Abstract
In this work, kindergarten toy storage, defined as the construction area, puzzle area, scientific observation area, and role-playing area, and the toy characteristics of the four areas, was studied. Interviews and grounded theory were used to observe and summarize the behavioral needs of 3- to 6-year-old children and preschool teachers. Analytical hierarchy process (AHP) was used to analyze behavioral needs. It was concluded that the kindergarten toy locker optimization was designed to improve storage efficiency. However, the current layout of kindergarten toy lockers is chaotic, and children cannot efficiently and autonomously take toys from toy lockers. The best toy locker layout scheme was selected through an eye tracking experiment. The subjects were all 3- to 6-year-old children, a total of 30 people. By comparing the data such as hot spot map, trajectory map, area of the first viewpoint, and gaze time when children observed different lockers layout during the experiment, the optimal layout scheme of kindergarten toy lockers was comprehensively analyzed. Optimizing the zoning, classification, and storage of kindergarten toys is conducive to improving the efficiency of children’s independent storage, creating a kindergarten game and teaching environment conducive to children’s development.
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Design of Kindergarten Toy Lockers
Yanfeng Miao,a Xiaojie Xie,a Wenye Qi,a and Wei Xu b
In this work, kindergarten toy storage, defined as the construction area, puzzle area, scientific observation area, and role-playing area, and the toy characteristics of the four areas, was studied. Interviews and grounded theory were used to observe and summarize the behavioral needs of 3- to 6-year-old children and preschool teachers. Analytical hierarchy process (AHP) was used to analyze behavioral needs. It was concluded that the kindergarten toy locker optimization was designed to improve storage efficiency. However, the current layout of kindergarten toy lockers is chaotic, and children cannot efficiently and autonomously take toys from toy lockers. The best toy locker layout scheme was selected through an eye tracking experiment. The subjects were all 3- to 6-year-old children, a total of 30 people. By comparing the data such as hot spot map, trajectory map, area of the first viewpoint, and gaze time when children observed different lockers layout during the experiment, the optimal layout scheme of kindergarten toy lockers was comprehensively analyzed. Optimizing the zoning, classification, and storage of kindergarten toys is conducive to improving the efficiency of children’s independent storage, creating a kindergarten game and teaching environment conducive to children’s development.
DOI: 10.15376/biores.19.1.434-455
Keywords: Kindergarten toy lockers; AHP; Eye-movement experiment; Storage efficiency
Contact information: a: College of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, China; b: Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources; * Corresponding author: myf1203@163.com
Research Background
In general, there is a lack of research on the design of kindergarten furniture, especially research that focuses on user needs and relevant research that utilizes the children’s group as the conceptual research object. Published work, up to this point, not only lacks in-depth children’s group face-to-face observation, but deep excavation of the real needs of the children’s group. Thus, studies cannot meet the actual needs of the target users.
In the early stage, the observation method was used to investigate the storage behavior of children. It was found that the kindergarten toy locker is the main furniture in the play area. The locker serves as a regional space division tool. While due to batch customization or procurement, the internal division of the cabinet does not provide characteristic differences, and the location of each play area is close to each other, regional characteristics that are vague do not facilitate children to play area identification, and labels should be used to for indication. Children can use this series of actions as an educational game when putting toys into correct places. However, in the process of this game, the children may not be easily able to identify complex visual information features, resulting in toy storage errors. This not only affects the experience of children’s play, but it also increases the workload of preschool teachers. Therefore, it is necessary and feasible to research kindergarten toy lockers to reduce children’s storage error rate and improve storage efficiency.
Research Review
Research related to user behavior and user needs
Generally, in the research process, through in-depth interviews, questionnaire surveys and behavioral observations (Village et al. 2015; Merbah et al. 2020; Konstanti et al. 2021), analysis of the needs of target users can be carried out to clarify the explicit and implicit needs of target users (van Liempd et al. 2018; Richter et al. 2019). If there are too many demand points, SPSS software can be used for principal component analysis or the AHP method used for weight calculation, and the main requirements of users can be determined based on the weight results, which would allow targeted design optimization (Yang et al. 2019; Neira-Rodado et al. 2020; Wang et al. 2021).
Grounded theory is a qualitative research method that uses systematic procedures to develop and inductively guide grounded theory for a certain phenomenon. Grounded theory contains initial encoding, focus encoding, and theoretical encoding. Zhou et al. (2023) used grounded theory to code and analyze the humanistic care factors to improve the design standards of intelligent elderly products and to promote the more rational use of humanistic care factors in design. Grounded theory can effectively study the needs and behavioral characteristics of target users. Cheer et al. (2015) have found that researchers are increasingly using grounded theory methodologies to study the professional experience of nurses and midwives.
Emotion plays an important role in the use of furniture. Through combining case study and field investigation, the user emotion can be integrated, the user’s emotional response can be identified, and the user’s emotional evaluation can be analyzed, which can enrich the research of emotional design (Cheng et al. 2020; Angelaki et al. 2022).
Research on the design of kindergarten furniture
The research-literature for kindergarten furniture is relatively small, mainly in the field of storage, sitting furniture, activity area furniture, and interior space-related design studies (Cheng et al. 2019). The design strategy is mainly reflected in the playful, guidance, modularity, safety, fun, growth, and emotional design methods (Purwaningrum et al. 2015; Gimenez et al. 2016). Fun and emotionality are concentrated in the color, shape, and material performance of kindergarten furniture. Safety is expressed in the stability and physiological appropriateness of kindergarten furniture. Guidance is mainly manifested in the design of storage guidance for young children. Playfulness and interest are expressed in the integration of game mechanisms and interesting elements into furniture design to increase the interaction between children and furniture. Modularity is expressed in the design of furniture that can be combined and disassembled to meet a variety of functional forms by building combinations.
Choi et al. (2016) presented a CMF (Color, Material, Finishing) strategy for a table in kindergarten classroom. The multi-sensory CMF strategy can deeply understand the characteristics of children’s development. Through comparing the developmental characteristics of the kindergarten students with those of the CMF to derive the multi-sensory design element, the multi-sensory design elements can be obtained. Design factors derived from the development process of kindergartners are classified into keywords and used for table analysis for kindergartners.
Research on children’s furniture design
Some scholars have studied children’s furniture from the perspective of furniture size or furniture color. Sejdiu et al. (2023) obtained anthropometric data of primary school students in the Republic of Kosovo by measuring the body parts of 720 students in 12 different primary schools in four different regions of the Republic of Kosovo. Based on the study, they provided effective recommendations for school furniture design. Jiang et al. (2020) analyzed children’s influence on furniture selection from the perspective of color preference. The study found that the influence varies with different types of furniture. In addition, children have slightly different preferences for furniture in different functional spaces, and children of different genders and ages have different choices in furniture color.
Environmentally friendly design is increasingly and widely used in the field of children’s furniture design. Environmentally friendly design follows the theory of sustainable development (Tian et al. 2018; Peng et al. 2021). Such design practices recycle resources and provide a scientific basis for product rationality (Vidal et al. 2022). The environmentally friendly design of children’s furniture has a significant impact on children’s growth. Wei and Madina (2022) focused on the use of environmentally friendly materials in the design of children’s furniture and combined fuzzy technology with structured design technology to build a fuzzy technology-based children’s furniture design system.
In the process of emotional design, children’s furniture design needs to consider the application of artificial intelligence design concept, to meet the rapid growth of children’s physical and mental needs. Through discussing the necessity and design principle of the application of artificial intelligence in children’s furniture from the perspectives of society, children, and development trends, it can lay a foundation for interesting and multifunctional children’s furniture design (Zhang and Li 2022).
Eye tracker and furniture design
Eye tracking technology, based on human visual attention mechanism, is an objective and effective means of research and a feasible method to solve objective problems (Deng and Gao 2023). Eye movement data reflect objective indicators of human eye movement behavior and focus of attention, and each indicator has a specific meaning and scope of application (Chang et al. 2016). It is one of the focuses of this study to select appropriate indicators to establish the relationship between eye movement data and user morphological elements intention preference (Zhagn and Xu 2020). Through eye-tracking technology, one can extract the elements of furniture shape design, study the cognitive intention preference of shape features, and study the color preference of furniture (Xu and Zhang 2012; Liu et al. 2018). In the existing papers, no scholars have used eye-tracking technology to study children’s toy storage furniture, so the use of eye-tracking technology to study the layout of children’s toy lockers has innovative significance.
EXPERIMENTAL
Kindergarten toy storage defined
According to the definition of the unitary activity in kindergarten, the classroom is the main environment for children’s teaching and play activities. The classroom usually contains various types of regional activity spaces. The different activity areas are the unitary parts that constitute the overall classroom environment. Zhu (2019) summarized the views of experts and scholars in a study on the guidance strategies of kindergarten area activities, and defined area activities as areas where teachers create a reasonable teaching environment for children according to their educational goals and characteristics, and meet children’s free, autonomous, and self-selected interactive operation activities by putting in play materials and intervening to guide them. Generally, they set up constructive areas, educational areas, science areas, play areas, and art areas. These areas are set with construction area, puzzle area, science area, play area, artwork area, etc. As shown in Table 1, there are four main activity areas where the materials are toys: construction area, puzzle area, science observation area, and role-playing area, and other activity areas where the materials are not toys, so they are not included in the present study.
Table 1. Attributes of Toys and Materials Placed in the Activity Area
Based on the above situation, toy storage in kindergarten is defined as toy material storage in construction area, educational area, scientific observation area, and role-playing area.
Kindergarten Toy Storage Features
Characteristics of toy materials in the construction area
The main development area is health-related, through the development of children’s fine motor and creative imagination, the main core experience is to develop children’s hand-eye coordination and organization and construction ability, to understand the nature of various construction materials, to learn the spatial relationships, to understand the concept of whole and part, and to enhance the understanding of quantity and figure. The main core experiences are aimed to promote the development of their perception and thinking, and to improve their aesthetic ability in constructing shapes.
The toys in the construction area are mainly wooden blocks, which can be divided into small blocks, medium blocks, and large blocks in terms of their size (Fig. 1).
Characteristics of toy materials in the puzzle area
The toys in the puzzle area are diverse. To ensure that children explore and discover in rich materials, enough toy materials should be put in the puzzle area, which can provide children with more opportunities to choose and effectively reduce the phenomenon that children do not use enough toys, which may result in them arguing with each other.
The toys in the puzzle area are divided into four categories: puzzles, cards, rules, and math, including pegs and stacking toys to develop children’s fine motor skills; cards, including phonics cards and puzzles to develop children’s reading and writing skills, and cognitive skills; tabletop games and board games to exercise children’s thinking skills; and math, including graphical matching and arithmetic games to improve children’s mathematical and graphical cognitive skills. To enhance children’s mathematical cognition and graphics cognitive ability, the most used educational toys are puzzles, chess toys, counting toys, and literacy and phonics cards (Fig. 2).
Characteristics of toy materials in the science observation area
The toys and materials in the science observation area are mainly scientific observation instruments and props and game materials. The main developmental elements are intended to exercise children’s ability to discover and solve problems and to develop and deepen scientific cognition. The number and types of toy materials are smaller compared to the educational area. The toys in the science observation area are mostly in bulk form, small in size, and difficult to calculate, and require baskets for storage. The storage for children is to observe and manipulate anytime and anywhere. The most commonly used toys in the science observation area are magnet toys and magnifying glasses (Fig. 3).
Characteristics of toy materials in the role-playing area
The toy materials in the role-playing area are mainly situational simulation toy sets and include props and materials such as simulated food and household items. The game is played by children acting out the role of different occupations, using simulated toys to simulate and restore the work or life scenes of adults, rehearsing and communicating with each other, mainly to cultivate children’s social interaction and adaptability, and to exercise children’s communication and expression skills (Fig. 4).
Distribution of applicable objects of toys
Based on the requirements of preschool education for early childhood development, toys were classified into six types that can serve in the education of preschoolers and can be understood and filled by early childhood educators. One-way analysis of variance (ANOVA) was conducted using SPSSAU software (Beijing Qingsi Technology Co., Ltd., version 23.0, Beijing, China) to test and analyze whether the number of toys of each type differed across age classes. As shown in Table 2, the significance levels for all six toy categories were greater than 0.05, indicating that there was no significant difference in the number of toys in the puzzle category across age classes. This reflects that the age differences in toy manipulation among preschoolers were not significant.
Table 2. Categorization of Toys
Study of Human Behavior
Qualitative interviews were conducted with kindergarten teachers to understand the behavioral needs of children and teachers in the process of using toy lockers. The collected interview records were converted into text. Qualitative research methods and grounded theories were used to analyze the interview results, which were divided into three steps, namely initial coding, focused coding, and theoretical coding, to obtain the information of teacher user groups for kindergarten toy lockers.
The initial coding is the process of the primary analysis, comparison, and screening of the original interview text data to find the conceptual class, and conceptualization and labeling by extracting keywords. A total of 22 conceptual coding results were extracted from the interviews. The specific initial coding is shown in Table 3.
Focus coding is the process of transforming empirical descriptions into analytical concepts, extracting the most important or most frequently occurring concept codes for correlation, classification, and summarization, as well as merging abstract concept genera, which is a process of cluster analysis, forming main categories and sub-categories from top to bottom, and finally obtaining five main categories. These include functional improvement and optimization, appearance and styling, functional extension, operational requirements, and user experience, specifically the specific focus codes are shown in Table 4.
Table 3. The Process of Initial Encoding
Table 4. The Process of Focus Encoding
The theoretical encoding was completed by further integrating and condensing the conceptual categories coded in the first two stages to form the core category, with the core content being user needs. The interview data of the two interviewed users set aside were tested for saturation around the theory of user needs. No new initial conceptual categories or new relational links emerged during the coding process, so it was determined that the theory had reached saturation, and the primary and secondary categories were more complete. All category classes were logically concatenated to create a theoretical analysis model of user needs, as shown in Table 5.
Table 5. Influencing Factors of User Needs
User behavior demand weighting analysis
The AHP is a system analysis method for evaluation and decision-making, which has the characteristics of combining qualitative and quantitative items. It can make a clear evaluation of fuzzy and difficult-to-quantify problems (Cui et al. 2022). Through establishing a fuzzy consistent judgment matrix through pairwise comparison of elements, the qualitative indexes are converted into quantitative data, the comprehensive weights of each element are calculated, and the different requirements are prioritized to provide an objective scientific theoretical basis for design.
Fig. 5. Analytic hierarchy model
The corresponding hierarchical analysis model (Fig. 5) was constructed by combining the interview user demand influence factor model, including the target layer, the criterion layer (5 elements), and the indicator layer (11 elements), and then the interview user demand influence factor model was constructed into a comparison matrix Y.
In the matrix Y, yij (i = 1,2,…,n;j = 1,2,…,n;n = 11) is the important judgment of element i compared with element j, y = Biji : Bj , then yji = 1/yij . yij = Bi : Bj , then yji = 1/yij. Let the maximum characteristic root of the judgment matrix be λmax, and the normalized eigenvector of each element be the weight W. According to the 9-level scale method of judgment matrix elements for the relative importance of paired elements, five experts were invited to form an evaluation team to judge the elements of each level in pairs, construct the judgment matrix of each level index, and use SPSS to calculate the weight value of all evaluation indexes as Guideline layer:
The CR values of the target and criterion layers are less than 0.1, which means that the matrix Y meets the requirements through the consistency test, so the combination weight W can be used as the basis for decision-making. In the criterion layer, function improvement and optimization (0.4321) > function expansion (0.2365) > operation requirement (0.1327) > appearance modeling (0.1255) > using experience (0.0731); in the index layer, the top five elements in order of importance are improving storage efficiency (0.2801), improving fun (0.1774), openness of space (0.0992), ease of movement (0.0885), and play area identification (0.0837). To make the design goal clear, this kindergarten toy locker optimization is designed to improve storage efficiency.
Locker optimization design
Combined with the above research on the toy storage characteristics of kindergarten toys, among the four activity areas where the materials put in are toy attributes, the toy characteristics of the role-playing area differ significantly from those of the other three areas, and the toys in the role-playing area have little impact on the overall storage efficiency of children in the process of storing toys. The study was conducted in the three other areas (puzzle area, construction area, and science observation area). The puzzle area contains four kinds of toys: puzzles, chess toys, counting toys, and phonics cards; the construction area contains two kinds of toys: small blocks and large blocks; the science area contains two kinds of toys: magnet toys and magnifying glasses.
To adapt to the standardization and automation of the production of kindergarten furniture products, the variability of the internal dimensions of the lockers should be minimized, so the lockers are divided into 4 layers, where each layer has 6 grids of the same size. The internal grid of the locker is 350 mm long, 300 mm high, and 430 mm deep. The overall length of the locker is 2226 mm, the overall width is 450 mm, and the overall height is 1300 mm.
The lockers are partitioned by color, and the matching choice of color and toy area, combined with the questionnaire and color semantics, results in the original wood color representing the construction area, accounting for 6 grids; yellow representing the puzzle area, accounting for 12 grids; and blue representing the science area, accounting for 6 grids.
Purpose of the experiment
The eye-tracking device was used to capture the subjects’ eye-movement data when observing the experimental pictures. The representational data when interpreting the subjects’ information was collected in each region using the division of interest zones. The authors analyzed the presentation of the subjects’ visual information in different locker division layouts to determine the subjects’ eye-movement gaze sequence pattern, visual field distribution, and information acquisition difficulty. After the eye-movement experiment, the locker layout with the highest storage efficiency was inferred based on the eye-movement data results to optimize the locker design.
Subject selection and experimental preparation
The subjects were all young children aged 3 to 6 years old, 30 in total. There were 13 boys and 17 girls. All subjects were asked about their experience with kindergarten toy lockers and had some knowledge of the product features. The subjects’ visual acuity was above 1.0 with bare eyes or corrected eyes and no astigmatism. The subjects were trained before the start of the experiment and were able to successfully complete the eye-movement data capture. The experiment was conducted using a Tobii Pro Fusion telemetric oculometer (Tobii Pro Fusion, Tobii, Stockholm, Sweden) with a sampling frequency of 250 Hz and Ergolab eye-tracking software (KingFar International Inc, version: 3.17.2, Beijing, China), which consists of an experimental design module, an eye capture module, a recording module, control software, and data analysis software to obtain natural eye-movement behavior data. There were no visual and auditory interference factors during the eye movement experiment to ensure the accuracy of the eye movement experiment.
Experimental steps
Before the formal experiment began, the staff introduced the basic situation of the kindergarten toy locker and the experimental procedure to the subjects and assigned the experimental task. The experimental task was: after each picture of the toy locker appeared, read every label on the locker according to their own eye-movement habits, and try to read the same labels together (the label colors were unified to reduce the impact of the differences between the labels on the subjects’ vision). After each picture, click the left mouse button to switch to the next one automatically.
There were three groups of pictures as follows: the first group had only one locker picture without color differentiated layout; the second group had 13 locker pictures with different layouts, all using original wood, and were yellow and blue for differentiation; the third group also had 13 locker pictures with different layouts, but their layouts corresponded to the second group one by one, and the colors were further subdivided by shades of hues. Each layout case of the second and third groups was set up with the corresponding AOI (Automated Optical Inspection) interest area according to the type of toys (puzzle, construction, or science).
The above experimental procedure was repeated for each subject until the end of all samples played. The eye-movement experimental data were considered valid if the subjects did not feel obvious fatigue during the experiment. The experimental equipment and field conditions are shown in Fig. 6.
Fig. 6. Experimental site
RESULTS
Table 6 shows the hot spot map and the trajectory map of the eye tracking experiment above. Table 7 shows the area of the first viewing point of the eye tracking experiment above.
(1) Hotspot diagrams were used to observe the attention distribution of the subjects. The hotspot map is a common representation of eye-movement data, which can visually reflect the subjects’ attention to each locker grid. The red area of the hotspot map indicates the most concentrated area of browsing and gazing, and the larger the area indicates the more focused the subject’s vision. The longer the subject spends in a certain area of interest, the stronger the intentional preference for that storage area, and the increase in feedback time leads to a darker color of the hotspot map (Liang et al. 2022).
The comprehensive analysis of the color shades and area size of each group of pictures showed that the subjects’ visual field was more concentrated in the middle and upper part of the cabinet, and the observation time was shorter at the sides and bottom of the cabinet.
(2) The eye-tracking diagram was used to obtain the subjects’ tendency of browsing order (Niu and Huang 2022). Most of the subjects focused their first sight on the center and upper area of the interface, and then scanned each tab one by one, resulting in a “Z”-shaped scanning result. However, the stepped and T-shaped layout will affect the scanning results; some more complex layouts will increase the sequence and repeat the areas already seen.
Table 6. Experimental Hot Spot Map and Trajectory Map
Table 7. Area of the First Viewing Point
In the table, P represents puzzle area, C represents construction area, and S represents science observation area.
In the second group of 13 scenarios, the shortest average gaze time was for serial number 2-11, followed by serial number 2-13, and then serial number 2-12. The duration was 7.487 s, 7.610 s, and 7.669 s, respectively; the longest average gaze time was for serial number 2-9, followed by serial number 2-4, and then serial number 2-8. The duration was 10.001 s, 9.295 s and 9.211 s, respectively.
Among the 13 scenarios in the third group, the shortest average gaze time was for serial number 3-12, followed by serial number 3-11, and then serial number 3-13. The duration was 6.224 s, 6.232 s, and 6.257 s, respectively; the longest average gaze time was for serial number 3-9, followed by serial number 3-4, and then serial number 3-10. The duration was 8.443 s, 8.282 s and 8.246 s, respectively (Table 8).
Table 8. Average Gaze Time
The average gaze time for the first group was 9.828 s, the overall average gaze time for the second group was 8.729 s, and the overall average gaze time for the third group was 7.505 s. It can be seen that the three-color partitioning was better than the colorless partitioning in terms of information extraction efficiency, while adding hues for partitioning was better than the three-color partitioning in terms of information extraction efficiency. Combined with the eye-trajectory diagram, it can be seen that when the subjects observed the third group of locker samples with increased hue, they reduced the unnecessary looking back due to the difference in hue, which effectively reduced the information extraction time.
Optimal layout selection
The data of total gaze time for the three samples with serial numbers 3-11, 3-12, and 3-13 were analyzed in conjunction with SPSS. The results of the chi-square test in Tables 9 and 10 showed that the one-way variance significance level was greater than 0.05, indicating that there was no significant difference in the subjects’ total gaze time when observing the above three samples with different layouts.
Table 9. Chi-square Test 1
Table 10. Chi-square Test 2
The standard deviation of the total gaze time was calculated for samples 3-11, 3-12, and 3-13 (Table 11). From the table, the standard deviation of sample 3-13 was the lowest among the three samples, indicating that the data of this sample were less discrete than the remaining two samples in terms of total gaze time. Therefore, sample 3-13 was selected as the optimal toy locker layout solution.
Table 11. Standard Deviation of Total Gaze Time
DISCUSSION
To make the design objectives clear, AHP hierarchical analysis was used for the determination of each requirement weight, but the method has certain limitations: AHP hierarchical analysis has a strong subjective character in the process of determining the index weight vector, so it affects the results of each requirement weight to some extent.
The eye-movement experiment sample only distinguishes the layout from color and further divides the kindergarten toy locker from hue through color semantics, and subsequent design optimization can be done from the tactile and material texture level of partitioning, and conduct in-depth research on the physical, chemical, mechanical, and technological properties of storage cabinet materials to explore whether the efficiency of storage for young children can be further improved based on the current study.
During the eye-movement experiment, children first gazed at the AOI area mostly in the puzzle area, which was influenced by two main factors in addition to the subjects’ eye-movement manipulation habits:
- The yellow color for the puzzle area is more saturated and brighter than the blue color for the science area and the log color for the construction area, and children’s eyes are more likely to be influenced by it and look at it for the first time.
- The focus point is related to the size of the layout. The layout of the puzzle area accounts for 50% of the overall layout, while the layout of the science area and the construction area each account for 25% of the overall layout. Therefore, the puzzle area is more likely to be watched than the science observation area and construction area.
SUMMARY
This study investigated the storage behavior and needs of special groups in kindergartens, and it was concluded that the difficulty encountered by users in the operation and use of kindergarten toy lockers was that they cannot efficiently store toys. By conducting eye movement experiments related to the layout of kindergarten toy lockers for children aged 3 to 6 and comprehensively analyzing different eye movement data, the layout of toy lockers that is most conducive to children’s efficient storage was obtained, so that toy lockers could better guide and cultivate children’s classification storage cognition, reduce the error rate of toy classification and partition storage, and carry out systematic development, design and research on kindergarten toy lockers.
This study explored the optimization of the layout of lockers through eye movement experiments. The proper layout of storage partitions has great significance for children: in the game of toy storage, they become more efficient, making them more willing to actively put things in order. Therefore, it can effectively mobilize children’s classified storage ability and enthusiasm and cultivate personal life skills and good habits. In addition, a reasonable storage partition layout is also conducive to improving the quality of kindergarten game education facilities and creating a kindergarten play environment and teaching environment conducive to the development of children.
FUNDING
Project Funding from National Art Fund Traditional Woodcarving and Modern Creative Talent Training Project [2019-A-04-(149)-0691] is greatly acknowledged.
REFERENCES CITED
Angelaki, S., Triantafyllidis, G. A., and Besenecker, U. (2022). “Lighting in kindergartens: Towards innovative design concepts for lighting design in kindergartens based on children’s perception of space,” Applied Sustainability 14(4), article 2302. DOI: 10.3390/su14042302
Chang, W.-D., Cha, H.-S., and Im, C.-H. (2016). “Removing the interdependency between horizontal and vertical eye-movement components in electrooculograms,” Applied Sensors 16(2), article 227. DOI: 10.3390/s16020227
Cheng, I.-F., Kuo, L.-C., Lin, C.-J., and Chieh, H.-F., and Su, F.-C. (2019). “Anthropometric database of the preschool children from 2 to 6 years in Taiwan,” Journal of Medical and Biological Engineering 39, 552-568. DOI: 10.1007/s40846-018-0436-4
Cheng, X., Hou, W., and Sheng, Q. (2020). “Affective design in children products: A case study of toy storage product,” in: 2020 13th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China, pp. 136-139. DOI: 10.1109/ISCID51228.2020.00037
Cheer, K., MacLaren, D., and Tsey, K. (2015). “The use of grounded theory in studies of nurses and midwives’ coping processes: a systematic literature search,” Contemporary Nurse 51(2-3), 200-219. DOI: 10.1080/10376178.2016.1157445
Choi, Y. Joonga and Choi, M.-S. (2016). “A study on the establishment of CMF strategy for multi-sensing furniture design for kindergartener-focusing on set of tables that help to improve creativity,” Journal of Basic Design & Art 17(6), 617-632.
Cui, X., Ge, M., and Shen, X. (2022). “Application of comprehensive evaluation in new-product-development evaluation: The case of landscape-architectural outdoor wooden furnishing,” Forests 13(10), article 1552. DOI: 10.3390/f13101552
Deng, R., and Gao, Y. (2023). “A review of eye tracking research on video-based learning,” Education and Infomation Technologies 28, 7671-7702. DOI: 10.1007/s10639-022-11486-7
Gimenez, R., do Nascimento Soares, R., Ojeda, V. V., Makida-Dionísio, C., and de J. Manoel, E. (2016). “The role of school desk on the learning of graphic skills in early childhood education in Brazil,” SpringerPlus 5, article 1120. DOI: 10.1186/s40064-016-2554-1
Jiang, L., Cheung, V., Westland, S., Rhodes, P. A., Shen, L., and Xu, L. (2020). “The impact of color preference on adolescent children’s choice of furniture,” Color Research & Application 45(4), 754-767. DOI: 10.1002/col.22507
Konstanti, C., Karapanos, E., and Markopoulos, P. (2021). “The behavior change design cards: A design support tool for theoretically-grounded design of behavior change technologies,” International Journal of Human–Computer Interaction 38, 1238-1254. DOI: 10.1080/10447318.2021.1990519
Liang, H., Liu, Y., and Chen, Y. (2022). “Research on morphological design elements of office chair based on eye movement tracking technology,” Journal of Forestry Engineering 2022, 7(02), 207-214. DOI:10.13360/ j.issn.2096-1359.202104027
Liu, Y., Zhou, Y., Qiu, H., and Shen, L. (2018). “Visual characteristics study of traditional round-backed armchair based on eye-tracking,” in: Man–Machine–Environment System Engineering, S. Long, and B. Dhillon (eds), MMESE 2018 paper, Springer, Singapore, pp. 95-104. DOI: 10.1007/978-981-10-6232-2_12
Merbah, J., Gorce, P., and Jacquier-Bret, J. (2020). “Interaction with a smartphone under different task and environmental conditions: Emergence of users’ postural strategies,” International Journal of Industrial Ergonomics 77, Article 102956. DOI: 10.1016/j.ergon.2020.102956
Neira-Rodado, D., Ortíz-Barrios, M., De la Hoz-Escorcia, S., Paggetti, C., Noffrini, L., and Fratea, N. (2020). “Smart product design process through the implementation of a fuzzy Kano-AHP-DEMATEL-QFD approach,” Applied Sciences 10(5), article 1792. DOI: 10.3390/app10051792
Niu X., and Huang J. (2022). “Research on backrest modeling of Ming-style furniture with full carving using technology of eye tracking,” Journal of Forestry Engineering 7(03), 200-206. DOI:10.13360/j.issn.2096-1359.202108018
Peng, C., Feng, D., and Guo, S. (2021). “Material selection in green design: A method combining DEA and TOPSIS,” Sustainability 13(10), article 5497. DOI: 10.3390/su13105497L
Purwaningrum, L., Funatsu, K., Xiong, J., and Nur-Rosyidi, C., and Muraki, S. (2015). “Effect of furniture weight on carrying, lifting, and turning of chairs and desks among elementary school children,” Applied PLoS ONE 10(6), article e0128843. DOI: 10.1371/journal.pone.0128843
Richter, H. O., Sundin, S., and Long, J. (2019). “Visually deficient working conditions and reduced work performance in office workers: Is it mediated by visual discomfort?,” International Journal of Industrial Ergonomics 72, 128-136. DOI: 10.1016/j.ergon.2019.05.007
Sejdiu, R., Sylejmani, B., Idrizi, L., Bajraktari, A., and Sejdiu, M. (2023). “Discrepancy between pupils’ body and classroom furniture in elementary schools: A case study in the Republic of Kosovo,” Work-a Journal of Prevention Assessment & Rehabilitation 75(2), 447-459. DOI: 10.3233/WOR-220023
Tian, G., Zhang, H., Zhou, M., and Li, Z. (2018). “AHP, Gray correlation, and TOPSIS combined approach to green performance evaluation of design alternatives,” IEEE Transactions on Systems, Man, and Cybernetics: Systems 48(7), 1093-1105. DOI: 10.1109/TSMC.2016.2640179
van Liempd, H. I. M., Oudgenoeg-Paz, O., Fukkink, R. G., and Leseman, P. P. (2018). “Young children’s exploration of the indoor playroom space in center-based childcare,” Early Childhood Research Quarterly 43, 33-41. DOI: 10.1016/j.ecresq.2017.11.005
Vidal, D., and Seixas, E. (2022). “Children’s green infrastructure: Children and their rights to nature and the city,” Frontiers in Sociology 7, article 804535. DOI: 10.3389/fsoc.2022.804535
Village, J., Searcy, C., Salustri, F., and Patrick Neumann, W. (2015). “Design for human factors (DfHF): A grounded theory for integrating human factors into production design processes,” Ergonomics 58, 1529-1546. DOI: 10.1080/00140139.2015.1022232
Wang, Y., Xu, Y., Jiang, Y., Wang, Z., and Niu, J. (2021). “Research on multi-sensory interactive toy intelligent storage box for young children’s behavioral characteristics,” in: 2nd International Conference on Intelligent Design (ICID), Xi’an, China, pp. 407-411. DOI: 10.1109/ICID54526.2021.00086
Wei, P., and Madina, Z. (2022). “Application of environmentally friendly materials in the design of children’s furniture based on fuzzy technology,” Mathematical Problems in Engineering 2022, article 5165699. DOI: 10.1155/2022/5165699
Xu, J., and Zhang, H. (2012). “Modern furniture color image based on eye tracking,” Applied Mechanics and Materials 157–158, 410-414. DOI: 10.4028/www.scientific.net/amm.157-158.410
Yang, Y., Tian, H., and Jiao, S. (2019). “Product design evaluation method using consensus measurement, network analysis, and AHP,” Mathematical Problems in Engineering 2019, article 4042024. DOI: 10.1155/2019/4042024
Zhagn, Z., and Xu, B. (2020). “Tang dynasty chair feature design based on kansei evaluation and eye tracking system,” Wood Research 65(1), 161-174. DOI: 10.37763/wr.1336-4561/65.1.161174
Zhang, W., and Li, S. (2022). “The integrated development of furniture design and children’s characteristics based on artificial intelligence,” Scientific Programming 2022, article 6036160. DOI: 10.1155/2022/6036160
Zhou, Z., Wang, L., Ye, R., and Yue, H. (2023). “A humanistic-care factors application hierarchical design-model for intelligent elderly products,” Heliyon 9(3), article ID e13734. DOI: 10.1016/j.heliyon.2023.e13734
Zhu, N. (2019). Research on Guiding Strategies for Regional Activities in Kindergartens from the Perspective of Development Suitability Practice Theory, Guizhou Normal University, Guiyang, China. DOI: 10.27048/d.cnki.ggzsu.2019.000099
Article submitted: September 20, 2023; Peer review completed: October 28, 2023; Revised version received and accepted: November 12, 2023; Published: November 21, 2023.
DOI: 10.15376/biores.19.1.434-455