conceptual framework.

A systematic review focused on specific steps to clarify the impact of public spaces' environmental quality on patients' satisfaction through the salutogenic approach. Searches were conducted in five databases and four scientific journals.

Five perceptional environment components of hospital public spaces: physical–psychological comfort, visibility, accessibility, legibility and relationability can be related to three indicators of salutogenic approach: manageability, perception and meaning and can be evaluated in patients' overall satisfaction: desire to use hospital again, to recommend the hospital to others, to prefer hospital to other healthcare environments and to trust in the hospital.

Despite studies on healthcare environments, there is a lack of research on the salutogenic approach in hospital public spaces. Therefore, this paper focuses on the environmental quality in public spaces as an influence on patients' satisfaction with the salutogenic perspective to create a health-promoting environment.

The purpose of this study is to propose a new method for the end-to-end classification of steel surface defects.

This study proposes an AM-AoN-SNN algorithm, which combines an attention mechanism (AM) with an All-optical Neuron-based spiking neural network (AoN-SNN). The AM enhances network learning and extracts defective features, while the AoN-SNN predicts both the labels of the defects and the final labels of the images. Compared to the conventional Leaky-Integrated and Fire SNN, the AoN-SNN has improved the activation of neurons.

The experimental findings on Northeast University (NEU)-CLS demonstrate that the proposed neural network detection approach outperforms other methods. Furthermore, the network’s effectiveness was tested, and the results indicate that the proposed method can achieve high detection accuracy and strong anti-interference capabilities while maintaining a basic structure.

This study introduces a novel approach to classifying steel surface defects using a combination of a shallow AoN-SNN and a hybrid AM with different network architectures. The proposed method is the first study of SNN networks applied to this task.