Next Article in Journal
Dynamic Characteristics Analysis and Finite Element Simulation of an Ancient Timber Building under Environmental Excitation
Next Article in Special Issue
Experimental Evaluation of Noise Exposure Effects on Subjective Perceptions and Cognitive Performance
Previous Article in Journal
A New Macro-Element for Predicting the Behavior of Masonry Structures under In-Plane Cyclic Loading
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Buildings
Volume 14
Issue 3
10.3390/buildings14030770
buildings-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Differences between Daylighting and Electric Lighting in Affective Response

by
Nuria Castilla
1,*,
Vicente Blanca-Giménez
2,
Carlos Pérez-Carramiñana
3 and
Carmen Llinares
4
1
Centro de Investigación de Tecnología de la Edificación (CITE), Universitat Politècnica de València, Camino de Vera, s/n, 46022 Valencia, Spain
2
Instituto Universitario de Conservación y Mejora de la Agrodiversidad Valenciana, Universitat Politècnica de València, Camino de Vera, s/n, 46022 Valencia, Spain
3
Departamento de Construcciones Arquitectónicas, Escuela Politécnica Superior, Universidad de Alicante, 03690 Alicante, Spain
4
Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano (HUMAN-Tech), Universitat Politècnica de València, Camino de Vera, s/n, 46022 Valencia, Spain
*
Author to whom correspondence should be addressed.
Buildings 2024, 14(3), 770; https://doi.org/10.3390/buildings14030770
Submission received: 11 February 2024 / Revised: 4 March 2024 / Accepted: 8 March 2024 / Published: 12 March 2024
(This article belongs to the Special Issue Effect of Indoor Environment Quality on Human Comfort)
Browse Figures
Versions Notes

Abstract

:
Humans are spending more time indoors than ever due to urbanisation and industrialisation, leading to higher electricity consumption in lighting systems. Recent research has demonstrated the significance of maintaining a balance between daylight and electric light to create an ideal learning environment that can significantly impact students’ academic performance. The objective of this study is to analyse the changes in students’ emotional response depending on the type of lighting in the classroom—whether it is daylight, electric light, or a combination of both. A field study was conducted with 521 university students to assess their affective response to the lighting environment inside their classroom. The results show that students prefer a Clear-efficient lighting environment for writing–reading tasks and a Soft-calm atmosphere for using electronic devices. For the paying attention tasks, a combination of daylighting and electric lighting is determined to be the best solution, while for the tasks of discussing–teamwork, students prefer daylighting. Daylighting is found to be the only lighting option that students like. Despite this, students still consider electric lighting and the combination of daylight and electric light adequate for a classroom. The findings of this study may help educators and designers create learning spaces that promote a positive and stimulating student environment by understanding the relationship between the lighting environment and students’ affective responses.

1. Introduction

Due to urbanisation and industrialisation, humans are increasingly spending a more significant proportion of their time inside buildings than ever before [1]. This trend has been attributed to the proliferation of urban settlements and the emergence of industries, which have considerably altered modern societies’ physical and social landscape. Although daylight exposure is still present in humans’ daily lives, many people have decreased their time under its influence [2]. Research indicates that up to 90% of people’s time is spent inside buildings [3,4]. As a result, even in areas with ample daylight, people are exposed to electric light for extended periods and this source of light has become a crucial component of their daily routines [1]. The student population is no exception to this rule. Students are often referred to as “indoor species” due to the significant amount of time they spend within educational institutions, which accounts for approximately one-third of their time [5].
The adoption of these indoor habits is causing a notable surge in electricity usage, particularly in lighting systems, which consume around 19% of the total energy consumed in buildings [6]. Nevertheless, reducing the energy demand in buildings is crucial due to the existing worldwide energy crisis. Regarding energy consumption, university campuses present an even more complex situation. Universities require a substantial amount of energy to maintain a comfortable environment for their students, faculty, and staff [7]. Consequently, research has revealed that campuses consume significantly more energy per unit area than residential buildings, ranging from 5 to 10 times higher [8,9,10,11]. This critical waste of energy highlights the need for educational facilities to adopt energy-efficient strategies to reduce emissions [12]. Moreover, higher education institutions are essential in creating a sustainable future [13]. Incorporating sustainably designed or green buildings can promote awareness and serve as a model for communities [14]. Additionally, universities can aid governments in reaching emission reduction goals by researching sustainable practices [15]. Consequently, to successfully implement sustainable practices on campus, universities must prioritise reducing resource and energy consumption [16,17].
At university campuses, lighting accounts for almost one-fifth of the energy consumed [18]. This waste of energy presents a significant challenge in designing classroom lighting systems that balance energy efficiency and optimal learning conditions [19]. Given its crucial role in human live, indoor lighting design has become essential to educational building design [20]. In fact, light has long been recognised as fundamental in creating a conducive learning environment [21] and is known to play a critical role in educational centres [22]. It is well known that the aesthetics and psychological aspects of learning environments are heavily influenced by lighting, but its significance extends beyond basic visibility [23]. Of course, adequate lighting facilitates optimal visual comfort [24], but improved indoor lighting quality has also been linked to increased well-being [25,26], health [27,28,29], alertness [30,31], productivity [32,33], and cognitive performance [33,34,35] among students. Therefore, designing good classroom lighting is a crucial and complex challenge for designers. Moreover, incorporating daylight illumination into classrooms increases student satisfaction [36] and helps to conserve energy [37,38,39]. Nevertheless, daylight cannot be present throughout the entire teaching day due to several factors, such as weather conditions, time of day, and building design. Therefore, electric light must supplement daylight to ensure that adequate illumination is provided in classrooms. In this sense, whether through daylight or electric light, both must be thoughtfully considered and optimised to establish an environment that facilitates successful learning and education. Proper consideration and optimisation of daylight and electric lighting are essential in creating an environment that fosters effective teaching.
Regarding daylighting, numerous studies have consistently shown the importance of natural light in classrooms [40,41,42,43,44,45,46,47,48,49,50]. Daylight positively impacts students’ cognitive performance [51,52], and enhances their connection to the outdoors [53]. Plympton [54] even found that students in university classrooms with greater exposure to daylight made more progress than those with less natural light. Daylight also provides various health benefits, such as regulating the body’s circadian rhythm [55] and improving health [56], mood [57], and well-being [58]. Conversely, inadequate provision of daylight can lead to adverse effects such as fatigue [39], stress [56], eye strain [59], circadian phase shifting [60], and the advance of Seasonal Affective Disorder (SAD) [61]. Daylighting is essential to creating optimal learning environments that enhance sustainability, while minimising the potential harmful effects of electric lighting [59,62,63]. Nowadays, it is widely acknowledged that daylighting is the most optimal lighting solution, and when utilised correctly, it can significantly decrease energy consumption and maintenance expenses [64].
Electric lighting is also crucial in learning environments for several reasons. Electric lighting enables extended hours of learning, but it also can adapt to varying weather conditions and enhance the accessibility of visual aids. Moreover, it facilitates diverse learning activities and integrates the lighting environment with the use of technology. As a result, electric lighting ensures the safety and security of the learning environment, affords architectural flexibility, and provides consistent and reliable illumination. Apart from complying with regulations and normative standards [65], electric lighting in educational environments has also been the subject of numerous studies [66,67,68]. Re-search has analysed the impact of different variables of artificial lighting, such as illuminance [66] and colour temperature [23] and colour rendering [67].
The combination of natural and electric lighting is critical for creating versatile and comprehensive learning spaces, particularly in educational buildings, which can significantly impact students’ learning processes and physiological states. With advancements in LED (Light Emitting Diode) technology, integrative lighting is now possible, enabling daylight and electric light combined throughout the day. LED systems offer high energy efficiency [68] because they permit more significant control potential and more precise brightness and spectral composition management [69]. These characteristics make LED technology ideal for optimizing light exposure to positively impact human functions that are affected by light [70]. Recent studies have demonstrated the significant impact of both daylight and electric lighting on students’ visual comfort, positive predisposition to learn, and circadian rhythm [71]. With these considerations in mind, further research may be needed to find the right combination of daylight and electric lighting to create integrated lighting solutions to support students’ health and learning.
New technologies such as the integration of Information and Communication Technologies (ICTs) [72], augmented and virtual reality (VR) [73], and artificial intelligence (AI) [74] are redefining the way students learn [75]. Higher education institutions are adapting to changes caused by external and internal factors [76]. The external factors include changes in the job market [77,78], the consequences of the COVID-19 pandemic [79], and economic and social development [80,81]. Internally, universities are engaging in fierce competition to improve their rankings [82], reduce drop-out rates [83], and promote multicultural integration [84]. These shifts are having a profound impact on how higher education is managed and carried out. New teaching methods need to be implemented to adapt to these changes, requiring new teaching practices in the classroom [85]. With these new learning practices comes a greater level of interactivity and a focus on different tasks [86]. The combination of multimedia elements in today’s classroom setting is unmatched. Multimedia learning entails obtaining or creating knowledge by handling a variety of representations, such as visual (static or dynamic illustrations) and auditory (spoken or written), at different levels of difficulty [87]. Therefore, it is critical to explore which lighting environment can best support all these new tasks [88]. Achieving this objective requires understanding how students evaluate their indoor lighting environment (ILE) and how it should be adapted for the tasks they actually perform in the classroom.
Taking all these considerations into account, the main objective of this study is to assess the differences between daylighting and electric lighting on students’ affective responses. For this purpose, the set of subjective evaluation scales that students employ to evaluate the ILE of their classroom is evaluated.
Furthermore, this study evaluates the correlation between the tasks conducted by students in their classroom and their affective impressions of the ILE. Additionally, it compares the varying affective responses of students under different lighting conditions such as daylighting, electrical lighting, or a combination of both. The findings of this analysis contribute to a better understanding of how students’ perceptions are influenced by the lighting conditions in their classroom, taking into account affective impressions, task factors, and the overall evaluation of the ILE.

2. Materials and Methods

Based on a field study, the methodology was developed to gather subjective evaluations from students about the lighting conditions in their classroom.

2.1. Field Study Settings

To ensure that the questionnaires were distributed in the most appropriate classrooms, a comprehensive list of Universitat Politècnica de València (39°28′50.3″ N 0°20′39.6″ W) classrooms was compiled. The classrooms were organised with great care and attention to detail, considering various factors such as classroom type and lighting. This categorisation process included all types of classrooms, dividing them by ceiling height, surface area, and type of teaching (including theory, practical, laboratory, and project work). The ILE was evaluated based on both daylighting and electric lighting. Regarding daylighting, window size, type of openings, and protection from direct sunlight were considered. Electric lighting was distinguished by several variables, including types of lamps, luminaires, average luminance, colour temperature, chromatic reproduction index, and other light characteristics. A comprehensive investigation of the university was performed following the production of the list, and on-site visits were conducted to confirm the appropriateness of the questionnaire test locations.
After an exhaustive evaluation process, 17 classrooms (Figure 1) were selected based on predetermined criteria, considering variability conditions and distinct lighting types (Table 1). These classrooms were representative and sufficiently differentiated to form part of the sample for the study. In order to extract affective impressions or semantic axes, it was necessary to establish relationships between multiple variables, which required a wide range of judgments. For this reason, students were asked to express their opinions on an extensive and diverse sample of classrooms. Although this set of design attributes may seem like confounding factors that need to be fully controlled and can introduce bias into the results, the solution randomly introduced the set of characteristics or attributes to reduce bias [89]. The reason for choosing each ILE and the classroom was that their characteristics were distinct and representative enough to be included in the sample.
Between April and May, the study was conducted in these 17 classrooms. These months were chosen to maintain a consistent and controlled climate to minimise any significant impact on the students’ responses. This approach was also intended to mitigate the effects of variable weather conditions on the feedback received during the study. Measurements were only taken on clear and pollution-free days to avoid interference from external factors such as smog, precipitation, or clouds. If rainfall occurred or the temperature exceeded a narrow range of 22.5 ± 2.0 °C, the study was halted to prevent any potential interference with the participants’ perception caused by ambient temperature [90].

2.2. Lighting Measurements Methodology

Lighting measurements were conducted on-site in real classrooms using a portable Asensetek Pro Standard ALP-01 spectrometer (NGL, New Taipei City, Taiwan), covering a measurement wavelength range of 380–780 nm. The range of illuminance was from 5 to 50,000 lux, reliable chromaticity was from 50 to 50,000 lux, optical resolution was 8 nm, with a repeatability (2σ) for x, and <0.0005, integration time from 6 ms to 16 s, and the storage range temperature was from −10 to 45 °C. The spectrometer was calibrated at Asesentek Laboratories (New Taipei City, Taiwan) providing a ±3% of uncertainty for measurements.
To obtain the measurements, the spectrometer was mounted on a tripod at a height of 0.85 m above floor level. Illuminance was measured using a grid of 1 m × 1 m. This measure was established taking into account that the maximum grid size (p) for classrooms with artificial lighting complied with the European Standard EN12464-1 [65], and the European Standard EN 17037 [91] for classrooms with daylighting.
The lighting design criteria that are specified in the European standard [65] include the following: average maintained illuminance, illuminance uniformity, and colour rendering index (CRI), which are shown in Table 1.

2.3. Participants in the Study

According to Comrey and Lee [92], the sample size was established based on the requirement that participants must be regular users of the classrooms selected for the field study. Eventually, 521 students (Table 2) participated with a mean age of 20.75 years and a standard deviation of 14.57. All the individuals who participated in the study were in good health and did not have any significant visual impairment, except for myopia or astigmatism, which were treated with corrective lenses. The students participated in the study voluntarily and were not remunerated for their involvement.

2.4. Research Process

Participants in the study were fully “immersed” in their classroom’s ILE while completing the questionnaire. Prior to beginning, each individual was informed of the purpose of the research and provided with clear instructions for proper completion of the questionnaire. The questions were randomised to prevent any potential bias in responses, and four different versions of the questionnaire were created. The completion time for each questionnaire ranged from 5 to 15 min. In the present study, a validated questionnaire developed in a previous study by the authors [88] using Differential Semantics (in the framework of Kansei Engineering) was used to analyse the affective impressions of university students. Following that questionnaire, the adjectives were evaluated using a 5-point-Likert scale, extending through totally agree, agree, neutral, disagree, and totally disagree.

2.5. Statistical Process

The data underwent statistical processing using the SPSS software package (https://www.ibm.com/products/spss-statistics, accessed on 10 April 2023). The analysis was segmented into three distinct phases (Figure 2) for a more comprehensive understanding of the results as follows:
Phase I:
This phase aimed to determine the group of emotional impressions of the global assessment of the ILE. In the first place, the group of emotional impressions or semantic axes was identified using factor analysis [93]. This method can locate unrelated variables that describe the perception of a specific product, in this case, the ILE of a classroom. Each axis or factor combines concepts from the original set, indicating significant correlations in students’ responses. Therefore, ideas with similar evaluations are grouped, representing common concepts that students use implicitly to assess their classroom. Finally, linear regression analysis was utilised to determine the effect of each axis on the overall assessment.
Phase II:
The objective of this phase was to establish a correlation between the set of emotional responses, collected in Phase I, and the academic tasks carried out in the classroom. Factor analysis was used to categorise classroom tasks by assessing ILEs. After this, Spearman’s correlation coefficient was employed to find the relationship between these tasks’ factors and emotional responses.
Phase III:
This phase aimed to investigate the impact of various lighting types on affective response, such as daylighting, electric lighting, and a combination of both. An ANOVA analysis was utilised to examine any significant differences between the different lighting types and the emotional impressions, the groups of tasks, and the global evaluation of the ILE.

3. Results

3.1. Part I: Detection of Significant Affective Responses in the Overall Evaluation of the Indoor Lighting Environments of the Classroom

3.1.1. Reduction of the Number of Perception Variables

Factor analysis [93] was used to identify the uncorrelated variables that characterise the perception of the classroom’s ILE. The group set of affective responses or semantic axes represents the set of subjective evaluation scales that students employ to evaluate the lighting environment of their classroom, adapted to the same language they use. Table 3 shows the variance of the semantic axes. These axes describe 59.73% of the variance in the original adjectives.
Applying factor analysis [93] to the students’ responses in the questionnaire, the original 37 adjectives were grouped into six uncorrelated factors (Table 4). These six axes symbolise the semantic space for students to their classroom’s ILEs as follows:
  • First axis: This axis has been interpreted as the perception of the ILEs as Surprising–amazing. It includes the adjectives “surprising”, “amazing”, “awesome”, “stimulating”, “interesting”, “original”, and “suggestive”. This axe describes 15.97% of the variance.
  • Second axis: This axis groups the adjectives “clear”, “sharp”, “efficient”, “with quality”, “bright”, “pleasant”, “satisfactory”, and “convenient”. This factor represents the dimension Clear–efficient and explains 10.15% of the variance.
  • Third axis: This axis represents the dimension of Soft–calm and groups the adjectives “soft”, “calm”, “dim”, “warm”, “natural”, “protector”, and “cosy”. It represents the 9.66% of the variance.
  • Fourth axis: It characterises the perception of the ILEs as Uniform. The Kasei words that form this factor are “uniform”, “homogeneous”, “orderly”, “balanced”, and “functional”. The variance explained by this factor is 8.91%.
  • Fifth axis: The terms that contain this axis are “lively”, “cheerful”, “dynamic”, “friendly”, “colourful”, and “beautiful”. It shows the sensation of the ILE as being Lively–cheerful. This factor explains 8.87% of the total variance.
  • Sixth axis: This axis congregates the concepts “brilliant”, “glaring–dazzling”, and “intense” and represents the impression of Brilliant–glaring. Variance explained by this factor ascend to 6.16%.
In order to establish the reliability of these axes, Cronbach’s alpha values were utilised (Table 4). These values, ranging from 0.70 to 0.91 for the first six dimensions, were found to be significant [94].

3.1.2. Ranking Affective Impressions According to Importance in the Global Assessment of the Indoor Lighting Environment

Linear regression analysis was utilised to rank the semantic axes or affective impressions based on their relationship with the variable “global assessment of the ILE of the classroom”. The model contains five significant axes (Equation (1)), with an R coefficient of 0.720. Figure 3 shows the ranking of affective impressions, accordingly to the global assessment variable. In the first place, the perception of the classroom’s ILE as being first Clear–efficient, with a coefficient of β = 0.474, has the greatest influence on the lighting global evaluation. Secondly, the perceptions of the light environment are Uniform, with a coefficient of β = 0.339. Thirdly, the axis is Lively–cheerful, with a coefficient of β = 0.241. Then, the axis is Soft–calm with a coefficient of β = 0.241. Finally, the axis is Surprising–amazing (s.l. = 0.175). In the model, the factor Brilliant–glaring is not significant (s.l. = 0.020) so it was excluded from the model.
ILE global assessment = 0.431 + 0.520 (Clear–efficient) + 0.372 (Uniform) + 0.264 (Lively–cheerful) + 0.192 (Surprising–amazing)

3.2. Part II: Correlation Analysis between Affective Impressions and Classroom Tasks

3.2.1. Identifying Task Factors

The factor analysis grouped the 14 main tasks that students perform in the classroom into four factors or axes based on the assessment of their lighting conditions. These factors explain 70.39% of the variance (Table 5).
The analysis aimed to identify the concept associated with each variable’s contribution to the factors. The following four factors were identified:
  • First factor: This factor represents the Writing–reading tasks performed by students in the classroom. It is positively correlated with the tasks of “writing”, “reading”, “drawing”, and “reviewing notes”. This factor accounts for 21.91% of the initial variance.
  • Second factor: This factor groups the tasks related to Discussing–teamwork. This factor is formed, with positive correlation, by the tasks of “discussing”, “teamwork”, “correcting”, “asking the teacher”, and “reflecting”. It explains 20.22% of the variance.
  • Third factor: The third factor represents the tasks of Paying attention. It is positively correlated with tasks such as “paying attention to the board”, “paying attention”, and “looking at the projector”. Of the sample variance it explains 15.04%.
  • Fourth factor: This factor represents the tasks Using devices performed by the students in the classroom. It is positively correlated with the tasks of “working on the computer “and “using the telephone, tablet, …”. This factor accounted for 21.91% of the initial variance.
The consistency of the perceptual space was confirmed using Cronbach’s Alpha. The reliability coefficient values for the first four factors were between 0.91 and 0.82, an indication of the high reliability of these scales [94].

3.2.2. Relationship between Affective Impressions and Factors of Tasks

The relationship between the groups of tasks perform currently in the classroom and affective impressions was established with a Pearson correlation coefficient (Figure 4).
The results show that different tasks in the classroom require different lighting conditions.
In the first place, generating the sensation of the ILE being Clear–efficient is mainly required for the Writing–reading tasks. The ILE should also generate the sensation of being Uniform, although to a lesser extent. There is no significant correlation between the factor of Writing–reading and the sensations of Surprising–amazing, Soft–calm, Lively–cheerful, and Brilliant–glaring.
In the second place, the task factor Discussing–teamwork shows significant correlations with the set of affective impressions Clear–efficient. This factor is linked to the sensations of the lighting conditions of being Soft–calm and Uniform to a lesser degree. The other impressions, Surprising–amazing, Lively–Cheerful, and Brilliant–glaring, show no significant correlation with this factor.
In the third place, the task factor of Paying attention is mainly related to the ILE generating the sensation of Uniform. The rest of the set of impressions show no significant correlation with this factor.
Finally, the task factor of Using devices shows significant correlations with the impressions of the lighting conditions as being Soft–calm. With lower correlations, Surprising–amazing. At the same time, it has a negative correlation with the axis Brilliant–glaring.

3.3. Phase III: Differences on Affective Response between Types of Lighting Conditions

3.3.1. Relationship between Affective Impressions and Type of Lighting Conditions

To explore the differences in perception, a discriminant analysis was conducted using “daylighting—electric lighting—daylighting + electric lighting” as the grouping variable and the affective impression scores as independent variables. Thus, it was possible to confirm the hypothesis that students possess distinct perceptual structures, enabling them to categorise different types of ILE based on their responses. The mean scores for each axis were then determined for various ILE types. Finally, ANOVA was employed to establish the significance of the differences (s.l. < 0.05) (Table 6). Results indicate that, depending on the type of ILE, there were significant differences in five of the six axes: Surprising–amazing, Clear–efficient, Soft–clam, Uniform, and Lively–cheerful. However, there were no significant differences in the Brilliant–glaring axis, indicating that regardless of the presence of daylighting or electric lighting, the perception of glare remained unchanged.
According to Figure 5, analysing the averages reveals that classrooms with daylighting evoke a stronger sense of Surprising–amazing compared to those with electric lighting. Similarly, classrooms with daylighting also receive higher ratings for feeling Clear–efficient, whereas classrooms with electric lighting receive negative ratings. In terms of Soft–calm, classrooms with daylighting receive the highest ratings, while those with electric lighting receive negative ratings.
In contrast, the feeling of Uniform is mainly achieved in classrooms with electric lighting and has an almost neutral effect in classrooms with daylight and electric lighting. Classrooms with natural light do not generate this feeling of uniformity. The feeling that the lighting environment is Lively–cheerful is mainly succeeded in classrooms with daylighting and not in classrooms with electric lighting or a combination of both. Finally, the Brilliant–glaring axis does not generate significant differences concerning the assessment of the lighting environment.

3.3.2. Relationship between Task Factors and Type of Lighting Conditions

The data presented in Table 7 indicate the existence of significant differences in the lighting evaluation if the classroom is used for Discussing–teamwork and Paying attention tasks. However, there are no significant differences for Writing–reading tasks, and the differences are nearly significant for Using devices tasks.
Figure 6 shows the mean results for each group of tasks based on the classroom’s lighting type. The Writing–reading tasks receive a positive rating primarily in classrooms with daylighting. The Discussing–teamwork tasks exhibit significant differences depending on the lighting type. Classrooms with daylighting are optimal for these tasks.
Regarding tasks that require Paying attention, the type of lighting in the classroom plays a significant role. According to students, a combination of daylighting and electric lighting is the most suitable for these tasks. Conversely, a classroom with electric lighting is almost neutral in this assessment and it becomes worse if the classroom has only daylighting.
Tasks that require the Use of devices exhibit almost significant differences. According to students, daylighting is the most suitable for these tasks, while electric lighting is less effective. However, assessing these tasks improves if the classroom has both daylighting and electric lighting, although it remains negative.

3.3.3. Analysis of the Overall Assessment of the Lighting Environment Depending on the Type of Lighting Conditions

As for the overall lighting assessment, ANOVA was used to determine whether the differences were significant (for a significant level < 0.05). Table 8 shows significant differences in almost all global assessment variables.
According to Figure 7, students clearly prefer daylighting when illuminating a classroom. Daylighting appears to be the only lighting option that students like. Conversely, electric light appears to be the only light source that students dislike, regardless of whether or not it is combined with daylight. Nonetheless, despite the clear preference for daylighting, students still consider both electric lighting and the combination of being adequate for a classroom and the classroom still being well lit.
Figure 8 shows significant differences in the lighting assessment for practical classes compared to theory classes. Students prefer a classroom with daylight for practical lessons and electric lighting for theory lessons. However, the ratings are similar if the classroom has a combination of natural and electric lighting. Despite this distinction, the overall ratings remain comparable when daylight and electric lighting are present in the classroom.

4. Discussion

This study aims to analyse the differences in affective response between daylighting and electric lighting when students evaluate the ILE of their classroom. In regards to the contribution to application, the findings of this study provide important outcomes and offer essential understandings of how students’ perceptions are affected, considering elements such as affective impressions, task factors, and overall evaluation of the ILE.
To begin with, this study underscores the significance of daylighting concerning students’ affective response towards the ILE of their classroom, which can ultimately affect their learning outcomes. Other studies also have shown the importance of daylight in educational environments [50,95,96]. The right balance between daylight and electric light is crucial for optimal classroom performance [97].
The relationship between affective impressions and type of lighting conditions showed significant differences in assessing all axes of affective impressions across different lighting conditions. Daylighting is mainly associated with Soft–calm feelings, followed by Clear–efficient, Lively–cheerful, and Surprising–amazing sensations. Conversely, electric lighting is only related to the Uniform sensation. The same is observed when daylight and electric lighting are combined.
In addition, students prefer different lighting environments based on the tasks they are carrying out in the classroom. This connection plays a crucial role in the belief that the ILE should elicit different sensations to suit the various tasks performed in the classroom. The outcomes of variable lighting in educational settings are consistent with these findings [98].
For Writing–reading tasks, the ideal lighting environment is Clear–efficient, Uniform, and Soft–calm. These results are in accordance with the lighting standards, like UNE-EN 12464-1 [65], which regulates the parameters of illuminance, uniformity, and glare in educational spaces. For tasks involving using electronic devices, students prefer a Soft–calm atmosphere.
There are also significant differences in the relationship between task factors and type of lighting conditions. Students mainly associated daylighting with tasks such as Discussing–teamwork and Using electronic devices. According to students, electric lighting is unsuitable for any task factors. A combination of daylighting and electric lighting is the best solution for paying attention. Conversely, a classroom with electric lighting is almost neutral in this assessment and worsens if the classroom only has daylighting.
Similarly, there are significant differences between lighting conditions in almost all global assessment variables. Students prefer daylighting when it comes to illuminating a classroom, and daylighting appears to be the only lighting option students like. Previous studies have shown that individuals generally form a positive perception of a space that is naturally illuminated [99]. On the contrary, electric light appears to be the only light source that students dislike, regardless of whether or not it is combined with daylight. Nonetheless, despite the clear preference for daylighting, students still consider both electric lighting and the combination of daylight and electric light adequate for a classroom and that the classroom is still well lit.
The findings of this study may enable designers and educators to create learning spaces that promote a positive and stimulating student environment by understanding the relationship between the lighting environment and students’ affective responses.
As limitations of the present work, it is worth noting that the field study was conducted in actual university classrooms, which combined various elements that may have influenced perception. At this stage, diversity is prioritised over consistency. The primary objective of this study is to derive independent semantic axes using a set of stimuli evaluations. For accurate and representative outcomes, it is imperative to have a sufficient number of individuals and stimuli that cover a wide range of evaluations for each adjective and their relationships. The methodology employed in this study follows Kish’s [89] approach, which involves controlling experiments by randomly introducing variables. This procedure assumes that chance will produce equivalent distributions of units in all variables under study, thereby rendering any resulting bias negligible. In the same vein, controlling the time of day during the study was not feasible due to the need for a large sample size. Typically, Spanish universities utilise their classrooms for extended periods from 8:00 a.m. to 9:00 p.m. Future investigations may explore implementing interventions at different times to determine if comparable outcomes can be achieved at various times of the day.
The process of gathering students’ opinions and conducting in situ evaluations of classroom ILEs has provided valuable insights. Nevertheless, it is essential to recognise that the combination of natural and electric lighting can significantly impact the values of lighting variables. Therefore, further research is necessary to determine the specific lighting parameter values that contribute to positive affective responses and establish any potential relationships between these affective impressions and visual performance. Another potential avenue for further investigation is incorporating LED light sources instead of the fluorescent sources studied in this research. It is worth highlighting that despite their comparable colour temperatures, differences in the light sources’ spectra and wavelengths could lead to variations in the affective response and visual performance.
This research will deepen our understanding of factors that influence individual affective responses toward ILEs, ultimately aiding in the design of more effective learning environments.

5. Conclusions

This study aimed to explore the impact of various types of lighting, such as electric light, daylight, or a combination of both, on students’ emotional responses in the classroom. The research findings suggest that students prefer a Clear-efficient lighting environment for Writing–reading tasks. For using electronic devices, a Soft–calm atmosphere is preferred. A blend of daylight and electric lighting is considered the most suitable option when paying attention is required, whereas daylighting is preferred for teamwork and discussion activities. Although students prefer daylighting, electric lighting and a combination of both are also acceptable in the classroom. This research can help educators and designers understand the connection between lighting and students’ emotional responses and create learning spaces that promote a positive and engaging atmosphere for students.

Author Contributions

Conceptualisation, N.C. and C.L.; methodology, N.C.; software, N.C. and C.L.; validation, N.C.; formal analysis, C.L.; investigation, N.C., V.B.-G. and C.P.-C.; resources, N.C.; data curation, N.C. and C.L.; writing—original draft preparation, N.C.; writing—review and editing, N.C. and C.P.-C.; visualisation, N.C.; supervision, N.C.; project administration, N.C.; funding acquisition, N.C. and V.B.-G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study has been conducted according to the Declaration of Helsinki. Ethical review and approval were waived for this study as it did not include any ethical issues and the questionnaire did not collect any personally identifiable data.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study. The invitation to the survey emphasises that participation is voluntary, anonymous, and optional. The participants could leave the survey at any time during the process and were also informed that the data would only be used for academic purposes.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Siraji, M.A.; Kalavally, V.; Schaefer, A.; Haque, S. Effects of Daytime Electric Light Exposure on Human Alertness and Higher Cognitive Functions: A Systematic Review. Front. Psychol. 2022, 12, 765750. [Google Scholar] [CrossRef]
  2. Alimoglu, M.K.; Donmez, L. Daylight exposure and the other predictors of burnout among nurses in a University Hospital. Int. J. Nurs. Stud. 2005, 42, 549–555. [Google Scholar] [CrossRef] [PubMed]
  3. Arsenault, H.; Hébert, M.; Dubois, M.C. Effects of glazing colour type on perception of daylight quality, arousal, and switch-on patterns of electric light in office rooms. Build. Environ. 2012, 56, 223–231. [Google Scholar] [CrossRef]
  4. Leech, J.A.; Nelson, W.C.; Burnett, R.T.; Aaron, S.; Raizenne, M.E. It’s about time: A comparison of Canadian and American time-activity patterns. J. Expo. Anal. Environ. Epidemiol. 2002, 12, 427–432. [Google Scholar] [CrossRef]
  5. Dunster, G.P.; Hua, I.; Grahe, A.; Fleischer, J.G.; Panda, S.; Wright, K.P.; Vetter, C.; Doherty, J.H.; de la Iglesia, H.O. Daytime light exposure is a strong predictor of seasonal variation in sleep and circadian timing of university students. J. Pineal Res. 2023, 74, e12843. [Google Scholar] [CrossRef]
  6. IEA. Buildings; IEA: Paris, France, 2022. [Google Scholar]
  7. Baloch, A.A.; Shaikh, P.H.; Shaikh, F.; Leghari, Z.H.; Mirjat, N.H.; Uqaili, M.A. Simulation tools application for artificial lighting in buildings. Renew. Sustain. Energy Rev. 2018, 82, 3007–3026. [Google Scholar] [CrossRef]
  8. China Building Energy Conservation Association. China Building Energy Consumption Research Report; China Building Energy Conservation Association: Beijing, China, 2021. [Google Scholar]
  9. Amber, K.P.; Aslam, M.W.; Hussain, S.K. Electricity consumption forecasting models for administration buildings of the UK higher education sector. Energy Build. 2015, 90, 127–136. [Google Scholar] [CrossRef]
  10. Yuan, X.; Zuo, J.; Huisingh, D. Green Universities in China—What matters? J. Clean. Prod. 2013, 61, 36–45. [Google Scholar] [CrossRef]
  11. Tan, H.; Chen, S.; Shi, Q.; Wang, L. Development of green campus in China. J. Clean. Prod. 2014, 64, 646–653. [Google Scholar] [CrossRef]
  12. Zhang, C.; Zhao, T.; Li, K. Quantitative correlation models between electricity consumption and behaviors about lighting, sockets and others for electricity consumption prediction in typical campus buildings. Energy Build. 2021, 253, 111510. [Google Scholar] [CrossRef]
  13. Wright, T.S.A. Definitions and frameworks for environmental sustainability in higher education. High. Educ. Policy 2002, 15, 105–120. [Google Scholar] [CrossRef]
  14. Berchin, I.I.; da Silva, S.A.; Ceci, F.; Gabriel, G.M.; Anhalt, T.C.; Guerra, J.B.S.O.A. The Role of Universities to Promote Sustainable Practices and Climate Change Adaptation: Analysis of the 22 Conferences of the Parties Using Text Mining. In Towards Green Campus Operations; World Sustainability Series; Springer: Cham, Switzerland, 2018; pp. 251–278. [Google Scholar]
  15. Lim, S.B.; Yong, C.K.; Malek, J.A.; Jali, M.F.M.; Awang, A.H.; Tahir, Z. Effectiveness of fear and crime prevention strategy for sustainability of safe city. Sustainability 2020, 12, 10593. [Google Scholar] [CrossRef]
  16. Omer, A.M. Energy, environment and sustainable development. Renew. Sustain. Energy Rev. 2008, 12, 2265–2300. [Google Scholar] [CrossRef]
  17. Herrera Burstein, Y.E.; Goñi Avila, N.M. Promoting sustainable consumption among university students: A systematic literature review. Int. J. Sustain. High. Educ. 2023, 25, 265–288. [Google Scholar] [CrossRef]
  18. Almeida, A.P.; Sousa, V.; Silva, C.M. Methodology for estimating energy and water consumption patterns in university buildings: Case study, Federal University of Roraima (UFRR). Heliyon 2021, 7, e08642. [Google Scholar] [CrossRef] [PubMed]
  19. Marzouk, M.; ElSharkawy, M.; Mahmoud, A. Analysing user daylight preferences in heritage buildings using virtual reality. Build. Simul. 2022, 15, 1561–1576. [Google Scholar] [CrossRef]
  20. Michael, A.; Heracleous, C. Assessment of natural lighting performance and visual comfort of educational architecture in Southern Europe: The case of typical educational school premises in Cyprus. Energy Build. 2017, 140, 443–457. [Google Scholar] [CrossRef]
  21. Robson, E.R. School Architecture: Being Practical Remarks on the Planning, Designing, Building, and Furnishing of School-Houses; Murray, J., Ed.; J. Murray: London, UK, 1874. [Google Scholar]
  22. Konstantzos, I.; Sadeghi, S.A.; Kim, M.; Xiong, J.; Tzempelikos, A. The effect of lighting environment on task performance in buildings—A review. Energy Build. 2020, 226, 110394. [Google Scholar] [CrossRef]
  23. Yang, W.; Jeon, J.Y. Effects of correlated colour temperature of LED light on visual sensation, perception, and cognitive performance in a classroom lighting environment. Sustainability 2020, 12, 4051. [Google Scholar] [CrossRef]
  24. Fakhari, M.; Vahabi, V.; Fayaz, R. A study on the factors simultaneously affecting visual comfort in classrooms: A structural equation modeling approach. Energy Build. 2021, 249, 111232. [Google Scholar] [CrossRef]
  25. Ru, T.; Kompier, M.E.; Chen, Q.; Zhou, G.; Smolders, K.C.H.J. Temporal tuning of illuminance and spectrum: Effect of a full-day dynamic lighting pattern on well-being, performance and sleep in simulated office environment. Build. Environ. 2023, 228, 109842. [Google Scholar] [CrossRef]
  26. Kim, D.H.; Mansfield, K.P. A cross-cultural study on perceived lighting quality and occupants’ well-being between UK and South Korea. Energy Build. 2016, 119, 211–217. [Google Scholar] [CrossRef]
  27. Zhu, Y.; Yang, M.; Yao, Y.; Xiong, X.; Li, X.; Zhou, G.; Ma, N. Effects of Illuminance and Correlated Color Temperature on Daytime Cognitive Performance, Subjective Mood, and Alertness in Healthy Adults. Environ. Behav. 2017, 51, 199–230. [Google Scholar] [CrossRef]
  28. Kang, H.Y.; Park, I.H.; Kim, M.J. The Role of Vision Screening and Classroom Illumination in the Vision Health of Korean School Children. J. Sch. Health 2003, 73, 358–362. [Google Scholar] [CrossRef]
  29. Mason, I.C.; Boubekri, M.; Figueiro, M.G.; Hasler, B.P.; Hattar, S.; Hill, S.M.; Nelson, R.J.; Sharkey, K.M.; Wright, K.P.; Boyd, W.A.; et al. Circadian Health and Light: A Report on the National Heart, Lung, and Blood Institute’s Workshop. J. Biol. Rhythms 2018, 33, 451–457. [Google Scholar] [CrossRef]
  30. Leichtfried, V.; Mair-Raggautz, M.; Schaeffer, V.; Hammerer-Lercher, A.; Mair, G.; Bartenbach, C.; Canazei, M.; Schobersberger, W. Intense illumination in the morning hours improved mood and alertness but not mental performance. Appl. Ergon. 2015, 46, 54–59. [Google Scholar] [CrossRef] [PubMed]
  31. Smolders, K.C.H.J.; de Kort, Y.A.W.; Cluitmans, P.J.M. A higher illuminance induces alertness even during office hours: Findings on subjective measures, task performance and heart rate measures. Physiol. Behav. 2012, 107, 7–16. [Google Scholar] [CrossRef] [PubMed]
  32. Ishii, H.; Kanagawa, H.; Shimamura, Y.; Uchiyama, K.; Miyagi, K.; Obayashi, F.; Shimoda, H. Intellectual productivity under task ambient lighting. Light. Res. Technol. 2018, 50, 237–252. [Google Scholar] [CrossRef]
  33. Shishegar, N.; Boubekri, M. Natural Light and Productivity: Analyzing the Impacts of Daylighting on Students’ and Workers’ Health and Alertness. Int. J. Adv. Chem. Eng. Biol. Sci. 2016, 3, 72–77. [Google Scholar] [CrossRef]
  34. Heschong, L. Daylighting and human performance. ASHRAE J. 2002, 44, 65–67. [Google Scholar]
  35. Liu, Q.; Huang, Z.; Li, Z.; Pointer, M.R.; Zhang, G.; Liu, Z.; Gong, H.; Hou, Z. A field study of the impact of indoor lighting on visual perception and cognitive performance in classroom. Appl. Sci. 2020, 10, 7436. [Google Scholar] [CrossRef]
  36. Samani, S.A.; Samani, S.A. The Impact of Indoor Lighting on Students’ Learning Performance in Learning Environments: A knowledge internalization perspective University of Applied Sciences. Int. J. Bus. Soc. Sci. 2012, 3, 127–136. [Google Scholar]
  37. Yu, X.; Su, Y. Daylight availability assessment and its potential energy saving estimation—A literature review. Renew. Sustain. Energy Rev. 2015, 52, 494–503. [Google Scholar] [CrossRef]
  38. Rucińska, J.; Trzaski, A. Measurements and simulation study of daylight availability and its impact on the heating, cooling and lighting energy demand in an educational building. Energies 2020, 13, 2555. [Google Scholar] [CrossRef]
  39. Vasquez, N.G.; Rupp, R.F.; Andersen, R.K.; Toftum, J. Occupants’ responses to window views, daylighting and lighting in buildings: A critical review. Build. Environ. 2022, 219, 109172. [Google Scholar] [CrossRef]
  40. Wu, W.; Ng, E. A review of the development of daylighting in schools. Light. Res. Technol. 2003, 35, 111–125. [Google Scholar] [CrossRef]
  41. Meresi, A. Evaluating daylight performance of light shelves combined with external blinds in south-facing classrooms in Athens, Greece. Energy Build. 2016, 116, 190–205. [Google Scholar] [CrossRef]
  42. Korsavi, S.S.; Zomorodian, Z.S.; Tahsildoost, M. Visual comfort assessment of daylit and sunlit areas: A longitudinal field survey in classrooms in Kashan, Iran. Energy Build. 2016, 128, 305–318. [Google Scholar] [CrossRef]
  43. Kim, T.; Hong, W.; Kim, H. Daylight evaluation for educational facilities established in high-rise housing complexes in Daegu, South Korea. Build. Environ. 2014, 78, 137–144. [Google Scholar] [CrossRef]
  44. Reinhart, C.F.; Weissman, D.A. The daylit area—Correlating architectural student assessments with current and emerging daylight availability metrics. Build. Environ. 2012, 50, 155–164. [Google Scholar] [CrossRef]
  45. Gueymard, C.A. Heschong Mahone Group Windows and Classrooms: A Study of Student Performance and the Indoor Environment. Calif. Energy Commision 2003, 37, 414–435. [Google Scholar] [CrossRef]
  46. Figueiro, M.G.; Brons, J.; Plitnick, B.; Donlan, B.; Leslie, R.P.; Rea, M.S. Measuring circadian light and its impact on adolescents. Light. Res. Technol. 2011, 43, 201–215. [Google Scholar] [CrossRef] [PubMed]
  47. Shemirani, S.M.M.; Memarian, G.H.; Naseri, S.P.; Nejad, H.H.; Vaziri, V. Investigating the behaviors of the elementary school students in reference to factors associated with daylight. Asian Soc. Sci. 2011, 7, 237–248. [Google Scholar] [CrossRef]
  48. Aumann, D.; Heschong, L.; Wright, R.L.; Peet, R. Windows and Classrooms: Student Performance and the Indoor Environment; Technical Report; California Energy Commission: Sacramento, CA, USA, 2004; pp. 139–154.
  49. Bezjak, B.; Černe, B.; Kalčič, I.; Medved, S. Optimizing the Form of School Buildings by Using the Requirements for Daylight Illumination. Archit. Sci. Rev. 2003, 46, 305–311. [Google Scholar] [CrossRef]
  50. Heschong, L.; Wright, R.L.; Okura, S. Daylighting impacts on human performance in school. J. Illum. 2002, 4480, 101–114. [Google Scholar] [CrossRef]
  51. Barrett, P.; Zhang, Y.; Moffat, J.; Kobbacy, K. A holistic, multi-level analysis identifying the impact of classroom design on pupils’ learning. Build. Environ. 2013, 59, 678–689. [Google Scholar] [CrossRef]
  52. Samani, S.A. The Influence of Light on Student’s Learning Performance in Learning Environments: A Knowledge Internalization Perspective. World Acad. Sci. Eng. Technol. 2011, 57, 540–547. [Google Scholar]
  53. Ruck, N.C. Building Design and Human Performance; Van Nostrand Reinhold, Co.: New York, NY, USA, 1989. [Google Scholar]
  54. Plympton, P.; Conway, S.; Epstein, K. Daylighting in Schools: Improving Student Performance and Health at a Price Schools Can Afford. In Proceedings of the American Solar Energy Society Conference, Madison, WI, USA, 16 June 2000. [Google Scholar]
  55. Tähkämö, L.; Partonen, T.; Pesonen, A.K. Systematic review of light exposure impact on human circadian rhythm. Chronobiol. Int. 2019, 36, 151–170. [Google Scholar] [CrossRef]
  56. Aries, M.; Aarts, M.; van Hoof, J. Daylight and health: A review of the evidence and consequences for the built environment. Light. Res. Technol. 2015, 47, 6–27. [Google Scholar] [CrossRef]
  57. Dong, Y.; Zhang, X. Study on the effect of awakening daylight in dormitories on morning alertness, mood, fatigue and sleep quality of college students. Build. Environ. 2021, 203, 108060. [Google Scholar] [CrossRef]
  58. Altomonte, S. Daylight and the occupant: Visual and physio-psychological well-being in built environments. In Proceedings of the PLEA 2009—Architecture Energy and the Occupant’s Perspective: Proceedings of the 26th International Conference on Passive and Low Energy Architecture; Demers, C., Potvin, A., Eds.; Presses de l’Université Laval: Quebec, Canada, 2009; p. 58. [Google Scholar]
  59. Knoop, M.; Stefani, O.; Bueno, B.; Matusiak, B.; Hobday, R.; Wirz-Justice, A.; Martiny, K.; Kantermann, T.; Aarts, M.P.J.; Zemmouri, N.; et al. Daylight: What makes the difference? Light. Res. Technol. 2020, 52, 423–442. [Google Scholar] [CrossRef]
  60. Münch, M.; Wirz-Justice, A.; Brown, S.A.; Kantermann, T.; Martiny, K.; Stefani, O.; Vetter, C.; Wright, K.P.; Wulff, K.; Skene, D.J. The Role of Daylight for Humans: Gaps in Current Knowledge. Clocks Sleep 2020, 2, 61–85. [Google Scholar] [CrossRef] [PubMed]
  61. Melrose, S. Seasonal Affective Disorder: An Overview of Assessment and Treatment Approaches. Depress. Res. Treat. 2015, 2015, 178564. [Google Scholar] [CrossRef] [PubMed]
  62. Costanzo, V.; Evola, G.; Marletta, L. A Review of Daylighting Strategies in Schools: State of the Art and Expected Future Trends. Buildings 2017, 7, 41. [Google Scholar] [CrossRef]
  63. De Kort, Y.A.W. Virtual environments as research tools for environmental psychology: A study of the comparability of real and virtual environments. In Proceedings of the 4th Annual International Workshop on Presence, Philadelphia, PA, USA, 21–23 May 2001. [Google Scholar]
  64. Edwards, L.; Torcellini, P. A Literature Review of the Effects of Natural Light on Building Occupants. Contract 2002, 58, 1–54. [Google Scholar] [CrossRef]
  65. EN 12464-1; Light and Lighting—Lighting of Work Places—Part 1: Indoor Work Places. CIE: Vienna, Austria, 2021.
  66. Castilla, N.; Higuera-Trujillo, J.L.; Llinares, C. The effects of illuminance on students′ memory. A neuroarchitecture study. Build. Environ. 2023, 228, 109833. [Google Scholar] [CrossRef]
  67. Revantino; Mangkuto, R.A.; Enge, A.; Munir, F.; Soelami, F.X.N.; Faridah. The effects of illuminance, colour temperature, and colour rendering of various existing light-emitting diode lamps on subjective preference and performance in Indonesia. J. Build. Eng. 2018, 19, 334–341. [Google Scholar] [CrossRef]
  68. Wagiman, K.R.; Abdullah, M.N.; Ariff, M.A.M.; Hassan, M.Y. Techno-economic analysis of lighting system retrofit at the Industrial Training Institute. In Proceedings of the 5th IET International Conference on Clean Energy and Technology (CEAT2018), Kuala Lumpur, Malaysia, 5–6 September 2018; Volume 2018. [Google Scholar]
  69. Dikel, E.E.; Newsham, G.R.; Xue, H.; Valdés, J.J. Potential energy savings from high-resolution sensor controls for LED lighting. Energy Build. 2018, 158, 43–53. [Google Scholar] [CrossRef]
  70. CIE. CIE statement on non-visual effects of light recommending proper light at the proper time. Light Eng. 2019, 23, 4–5. [Google Scholar]
  71. Ezpeleta, S.; Orduna-hospital, E.; Solana, T.; Aporta, J.; Pinilla, I.; Sánchez-cano, A. Analysis of photopic and melanopic lighting in teaching environments. Buildings 2021, 11, 439. [Google Scholar] [CrossRef]
  72. Asabere, N.Y.; Ahmed, A.M. Towards Enhancing Quality in Education through Information and Communication Technologies (ICTs) in Higher Educational Institutions (HEIs). Int. J. Comput. Appl. 2013, 62, 10–18. [Google Scholar] [CrossRef]
  73. Kavanagh, S.; Luxton-Reilly, A.; Wuensche, B.; Plimmer, B. A Systematic Review of Virtual Reality in Education. Themes Sci. Technol. Educ. 2017, 10, 85–119. [Google Scholar]
  74. Chiu, T.K.F.; Xia, Q.; Zhou, X.; Chai, C.S.; Cheng, M. Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Comput. Educ. Artif. Intell. 2023, 4, 100118. [Google Scholar] [CrossRef]
  75. Castro Benavides, L.M.; Tamayo Arias, J.A.; Arango Serna, M.D.; Branch Bedoya, J.W.; Burgos, D. Digital Transformation in Higher Education Institutions: A Systematic Literature Review. Sensors 2020, 20, 3291. [Google Scholar] [CrossRef] [PubMed]
  76. Co, M.J.; Hamadeh Kerbage, S.; Willetts, G.; Garvey, L.; Bhattacharya, A.; Croy, G.; Mitchell, B. Students coping with change in higher education: An overview. Educ. Res. Rev. 2023, 38, 100508. [Google Scholar] [CrossRef]
  77. Lauder, H.; Mayhew, K. Higher education and the labour market: An introduction. Oxford Rev. Educ. 2020, 46, 1–9. [Google Scholar] [CrossRef]
  78. Kandlbinder, P. Graduate employability and higher education: Past, present and future. HERDSA Rev. High. Educ. 2018, 5, 31–61. [Google Scholar]
  79. Marinoni, G.; van’t Land, H. The Impact of COVID-19 on Global Higher Education. Int. High. Educ. 2020, 7–9. [Google Scholar]
  80. Godonoga, A.; Sporn, B. The conceptualisation of socially responsible universities in higher education research: A systematic literature review. Stud. High. Educ. 2023, 48, 445–459. [Google Scholar] [CrossRef]
  81. Smolentseva, A. The contributions of higher education to society: A conceptual approach. Stud. High. Educ. 2023, 48, 232–247. [Google Scholar] [CrossRef]
  82. Hou, Y.W.; Jacob, W.J. What contributes more to the ranking of higher education institutions? A comparison of three world university rankings. Int. Educ. J. 2017, 16, 29–46. [Google Scholar]
  83. Larrabee Sønderlund, A.; Hughes, E.; Smith, J. The efficacy of learning analytics interventions in higher education: A systematic review. Br. J. Educ. Technol. 2019, 50, 2594–2618. [Google Scholar] [CrossRef]
  84. Campbell-Whatley, G.D.; Merriweather, L.; Lee, J.A.; Toms, O. Evaluation of Diversity and Multicultural Integration Training in Higher Education. J. Appl. Educ. Policy Res. 2016, 2, 1–14. [Google Scholar]
  85. Aljawarneh, S.A. Reviewing and exploring innovative ubiquitous learning tools in higher education. J. Comput. High. Educ. 2020, 32, 57–73. [Google Scholar] [CrossRef]
  86. García-Morales, V.J.; Garrido-Moreno, A.; Martín-Rojas, R. The Transformation of Higher Education After the COVID Disruption: Emerging Challenges in an Online Learning Scenario. Front. Psychol. 2021, 12, 616059. [Google Scholar] [CrossRef] [PubMed]
  87. Mayer, R.E. Introduction to multimedia learning. In The Cambridge Handbook of Multimedia Learning, 2nd ed.; Cambridge University Press: Cambridge, UK, 2014; pp. 1–24. ISBN 9781139547369. [Google Scholar]
  88. Castilla, N.; Llinares, C.; Bisegna, F.; Blanca-Giménez, V. Affective evaluation of the luminous environment in university classrooms. J. Environ. Psychol. 2018, 58, 52–62. [Google Scholar] [CrossRef]
  89. Kish, L. Survey Sampling; John Wiley and Sons: New York, NY, USA, 1995; ISBN 9780471109495. [Google Scholar]
  90. Wu, Y.; Chen, X.; Li, H.; Zhang, X.; Yan, X.; Dong, X.; Li, X.; Cao, B. Influence of thermal and lighting factors on human perception and work performance in simulated underground environment. Sci. Total Environ. 2022, 828, 154455. [Google Scholar] [CrossRef] [PubMed]
  91. EN 17037:2018+A1:2021; Daylight in Buildings. BSI: London, UK, 2021.
  92. Comrey, A.L.; Lee, H.B. A First Course in Factor Analysis; Taylor & Francis: New York, NY, USA, 1992. [Google Scholar] [CrossRef]
  93. Basilevsky, A. Statistical Factor Analysis and Related Methods: Theory and Applications; Wiley: New York, NY, USA, 1994; ISBN 0471570826. [Google Scholar]
  94. Streiner, D.L. Starting at the beginning: An introduction to coefficient alpha and internal consistency. J. Pers. Assess. 2003, 80, 99–103. [Google Scholar] [CrossRef]
  95. Bellia, L.; Pedace, A.; Barbato, G. Lighting in educational environments: An example of a complete analysis of the effects of daylight and electric light on occupants. Build. Environ. 2013, 68, 50–65. [Google Scholar] [CrossRef]
  96. Vandewalle, G.; Balteau, E.; Phillips, C.; Degueldre, C.; Moreau, V.; Sterpenich, V.; Albouy, G.; Darsaud, A.; Desseilles, M.; Dang-Vu, T.T.; et al. Daytime Light Exposure Dynamically Enhances Brain Responses. Curr. Biol. 2006, 16, 1616–1621. [Google Scholar] [CrossRef]
  97. Gentile, N.; Lee, E.S.; Osterhaus, W.; Altomonte, S.; Naves David Amorim, C.; Ciampi, G.; Garcia-Hansen, V.; Maskarenj, M.; Scorpio, M.; Sibilio, S. Evaluation of integrated daylighting and electric lighting design projects: Lessons learned from international case studies. Energy Build. 2022, 268, 112191. [Google Scholar] [CrossRef]
  98. Wessolowski, N.; Koenig, H.; Schulte-Markwort, M.; Barkmann, C. The effect of variable light on the fidgetiness and social behavior of pupils in school. J. Environ. Psychol. 2014, 39, 101–108. [Google Scholar] [CrossRef]
  99. Gou, Z.; Lau, S.S.Y.; Qian, F. Comparison of mood and task performance in naturally-lit and artificially-lit environments. Indoor Built Environ. 2015, 24, 27–36. [Google Scholar] [CrossRef]
Buildings 14 00770 g001
Figure 1. Research classrooms.
Figure 1. Research classrooms.
Buildings 14 00770 g001
Buildings 14 00770 g002
Figure 2. Study development.
Figure 2. Study development.
Buildings 14 00770 g002
Buildings 14 00770 g003
Figure 3. Ranking of affective impressions, accordingly to the global assessment variable.
Figure 3. Ranking of affective impressions, accordingly to the global assessment variable.
Buildings 14 00770 g003
Buildings 14 00770 g004
Figure 4. Relationship between emotional responses and task factors.
Figure 4. Relationship between emotional responses and task factors.
Buildings 14 00770 g004
Buildings 14 00770 g005
Figure 5. Relationship between affective impressions and type of lighting conditions.
Figure 5. Relationship between affective impressions and type of lighting conditions.
Buildings 14 00770 g005
Buildings 14 00770 g006
Figure 6. Relationship between task factors and type of lighting conditions.
Figure 6. Relationship between task factors and type of lighting conditions.
Buildings 14 00770 g006
Buildings 14 00770 g007
Figure 7. Relationship between lighting global assessment and type of lighting conditions.
Figure 7. Relationship between lighting global assessment and type of lighting conditions.
Buildings 14 00770 g007
Buildings 14 00770 g008
Figure 8. Relationship between lighting global assessment and type of lighting conditions, depending on whether the classroom is dedicated to theory or practice.
Figure 8. Relationship between lighting global assessment and type of lighting conditions, depending on whether the classroom is dedicated to theory or practice.
Buildings 14 00770 g008
Table 1. Indoor lighting environments’ characteristics of the classrooms.
Table 1. Indoor lighting environments’ characteristics of the classrooms.
Dimensions
(W × L × H)
[m]		N. of SeatsFloor LevelType
of Light		N. of
Win-dows		N. of
Roof-lights		Total Window
Area [m2]		Orien-tationSun Shading
System		Type of LampN. of Lumi-nairesN.
of Lamps		Lamps Power
[W]		Em
[lx]		Ēm.wall [lx]U0CRI/
Ra		Colour Temperature
[K]
18.79 × 11.52 × 2.95331stElectric20---Fluorescent272736 4154600.49803210
211.71 × 12.46 × 2.93201stElectric20---Fluorescent313436 4804950.46803160
38.70 × 17.88 × 2.93391stElectric00---Fluorescent333436 4705400.54803050
48.74 × 17.67 × 2.9340Electric00---Fluorescent283236 6004700.59803110
58.72 × 17.56 × 2.90270Electric00---Fluorescent333436 6105500.52803180
68.70 × 7.82 × 2.82251stElectric00---Fluorescent293536 4204150.60803090
78.73 × 17.59 × 2.92400Daylight + Electric10018.26NortheastAluminium blindsFluorescent303436 5404450.65803150
88.70 × 17.74 × 2.94111stDaylight + Electric505.79SouthOpaque roller shutterFluorescent353536 4355200.75803220
98.66 × 17.78 × 2.91131stDaylight + Electric303.93SouthwestAluminium blindsFluorescent323436 4705400.90803050
105.81 × 11.88 × 2.9672ndDaylight + Electric8019.18SouthAluminium blindsFluorescent202036 4604650.80803340
115.89 × 8.92 × 3.00201stDaylight + Electric309.38SouthOpaque roller shutterFluorescent141436 3401150.83803340
128.80 × 10.20 × 3.25630Daylight + Electric4022.95EastOpaque roller shutterFluorescent363636 7503400.66803410
135.84 × 7.42 × 4.7572ndDaylight1112.68WestTranslucent glass----3954700.75-3940
147.85 × 35.93 × 4.73372ndDaylight8687.72North-----3053650.80-4000
154.86 × 8.89 × 3.74482ndDaylight8657.42SouthwestOpaque roller shutter----9808900.86-3420
168.83 × 35.96 × 4.74502ndDaylight3 + 6616.32 +
64.47		Northeast-/Translucent glass----3854200.78-3945
177.75 × 12.70 × 2.82412ndDaylight9035.88SoutheastOpaque roller shutter----5204200.95-3940
Table 2. Participants.
Table 2. Participants.
AgeGender
FrequencyPercentage FrequencyPercentage
<2018335.12%Male23244.50%
20–2430458.35%Female28955.50%
25–29295.57%
>3050.96%
Total521100%Total521
Table 3. Variance.
Table 3. Variance.
ComponentInitial EigenvaluesRotation Sums of Squared Loadings
Total% of VarianceTotal% of VarianceCumulative %
111.2429.576.0715.9715.97
24.5612.003.8610.1526.12
33.108.163.679.6635.78
41.584.163.398.9144.69
51.132.973.378.8853.57
61.092.882.346.1659.73
Table 4. Meaning of affective responses or Kasei semantic axes.
Table 4. Meaning of affective responses or Kasei semantic axes.
AxesMeaning of the AxesConcepts IncludedVariance
Explained		Cronbach’s
Alpha
1Surprising–amazingSurprising (0.851); amazing (0.820); awesome (0.801); stimulating (0.749); interesting (0.701);
original (0.676); suggestive (0.563); enabling (0.462)		15.97%0.91
2Clear–efficientClear (0.705); sharp, defined (0.679); efficient (0.652); with quality, rich (0.650); bright (0.640); pleasant (0.498); satisfactory (0.460); convenient (0.415)10.15%0.85
3Soft–calmSoft (0.648); quiet (0.647); calm (0.646);
dim, subtle (0.622); warm (0.526); natural (0.455); protector (0.453); cosy (0.430)		9.66%0.80
4UniformUniform (0.772); homogeneous (0.729);
orderly (0.720); balanced (0.683); functional (0.619)		8.91%0.82
5Lively–cheerfulLively (0.711); dynamic (0.667); cheerful (0.582); friendly (0.525); colourful (0.518); beautiful (0.496)8.87%0.86
6Brilliant–glaringBrilliant (0.740); glaring (0.702); intense (0.692)6.16%0.70
Table 5. Task factor analysis.
Table 5. Task factor analysis.
FactorMeaning of the FactorsConcepts IncludedVariance ExplainedCronbach’s Alpha
1Writing–readingWriting (0.867); reading (0.825)
drawing (0.822);
reviewing notes (0.658)		21.91%0.91
2Discussing–teamworkDiscussing (0.816); teamwork (0.716); correcting (0.630); asking the teacher (0.602); reflecting (0.554)20.22%0.85
3Paying attentionPaying attention to the board (0.838); paying attention (0.745);
looking at the projector (0.606)		15.04%0.80
4Using devicesWorking on the computer (0.768);
using the telephone, tablet (0.770)		13.22%0.82
Table 6. Comparison of axes scores across ANOVA analysis type of lighting results.
Table 6. Comparison of axes scores across ANOVA analysis type of lighting results.
SSdfMSFSig
Surprising–amazingBetween Groups
Within Groups
Total		16.033
499.967
516.000		2
514
516		8.016
0.973		8.2410.000
Clear–efficientBetween Groups
Within Groups
Total		19.315
496.685
516.000		2
514
516		9.657
0.966		9.9940.000
Soft–calmBetween Groups
Within Groups
Total		35.754
480.246
516.000		2
514
516		17.877
0.934		19.1340.000
UniformBetween Groups
Within Groups
Total		11.965
504.035
516.000		2
514
516		5.983
0.981		6.1010.002
Lively–cheerfulBetween Groups
Within Groups
Total		16.986
499.014
516.000		2
514
516		8.493
0.971		8.7480.000
Brilliant–glaringBetween Groups
Within Groups
Total		0.144
515.856
516.000		2
514
516		0.072
1.004		0.0720.931
Table 7. Analysis of variance (ANOVA) to compare the task factors and type of lighting scores.
Table 7. Analysis of variance (ANOVA) to compare the task factors and type of lighting scores.
SSdfMSFSig
Writing––reading Between Groups
Within Groups
Total		0.753
518.247
519.000		2
517
519		0.376
1.002		0.3750.687
Discussing–
teamwork		Between Groups
Within Groups
Total		14.329
504.671
519.000		2
517
519		7.165
0.976		7.3400.001
Paying attentionBetween Groups
Within Groups
Total		7.717
511.283
519.000		2
517
519		3.858
0.989		3.9020.021
Using devicesBetween Groups
Within Groups
Total		5.114
513.886		2
517
519		2.557
0.994		2.5720.077
Table 8. Analysis of variance (ANOVA) to compare the global assessment and type of lighting scores.
Table 8. Analysis of variance (ANOVA) to compare the global assessment and type of lighting scores.
SSdfMSFSig
I like itBetween Groups
Within Groups
Total		71.806
600.072
671.877		2
518
520		35.903
1.158		30.9920.000
Well illuminatedBetween Groups
Within Groups
Total		13.838
612.385
626.223		2
518
520		6.919
1.182		5.8530.003
Adequate lightingBetween Groups
Within Groups
Total		17.976
465.912
483.889		2
518
520		8.988
0.899		9.9930.000
Adequate lighting for theoryBetween Groups
Within Groups
Total		4.967
496.991
501.958		2
518
520		2.484
0.959		2.5890.076
Adequate lighting for practiceBetween Groups
Within Groups
Total		47.624
604.057
651.681		2
518
520		23.812
1.166		20.4200.000
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Castilla, N.; Blanca-Giménez, V.; Pérez-Carramiñana, C.; Llinares, C. Differences between Daylighting and Electric Lighting in Affective Response. Buildings 2024, 14, 770. https://doi.org/10.3390/buildings14030770

AMA Style

Castilla N, Blanca-Giménez V, Pérez-Carramiñana C, Llinares C. Differences between Daylighting and Electric Lighting in Affective Response. Buildings. 2024; 14(3):770. https://doi.org/10.3390/buildings14030770

Chicago/Turabian Style

Castilla, Nuria, Vicente Blanca-Giménez, Carlos Pérez-Carramiñana, and Carmen Llinares. 2024. "Differences between Daylighting and Electric Lighting in Affective Response" Buildings 14, no. 3: 770. https://doi.org/10.3390/buildings14030770

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop