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Preliminary research on the effect of spatial layout on peer academic support relationships in first-year university students: a case study of the school of architecture at SCUT Preliminary research on the effect of spatial layout on peer academic support relationships in...
Ji, Mian; Liu, Yubo; Deng, Qiaoming; Zhang, Yuhao; Zhao, Si
2023-03-03 00:00:00
JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING https://doi.org/10.1080/13467581.2023.2182636 ARCHITECTURAL PLANNING AND DESIGN Preliminary research on the effect of spatial layout on peer academic support relationships in first-year university students: a case study of the school of architecture at SCUT a,b a,b a,b a,b a,b Mian Ji , Yubo Liu , Qiaoming Deng , Yuhao Zhang and Si Zhao a b School of Architecture, South China University of Technology, Guangzhou, China; State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou, China ABSTRACT ARTICLE HISTORY Received 26 July 2022 Contemporary university curricula increasingly encourage students to develop peer academic Accepted 16 February 2023 support relationships, especially first-year students who are facing the transition from teacher- oriented to self-directed learning. Previous studies have identified spatial layout as a factor KEYWORDS impacting peer relationships by comparing the effects of different spatial layout categories. Learning space; spatial More insights into the quantitative correlation between spatial layout and peer academic layout; peer academic support relationships will meaningfully complement the existing findings. This paper therefore support relationships; spatial uses spatial configuration and spatial proximity as quantitative measures and adopts a quasi- configuration; spatial proximity experimental approach to investigate the effects of spatial layout on peer academic support relationships in first-year university students. Data were collected from a sample of first-year students enrolled in the School of Architecture at South China University of Technology. We use longitudinal data to measure and compare the peer academic support relationships as well as the corresponding spatial layout. The results support the importance of the spatial layout of the learning space for the development of peer academic support relationships. In addition, suggestions for university administrators and architects are proposed. 1. Introduction 2011; Montello 1988), which relate to the comfort Contemporary university curricula increasingly encou- dimension. Sociopsychological factors (López-Chao rage students to develop peer academic support rela- et al., 2021) and spatial layout typologies (Park et al., tionships (Brouwer et al. 2018; Celant 2013), especially 2014; Shernoff et al. 2017) are discussed as well, reveal- first-year students who are facing the transition from ing the social dimension of learning space. Existing teacher-oriented to self-directed learning (Chow et al., studies on spatial layout have proven the effect on 2008; Brouwer et al. 2016). Building peer relationships social relationships by comparing different categories is particularly challenging but significant for first-year of learning space. More insights into the effect of students. First, since most students move to university spatial layout on peer relationships derived by quanti- cities that are physically distant from their families, tative methods are meaningful for complementing the classmates who live and study together may become research findings. the most important source of support to help them The quantitative correlation between spatial layout adjust to university life (Chow and Healey 2008; Buote and social relationships has been examined in the et al. 2007). Building peer relationships during the workplace (Peponis et al. 2007; Sailer et al., 2012; transition period contributes to first-year students’ Wineman et al. 2014). Spatial configuration as engagement and study success (Brouwer et al. 2016). a global measure and spatial proximity as a local mea- Second, the self-directed learning mode in a university sure show how spatial layout affects social interaction. depends on the peer academic support network more However, research on university learning space than other motivations for self-directed study. remains scarce. The difference in occupant behaviour Academic communication and collaboration play and spatial layout calls for specialized empirical studies a significant role in the learning process. on the spatial effect on university students. This paper Previous studies have identified several factors therefore sets out to explore how the spatial layout of affecting support networks. Studies linking learning the learning space affects peer academic support rela- space with learning process and outcomes mainly tionships in first-year university students. While there focus on indoor environment quality, such as ventila- are different dimensions of peer relationships (social tion, lighting, and acoustics (Castilla et al. 2018; and academic), we have focused on academic support Lewinski 2015; Marchand et al. 2014; Lansdale et al. relationships, as they are more attached to the learning CONTACT Qiaoming Deng dengqm@scut.edu.cn School of Architecture, South China University of Technology, Tianhe District, Guangzhou, China © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the Architectural Institute of Japan, Architectural Institute of Korea and Architectural Society of China. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 2 M. JI ET AL. space and directly benefit academic performance. In academic communication and collaboration with. applying spatial configuration and spatial proximity as Work-related communication and collaboration are quantitative measures of spatial layout, this paper necessary for a work process to culminate in success adopts a quasi-experimental approach to investigate (Hillier et al., 1991; Penn and Hillier 1992 &, 1999; Allen how spatial layout influences freshman peer academic et al., 2007; Wineman et al. 2014; Sailer et al., 2009). In support relationships on both the organizational scale previous studies, work-related relationships were eval- and personal scale. Data were collected from a sample uated and measured by face-to-face interaction fre- of first-year students enrolled in the school of architec- quency or network properties (Allen and Henn 2007; ture at South China University of Technology who Sailer and McCulloh 2012; Wineman, Kabo, and Davis experienced a change of classrooms for major courses. 2009). We use longitudinal data to measure and compare the effect of spatial layout on peer academic support rela- 2.2. Learning space in higher education tionships among the same group of students in two semesters. The results support the importance of spa- Learning space in higher education has been proven to tial layout for building peer academic support be an essential element for student academic out- relationships. comes. It has specific conditions related to teaching methodologies, including promoting academic inter- action in the classroom (López-Chao and López-Pena 2. Theoretical background and conceptual 2021). Existing studies linking learning space with model learning process and outcomes mainly focus on indoor environment quality, such as ventilation, lighting, 2.1. Peer academic support relationships acoustics, etc. (Castilla et al. 2018; Lewinski 2015; Identifying the factors that affect academic perfor- Marchand et al. 2014; Lansdale et al. 2011; Montello mance is an essential part of educational research. 1988). The findings often relate ventilation and lighting Previous studies have documented the importance of to the design of windows and light shelves (Kruger intelligence, motivation, personality traits, and perso- et al., 2004), while acoustics is related to the relative nal behaviours (e.g., class attendance) (Busato et al. position of recreation space and material choices 2000; Farsides et al., 2003; Kassarnig et al. 2018). (Zannin et al., 2009). Whether and how much these There is a growing interest in the influence of social factors affect comfort during learning processes are ties, which have been argued to be positively asso- the focus of research. There are also studies focusing ciated with academic performance (Brouwer et al. on the spatial effect on social interactions. López-Chao 2016; Tomás-Miquel, Expósito-Langa, and Nicolau- and López-Pena (2021) considered sociopsychological Juliá 2016; Eggens, Van der Werf, and Bosker 2008; factors in their study and identified satisfaction, func- Rizzuto, Ledoux, and Hatala 2009). In higher education, tionality, the possibilities of social interaction, and social ties consist of peer relationships, family relation- place attachment as part of the social dimension of ships, and faculty relationships (Brouwer et al. 2016). learning space. They argued that flexible spaces, which Peer relationships have been argued to be the most relate social relationships to the spatial layout typol- influential form of support since university students ogy, are more appropriate for interaction. There are stay together most frequently and provide psycholo- other similar studies linking the spatial layout of learn- gical support and exchange knowledge with each ing space with academic interaction. For instance, the other (Vignery et al., 2020). For first-year university educational effects of traditional classrooms and active students who are facing the transition from teacher- learning classrooms have been compared based on oriented to self-directed learning (Chow and Healey questionnaire surveys (Park and Choi 2014). The results 2008; Brouwer et al. 2016), peer relationships are parti- show that traditional classrooms may disadvantage cularly important for helping them adjust to university student learning experiences for those in certain seat life and obtain engagement (Tani, Gheith, and positions and that the gap in learning attitudes was Papaluca 2021; Thiele, Sauer, and Kauffeld 2018; offset in active learning classrooms. The better perfor- Brooman and Darwent 2014) and study success mance of active learning classrooms is due to their (Brouwer et al. 2016; Tomás-Miquel, Expósito-Langa, promotion of interactive and collaborative learning and Nicolau-Juliá 2016; Eggens, Van der Werf, and through spatial layout. The influence of students’ seat- Bosker 2008). Among various peer relationships, aca- ing location on student engagement, classroom demic support relationships that are related to knowl- experience, and academic performance is also exam- edge exchange may have a stronger influence on ined in the setting of a large university lecture hall academic performance than those more related to (Shernoff et al. 2017). Such studies prove the effect of friendship (Tomás-Miquel, Expósito-Langa, and spatial layout on academic relationships by comparing Nicolau-Juliá 2016). A student’s peer academic support different categories of learning space. Since network consists of those whom he or she has frequent a multimethod approach would be beneficial to JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 3 complement the research approaches already used, more insight into the quantitative correlation between spatial layout and peer academic support relationships could be meaningfully extracted from mixed method studies. 2.3. Effect of spatial layout on social relationships Spatial layout as a physical arrangement enables space to acquire its social logic through the probabilities of encounters by their frequency and type (Hillier et al., 1984). Existing research on the measurement of the spatial layout effect on performance has mainly focused on working spaces within office buildings. Office spatial layout is argued to be an enabling factor of social relations by affecting the way workers move and interact in the workplace (Sugiyama et al. 2021; Haapakangas et al. 2019; Wineman, Kabo, and Davis 2009). The measures studied in office spatial layouts include the classification of office type (closed- or open-plan office), the amount and location of shared space (Hua et al. 2011), spatial configuration (Hillier Figure 1. An illustration of different spatial configurations and Penn 1991; Penn et al., 1992 &, 1999; Allen and (Hillier 2007, 22). Henn 2007; Peponis et al. 2007; Wineman, Kabo, and Davis 2009 &, 2014; Sailer and McCulloh 2012 &, 2019; Deng, Liu, and Ji 2021), distances between coworkers, implies that a dyadic level of analysis is necessary. and visibility of coworkers (Sugiyama et al. 2021). The Previous studies have found that spatial proximity can correlations between these spatial measures and work- lower the barriers to encounters and dyadic communica- related communication or collaboration are examined tion, thus facilitating dyadic relationship formations to explain the effect of spatial layout. (Allen 1977; Allen and Fustfeld 1975; Allen and Henn Spatial configuration is a core concept of space syntax 2007; Festinger, Schachter, and Back 1950; Sailer and (Hillier and Hanson 1984), which is a sophisticated analy- McCulloh 2012; Wineman, Kabo, and Davis 2009; Kabo tic technique widely applied for measuring spatial layout. 2017). Except for physical distance, visibility is Spatial configuration describes how space is organized a measurement of spatial proximity as well. Existing stu- and reveals the overall topological relationships of each dies have proven the existence of stronger relationships space. Figure 1 is an illustration of different spatial con- between those who have interpersonal visibility and figurations. Different spatial configurations result in dif- face-to-face interaction in the workplace (Stryker, 2011; ferent levels of accessibility, which impacts actual Penn et al., 1992; Sailer and McCulloh 2012; Peponis et al. movement that may trigger encounters and social inter- 2007; Toker et al., 2008). actions. Integration and choice are representative syntac- The correlation between spatial layout and social rela- tic properties used to quantify spatial configuration in tionships has been examined in the workplace. In terms of to-movement and through-movement poten- Peponis’s (2007) study of a communication design firm, tials on a global scale. Although spatial configuration the social network properties (density and centrality) cannot reflect real movement precisely because of the were used to measure the effect of spatial layout. Sailer multiple factors involved in movement, to some extent it (2012) used exponential random graph models (ERGMs) reflects the spatial potential for movement according to as network probability models to analyse whether and accessibility. Previous empirical studies have proven how much spatial configuration contributed to social strong correlations between real movement and spatial network construction in four organizations. Wineman configuration (e.g., Serrato et al., 1999; Penn, Desyllas, (2014) argued that spatial layout structures patterns of and Vaughan 1999; Wineman, Kabo, and Davis 2009; circulation, proximity, awareness of others, and encoun- Liu et al. 2021), indicating the applicability and reliability ters in organizations, which are fundamental to social of spatial configuration as a global predictor in spatial networks. In her study of three organizations, the spatial layout studies. layout measures of proximity and movement choice and Spatial proximity is another important measure of the social network measures of betweenness and degree spatial layout, especially on the local scale, which mea- are examined to understand their influence on sures interpersonal distances. Kabo (2017) argues that innovation. the dyad is the most basic unit of social interaction and 4 M. JI ET AL. Figure 2. Conceptual model. Although the research framework of the quantita- relationships and spatial layout in terms of spatial tive correlation between spatial layout and social net- configuration and spatial proximity. To avoid other works has been developed in the workplace, existing impact factors on academic support relationships, the empirical studies remain scarce in learning spaces for experiment was designed as pre- and posttreatments university students. Unlike employees, modern univer- that compare peer academic support relationships sity students are supposed to take responsibility for within the same organization in two spatial layout their own learning process. Except for lecture time, settings. student use of classrooms is not constrained by time, First, the floor plans and seat allocation of the while employee attendance should conform with orga- selected classrooms were obtained as the original spa- nizational regulations. Moreover, there is no hierarch- tial layout data. An online survey was conducted to ical difference (such as a manager and subordinates in collect original data for peer academic support rela- offices), and students share the learning space more tionships, including attendance, academic communi- equally. Therefore, the occupant behaviour in learning cation relationships, academic collaboration spaces differs from workers in offices in terms of time relationships, and organizational proximity. and styles. The difference in occupant behaviour and Attendance enables the comparison of the frequency spatial layout calls for specialized empirical studies on of face-to-face academic communication across the the effect of spatial layout on university students. two semesters. The data of academic communication relationships, academic collaboration relationships, and organizational proximity are obtained at the dya- 2.4. Conceptual model and research scope dic dyad level to construct interpersonal relationship matrices. The organizational proximity was used as The scope of the present study aims to examine the relationships between the spatial layout of the learn- a reference to discuss spatial layout effects. Then, based on the original data, spatial configura - ing space and peer academic support relationships in tion and proximity as layout measures are calculated first-year university students. The conceptual model is by Depthmap (Turner 2006). Frequency, density, and summarized in Figure 2, which shows the measures of spatial layout and academic support network on per- centrality as network measures are calculated by UCINET (Bugatti et al., 2002). sonal and organizational scales. This study sets out to Data was analysed at the organizational scale and examine whether and how overall spatial configura - tion and dyadic spatial proximity (calculated by the personal scale. On the organizational scale, the pre- and postdifference of spatial configuration and aca- Depthmap) matter for personal and organizational demic support network properties were compared by academic support relationships (calculated by UCINET). The following two research questions are t test. On a personal scale, the correlations between spatial proximity matrices and academic support rela- derived from the theoretical background related to tionship matrices were examined by the QAP correla- the spatial layout effect on performance: 1) How does spatial configuration affect academic support net- tion and regression analysis with a randomization test. works in first-year university students? 2) How does dyadic spatial proximity affect personal academic sup- port relationships in first-year university students? 3.2. Sample The experiment was conducted at South China 3. Methods University of Technology (SCUT). Data were collected from five classes of first-year students enrolled in the 3.1. Research design and procedure school of architecture. They experienced a move at The research was conducted using an exploratory the beginning of the second semester, which quasi-experimental approach that sought to examine enables us to measure the effect of spatial layout in the relationship between peer academic support a pre- and postexperiment. The studied classrooms JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 5 Table 1. The composition of respondents. AD1 AD2 UP1 UP2 LA total First semester Enrolment 58 53 32 32 32 207 respondent 53 43 25 29 27 177 Second semester Enrolment 57 53 32 32 32 206 respondent 57 51 32 32 29 201 Respondents of both semesters 53 43 25 29 24 174 (91.4%) (81.1%) (78.1%) (90.6%) (75%) (84.1%) consist of personal desks and shared discussion 5 = daily, 4 = several times per week; 3 = weekly, tables. Because of the disciplinary requirement for 2 = monthly, 1 = less than monthly. manual drawing and modelling, each student owns a fixed desk and seat for work. The discussion tables ● Organizational proximity are used for class with teachers approximately twice a week. Except for class time, students can use these Organizational proximity is investigated as a reference classrooms at any time. Due to the flexibility and to demonstrate the spatial layout effect on academic autonomy of use, the occupant behaviour of the support relationships. The question lists all the names learning space is less affected by teaching methodol- of classmates in a complete list. The respondent makes ogy. Academic interaction in classrooms occurs more ranked choices (0 = no organizational relation, 1 = one spontaneously. Although the students belong to five or more organizational relations) according to his or classes with three overarching majors (two in archi- her relations with each classmate. The surveyed orga- tectural design: AD1, AD2; two in urban planning: nizational relations include roommates, former UP1, UP2; and one in landscape architecture: LA), acquaintances, members of the same student union, they have the same courses in the first year. In classmates in elective courses, members of the same other words, they had the same lecture time and major course groups, etc. The data collected from all learning tasks. In addition, most spatial characteris- the respondents will be combined into a symmetrical tics (e.g., building, interiors, furniture) are the same binary matrix. Only if both individuals choose “1” (one except for the spatial layout. Therefore, these five or more organizational relations) will the relationship classes are comparable regarding use purposes and between the dyad be recorded as “1”. spatial quality (except spatial layout). We conducted an online survey to collect peer aca- ● Academic communication relationships demic support network data across two waves: at the end of the first semester and the end of the second Similar to the organizational network questions, semester in the 2020–2021 academic year. In the first respondents rate the frequency of their face-to-face semester, out of 207 students, 177 respondents com- academic communication (taking place in classrooms; pleted the questionnaire (response rate of 85.5%). In 5 = daily, 4 = several times per week, 3 = weekly, the second semester, one international student 2 = monthly, 1 = less than monthly) with each class- dropped out due to the COVID-19 pandemic, and the mate whose name is listed. Only if both individuals response rate increased to 97.6%. To ensure the cred- choose greater than “4” (several times a week) will ibility of the pre- and postresearch, we use only the the dyad be recorded with the mean value of the two data from respondents of both semesters (response sides in a symmetrical matrix, representing the inter- rate of 84.1%) for analysis. The composition of respon- personal academic communication relationship. In the dents is represented in Table 1. We informed students analysis related to frequency, we use this matrix with about the study’s aims, procedure, and ethical aspects. multiple values. Regarding the social network analysis, The completion time was 10–15 min, and participation we transform the matrix into binary values. was voluntary. ● Academic collaboration relationships 3.3. Measures and tools As collaboration chances depend more on the task requirements for first-year university students, we sur- 3.3.1. Measures for peer academic support network vey whether they have opportunities for academic Attendance, organizational proximity, academic com- collaboration instead of the frequency of academic munication relationships, and academic collaboration collaboration. The obtained data are used for analysis relationships were obtained by an online survey. on a personal scale (to analyse who they collaborate ● with) but not on an organizational scale (to compare Attendance the change in frequency pre- and postexperience). The question of academic collaboration is set with the Attendance is measured on a five-point scale by options (0 = none, 1 = have once or more times). a questionnaire with all students in both semesters: 6 M. JI ET AL. Only if both sides choose “1” (once or more times) will configuration properties. Angular segment analysis the dyad be recorded as “1”. The data collected are is a powerful tool for measuring accessibility in combined into a binary matrix that represents the linear networks and thus predicting social activities interpersonal academic collaboration relationships (Al-Sayed 2014). This analysis is on the level of within the class. segments constructed according to the movement Based on the above data, social network analysis by space, considering their topological, metric, and UCINET 6 (version 6.212) is conducted to evaluate aca- angular connections. The distance between two demic support networks. The attributes that we segments is the least angular cost within the net- employ for network evaluation are as follows. work. The linear network in Figure 3 is an example of angular segment analysis of a classroom. Density Different colours represent different accessibility values, such as integration and choice, which are Density measures the number of directed relation- global attributes of the spatial network. Integration ships divided by the number of possible directed measures the accessibility (calculated by angular relationships (Wasserman et al., 1994), which is cost) of a segment as the destination from all used to evaluate the academic communication net- other segments within the network. Choice mea- work on an organizational scale. In this study, den- sures the probability of a segment on the shortest sity by groups is calculated as well. The results path (calculated by angular cost) between every help demonstrate the influence of separated two segments within the network. The specific classrooms. syntactic properties that we employ by angular segment analysis are as follows. Normalized degree centrality NAIN (R = n) Degree is simply the number of connections a student has. The normalized degree centrality is the existing Integration measures the to-movement potential of degrees divided by the maximum possible degrees a space, which is one of the most powerful syntactic expressed as a percentage (Borgatti, Everett, and properties in both analysis and movement prediction. Freeman 2002), which can reflect a student’s level of It shows how deep or shallow space is in relation to all popularity (Wasserman and Faust 1994) within the other spaces. Using integration, spaces are ranked network. from the most integrated to the most segregated. Integration is usually indicative of how accessible space is and is thought to correspond to rates of social 3.3.2. Measures of spatial layout encounter (Hillier 1996). In this study, we conduct The measures of spatial layout are calculated in angular segment analysis to calculate the integration Depthmap. We conduct angular segment analysis value of each segment within a class on a global scale on an organizational scale to calculate the spatial Figure 3. An example of angular segment analysis of a classroom. The linear network is constructed according to the movement space; the colour is according to the values of different measures. The above figure is an example of angular segment analysis coloured by NAIN (R = n). JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 7 (R = n). The results are normalized to permit compar- by angular segment analysis on a global scale (R = n). ison of different models. NAIN represents the normal- NACH represents the normalized choice. ized integration. We conduct step depth analysis on a personal scale to calculate the spatial proximity of dyads (metric step NACH: (R = n) shortest-path length, visual step depth). Step depth analysis is based on a graph of a plan, which is divided Choice measures the through-movement potential of into grids, in which each grid is a unit for analysis. The a space. Segments that record high global choice are spatial relationship of each unit can be calculated in located on the shortest paths from all origins to all terms of interaccessibility or intervisibility. In our study, destinations. Compared to integration, choice is descrip- the analysis grid is set as 250 mm*250 mm, which can tive of movement rather than occupation. In this study, locate each student’s seat precisely. Figure 4 shows an the choice of each segment within a class is calculated example of step depth analysis of a seat within Figure 4. An example of step depth analysis of a seat within a classroom. The upper figure represents the result of the metric distance (measured by metric step shortest-path length) from a seat (the highlighted yellow grid) to all other grids (including other seats). The lower figure represents the result of the visual distance (measured by number of turns) from a seat (the highlighted yellow grid) to all other grids (including other seats). Different colours represent different values, and the values of each grid can be exported for further statistical analysis. In angular segment analysis, different radii can be set to define the analysis area of each segment. “R = n” means that the analysis of each segment takes all the segments in the class into consideration. 8 M. JI ET AL. a classroom. The syntactic properties that we employ UP1 reduces the NAIN (R = n), while the seat location is in this analysis are as follows. adjusted directly along the main path that increases accessibility in the post plan of UP2. Dyad physical proximity: Metric step shortest- path length 4.1.2. Peer academic support relationships pre- and posttreatment The metric step shortest-path length measures physi- Table 3 shows the peer academic communication fre- cal proximity by the metric distance between every quency before and after the change in classrooms. two students’ seat locations. It is calculated by step Among the five classes, the average attendance in depth analysis in Depthmap. The results are presented the four of the classes changes. To avoid the influence as a symmetrical matrix with metric distance values. of attendance, we use the ratio of academic commu- nication frequency per session (academic communica- Dyad visual proximity: Visual step depth tion frequency divided by attendance) to evaluate the change in academic communication behaviour Visual step depth measures the visual proximity by between pre- and posttreatment. The results show topological distance between students’ seat locations that the values of AD2, UP1, and LA significantly in a dyad. It is calculated by step depth analysis in decreased, which corresponds with the NAIN(R = n) Depthmap. The results are presented as results. Although the trends of AD1 and UP2 (increase) a symmetrical matrix with topological distance values. correspond to the NAIN (R = n) (increase), the p values by t test do not show significance at the 0.05 level. The corresponding trends of the five classes demonstrate 4. Results the effect of spatial layout on peer academic commu- nication to some extent. 4.1. Organizational scale The density comparison of academic communica- 4.1.1. Spatial configuration of pre- and tion networks in pre- and posttreatment is con- posttreatment ducted in UCINET 6, which runs the bootstrap The spatial configuration properties of each class are paired sample t test. The results of density and compared between pre- and posttreatment (Table 2). degree centrality (Tables 4 and 5) are similar, with The results of NACH (R = n) did not show a significant a significant decrease in AD2, UP1, and LA. The cor- change, while the NAIN (R = N) of all five classes responding trend of the network following spatial showed a significant change: AD1 and UP2 increased change indicates the effect of spatial layout on peer while AD2, UP1, and LA decreased. The pre- and post- academic support networks within a class. In parti- spatial layout of each class are presented in Figures 5 cular, the more segregated the learning space, the and 6. Students of AD1 are divided into two classrooms worse the peer academic support network will be. in both semesters. The increase in NAIN (R = n) is due The spatial configuration of the learning space to the absence of a floor difference between the two affects the possibility of peer academic classrooms in the second semester. Students of AD2 communication. and LA were in a single classroom in the first semester, To further explore the effect of spatial layout by while they were divided into three and two classrooms separated classrooms of an organization, density by on the same floor in the second semester. This results groups is conducted. The results of four cases (AD1- in a significant decrease in NAIN (R = n). Although UP1 pre, AD1-post, AD2-post, La-post) report on the and UP2 did not experience separated classrooms, the obvious preference for choosing academic communi- NAIN (R = n) changed due to the layout of the furni- cation with peers in the same classroom. As Table 6 ture. The appearance of cul-de-sacs in the post plan of shows, density values within the same classrooms (in Table 2. Spatial layout properties of pre- and posttreatment (t test). Mean ± standard deviation Difference Pre Post Difference 95% CI t p AD1 NAIN(R = n) 0.51 ± 0.05 0.68 ± 0.13 −0.17 −0.233 ~ −0.107 −5.584 ↑ 0.000** NACH(R = n) 0.68 ± 0.37 0.61 ± 0.58 0.07 −0.220 ~ 0.369 0.521 ↓ 0.607 AD2 NAIN(R = n) 1.56 ± 0.53 0.97 ± 0.25 0.59 0.263 ~ 0.911 3.806 ↓ 0.001** NACH(R = n) 0.52 ± 0.73 0.57 ± 0.57 −0.05 −0.523 ~ 0.430 −0.201 ↑ 0.842 UP1 NAIN(R = n) 1.86 ± 0.20 1.63 ± 0.28 0.23 0.023 ~ 0.443 2.304 ↓ 0.031* NACH(R = n) 0.64 ± 0.39 0.65 ± 0.50 −0.01 −0.387 ~ 0.372 −0.041 ↑ 0.968 UP2 NAIN(R = n) 1.51 ± 0.36 2.14 ± 0.91 −0.63 −1.194 ~ −0.719 −2.427 ↑ 0.030* NACH(R = n) 0.71 ± 0.67 0.63 ± 0.83 0.08 −0.459 ~ 0.628 0.327 ↓ 0.748 LA NAIN(R = n) 1.88 ± 0.64 0.90 ± 0.20 0.98 0.507 ~ 1.443 4.638 ↓ 0.001** NACH(R = n) 0.45 ± 0.73 0.54 ± 0.64 −0.09 −0.650 ~ 0.476 −0.319 ↑ 0.752 * p < 0.05 ** p < 0.01 JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 9 Figure 5. Spatial layout of the five classes in the first semester (distributed on two floors within a building). Figure 6. Spatial layout of the five classes in the second semester (on one floor within a building). Table 3. Peer academic communication frequency of pre- and posttreatment (paired t test). Paired Mean ± standard deviation Paired difference Pre Post Paired difference 95% CI t p AD1 Academic communication frequency 0.31 ± 0.24 0.24 ± 0.20 0.07 −0.002 ~ 0.138 1.948 ↓ 0.057 Attendance 3.94 ± 0.41 2.94 ± 1.39 1.00 0.610 ~ 1.390 5.148 ↓ 0.000** Academic communication frequency per use 0.08 ± 0.06 0.10 ± 0.09 −0.02 −0.051 ~ 0.006 −1.565 ↑ 0.124 AD2 Academic communication frequency 0.55 ± 0.47 0.29 ± 0.26 0.26 0.140 ~ 0.384 4.344 ↓ 0.000** Attendance 3.77 ± 0.72 3.28 ± 1.10 0.49 0.106 ~ 0.870 2.579 ↓ 0.013* Academic communication frequency per use 0.15 ± 0.13 0.09 ± 0.08 0.06 0.020 ~ 0.098 3.034 ↓ 0.004** UP1 Academic communication frequency 0.87 ± 0.47 0.37 ± 0.26 0.50 0.287 ~ 0.706 4.893 ↓ 0.000** Attendance 3.80 ± 0.65 3.20 ± 0.96 0.60 0.067 ~ 1.133 2.324 ↓ 0.029* Academic communication frequency per use 0.22 ± 0.12 0.13 ± 0.12 0.08 0.025 ~ 0.145 2.904 ↓ 0.008** UP2 Academic communication frequency 0.55 ± 0.42 0.63 ± 0.35 −0.08 −0.256 ~ 0.101 −0.891 ↑ 0.380 Attendance 3.62 ± 0.78 3.62 ± 0.94 0.00 −0.269 ~ 0.269 0.000 = 1.000 Academic communication frequency per use 0.15 ± 0.11 0.19 ± 0.12 −0.04 −0.099 ~ 0.018 −1.405 ↑ 0.171 LA Academic communication frequency 1.31 ± 0.71 0.68 ± 0.30 0.62 0.364 ~ 0.882 4.973 ↓ 0.000** Attendance 4.08 ± 0.28 3.88 ± 0.54 0.21 −0.007 ~ 0.423 2.005 ↓ 0.057 Academic communication frequency per use 0.32 ± 0.17 0.17 ± 0.07 0.15 0.085 ~ 0.208 4.929 ↓ 0.000** 10 M. JI ET AL. Table 4. Academic communication network density pre- and posttreatment (bootstrap paired t test). Mean Std. Error Pre Post difference of difference t p AD1 0.0726 0.0566 0.0160 0.0136 1.3971 ↓ 0.1650 AD2 0.11285 0.0653 0.0631 0.0248 3.3528 ↓ 0.0020** UP1 0.2033 0.0900 0.1133 0.0348 3.5973 ↓ 0.0010** UP2 0.1305 0.1478 −0.0172 0.0323 −0.6423 ↑ 0.5201 LA 0.3043 0.1594 0.1449 0.0481 3.7396 ↓ 0.0008** * p < 0.05 ** p < 0.01. Table 5. Degree centrality of the academic communication network pre- and posttreatment (paired t test). Paired (Mean ± standard deviation) Paired difference Pre Post Paired difference 95% CI t p AD1 7.26 ± 5.50 5.66 ± 4.67 1.60 −0.069 ~ 3.262 1.923 ↓ 0.060 AD2 12.85 ± 10.90 6.53 ± 5.74 6.31 3.488 ~ 9.136 4.511 ↓ 0.000** UP1 20.33 ± 10.98 9.00 ± 6.44 11.33 6.380 ~ 16.286 4.723 ↓ 0.000** UP2 13.05 ± 9.93 14.78 ± 8.14 −1.72 −5.810 ~ 2.361 −0.864 ↑ 0.395 LA 30.43 ± 16.42 15.94 ± 6.98 14.49 8.448 ~ 20.538 4.960 ↓ 0.000** * p < 0.05 ** p < 0.01 Table 6. Organizational density by separated classrooms. AD1-Pre Classroom 1(N = 43) Classroom 2(N = 10) Classroom 1(N = 43) 0.071 0.026 Classroom 2(N = 10) 0.026 0.556 AD1-Post Classroom 1(N = 29) Classroom 2(N = 24) Classroom 1(N = 29) 0.113 0.014 Classroom 2(N = 24) 0.014 0.080 AD2-Post Classroom 1(N = 11) Classroom2(N = 14) Classroom 3(N = 18) Classroom 1(N = 11) 0.455 0.019 0.010 Classroom 2(N = 14) 0.019 0.088 0.004 Classroom 3(N = 18) 0.010 0.004 0.131 LA-Post Classroom 1(N = 12) Classroom 2(N = 12) Classroom 1(N = 12) 0.273 0.049 Classroom 2(N = 12) 0.049 0.288 grey blocks) are much higher than interclassroom den- a positive and statistically significant correlation with sity, even if they belong to the same class organization. academic communication relationships of which the Therefore, dividing first-year university students who correlation coefficients range from 0.130 to 0.345. By belong to a class into separate classrooms is not con- comparing the correlation coefficient of pre and post- ducive to building peer academic support treatment (Figure 7), it is found that the absolute values relationships. of physical proximity in four of five classes (AD1, AD2, UP2, LA) increase, but that of organizational proximity shows a decreasing trend. In contrast, the absolute 4.2. Personal scale values of physical proximity in UP1 decrease signifi - cantly and those of organizational proximity increase The analysis on a personal scale aims to test whether slightly. The opposite trend in physical proximity and dyadic spatial proximity affects peer academic support organizational proximity indicates that physical proxi- relationships. Organizational proximity is analysed as mity can affect peer academic communication relation- a reference. The analysis is conducted by the QAP ships by reducing the dependence on organizational correlation analysis in UCINET 6, which runs the corre- proximity to some extent, which is beneficial for the lation analysis of different relational matrices with diversity of peer academic support relationships. a randomization test. The results are summarized in Regarding academic collaboration, the results report Table 7. a negative and statistically significant correlation with physical proximity (correlation coefficients range from 4.2.1. Dyadic physical proximity −0.088 to −0.293) and a positive and statistically signifi - The results show that nine of the ten cases report cant correlation with organizational proximity (correla- a negative and statistically significant correlation tion coefficients range from 0.062 to 0.674). The between physical proximity and academic communica- correlation effects of organizational proximity in all five tion relationships of which the correlation coefficients classes are significantly higher than those of physical range from −0.494 to −0.131. The results of organiza- proximity in the first semester. However, the effect of tional proximity perform better. All ten cases show JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 11 Table 7. Results of QAP correlation. Spatial proximity Physical proximity Visual proximity Organizational Metric distance Topological distance proximity AD1 Pre Academic communication relationships −0.136(0.000) ** −0.121(0.000) ** 0.278(0.000) ** Academic collaboration relationships −0.137(0.001) ** −0.148(0.000) ** 0.415(0.000) ** Post Academic communication relationships −0.228(0.000) ** −0.207(0.000) ** 0.219(0.000) ** Academic collaboration relationships −0.106(0.000) ** −0.087(0.001) ** 0.285(0.000) ** AD2 Pre Academic communication relationships −0.034(0.220) −0.036(0.233) 0.345(0.000) ** Academic collaboration relationships −0.038(0.191) −0.064(0.072) 0.536(0.000) ** Post Academic communication relationships −0.282(0.000) ** −0.252(0.000) ** 0.130(0.000) ** Academic collaboration relationships −0.200(0.000) ** −0.209(0.000) ** 0.062(0.043) * UP1 Pre Academic communication relationships −0.494(0.000) ** −0.416(0.000) ** 0.286(0.001) ** Academic collaboration relationships −0.154(0.015) * −0.113(0.040) * 0.674(0.000) ** Post Academic communication relationships −0.153(0.004) * −0.073(0.147) 0.290(0.000) ** Academic collaboration relationships −0.109(0.046) * −0.048(0.248) 0.347(0.000) ** UP2 Pre Academic communication relationships −0.131(0.010) * −0.105(0.041) * 0.337(0.000) ** Academic collaboration relationships −0.146(0.004) * −0.103(0.045) * 0.536(0.000) ** Post Academic communication relationships −0.140(0.003) ** −0.100(0.040) * 0.224(0.000) ** Academic collaboration relationships −0.088 (0.039) * −0.058(0.144) 0.231(0.000) ** LA Pre Academic communication relationships −0.275(0.000) ** −0.254(0.000) ** 0.261(0.000) ** Academic collaboration relationships −0.194(0.003) ** −0.076(0.120) 0.661(0.000) ** Post Academic communication relationships −0.319(0.000) ** −0.268(0.000) ** 0.240(0.000) ** Academic collaboration relationships −0.293(0.000) ** −0.270(0.000) ** 0.207 (0.001) ** physical proximity organizational proximity 0.6 0.5 0.4 0.3 0.2 0.1 A D 1 - A D 1 - A D 2 - A D 2 - U P 1 - U P 1 - U P 2 - U P 2 - L A - L A - -0.1 P R E P O S T P R E P O S T P R E P O S T P R E P O S T P R E P O S T Figure 7. Correlation between pre- and posttreatment academic communication relationships and physical/organizational proximity. physical proximity organizational proximity 0.8 0.6 0.4 0.2 AD1- AD1- AD2- AD2- UP1- UP1- UP2- UP2- LA- LA- -0.2 PRE POST PRE POST PRE POST PRE POST PRE POST Figure 8. Correlation between pre- and posttreatment academic collaboration relationships and physical/organizational proximity. organizational proximity decreases dramatically after The comparison between academic support the move, and the absolute values become even lower relationships (communication and academic colla- boration) and physical or organizational proximity than physical proximity in two classes (AD2 and LA). The is further discussed. As Figures 9 and 10 show, students in these two classes are divided into separate academic communication has a stronger correla- classrooms, and the effect of physical proximity tion with physical proximity, while academic colla- increases (Figure 8). In other words, the distance boration depends more on organizational brought by separated classrooms segregates academic proximity. collaboration. In sum, physical proximity affects peer academic collaboration in first-year university students to some extent. However, their academic collaboration 4.2.2. Dyadic visual proximity depends more on organizational proximity. The effect of The results show that eight of the ten cases report physical proximity is especially obvious in the context of a negative and statistically significant correlation separated classrooms. between visual proximity and academic 12 M. JI ET AL. 0.6 0.4 0.2 AD1-PRE AD1-POST AD2-PRE AD2-POST UP1-PRE UP1-POST UP2-PRE UP2-POST LA-PRE LA-POST -0.2 academic communication relationships academic collaboration relationships Figure 9. Correlation between academic support relationships and physical proximity. 0.8 0.6 0.4 0.2 -0.2 AD1-PRE AD1-POST AD2-PRE AD2-POST UP1-PRE UP1-POST UP2-PRE UP2-POST LA-PRE LA-POST academic communication relationships academic collaboration relationships Figure 10. Correlation between academic support relationships and organizational proximity. physical proximity visual proximity 0.6 0.4 0.2 A D 1 - A D 1 - A D 2 - A D 2 - U P 1 - U P 1 - U P 2 - U P 2 - L A - L A - -0.2 P R E P O S T P R E P O S T P R E P O S T P R E P O S T P R E P O S T Figure 11. Comparing visual proximity with physical proximity on the correlation with academic communication. physical proximity visual proximity 0.4 0.2 A D 1 - A D 1 - A D 2 - A D 2 - U P 1 - U P 1 - U P 2 - U P 2 - L A - L A - P R E P O S T P R E P O S T P R E P O S T P R E P O S T P R E P O S T Figure 12. Comparing visual proximity with physical proximity on the correlation with academic collaboration. communication of which the correlation coeffi - organizational proximity is similar to the above cients range from −0.073 to −0.416. Regarding correlation analysis. academic collaboration, only six of the ten cases report a negative and statistically significant corre- 5. Discussion lation with visual proximity of which the correla- tion coefficients range from −0.087 to −0.270. The current study explored whether and how the spa- Visual proximity shows high similarity with physical tial layout of the learning space is relevant for building proximity but has poorer performance as a spatial peer academic support relationships in first-year uni- proximity predictor for academic support relation- versity students. Spatial configuration as a global mea- ships (Figures 11 and 12). sure and spatial proximity as a local measure have Based on the results of the QAP correlation been examined as predictive measures of the spatial analysis, we further conduct the QAP regression layout effect on social relationships in the workplace. analysis on the academic communication relation- Previous studies on learning space have proven the ships by metric distance and organizational proxi- effect of spatial layout on academic performance by mity (Table 8). Eight of the ten cases prove metric comparing traditional classrooms and active learning distance to be a significant independent variable classrooms, which detail different spatial layouts by of which the standard coefficient runs from typology. Since a multimethod approach would be −0.112363 to −0.465666. The comparison with beneficial to complement the research approaches JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 13 Table 8. Results of QAP regression. R2 Adj R2 p Independent Un-std Coefficient Std Coefficient p AD1 Pre 0.090 0.090 0.000 Intercept 0.069739 0.000000 Metric distance −0.000001 −0.112363 0.001 Organizational proximity 0.179546 0.268355 0.000 Post 0.098 0.097 0.000 Intercept 0.120733 0.000000 Metric distance −0.000009 −0.222933 0.000 Organizational proximity 0.116834 0.214524 0.000 AD2 Pre 0.120 0.119 0.000 Intercept 0.089838 0.000000 Metric distance −0.000002 −0.031078 0.236 Organizational proximity 0.284864 0.344301 0.000 Post 0.093 0.092 0.000 Intercept 0.152418 0.000000 Metric distance −0.000008 −0.275714 0.000 Organizational proximity 0.056662 0.114399 0.000 UP1 Pre 0.295 0.294 0.000 Intercept 0.496374 0.000000 Metric distance −0.000054 −0.465666 0.000 Organizational proximity 0.191424 0.229196 0.000 Post 0.102 0.101 0.000 Intercept 0.080211 0.000000 Metric distance −0.000012 −0.136710 0.005 Organizational proximity 0.161501 0.281537 0.000 UP2 Pre 0.120 0.119 0.000 Intercept 0.107774 0.000000 Metric distance −0.000009 −0.080819 0.054 Organizational proximity 0.230204 0.324228 0.000 Post 0.063 0.062 0.000 Intercept 0.142145 0.000000 Metric distance −0.000011 −0.116242 0.012 Organizational proximity 0.149671 0.210440 0.000 LA Pre 0.123 0.121 0.000 Intercept 0.452852 0.000000 Metric distance −0.000029 −0.236674 0.000 Organizational proximity 0.214170 0.220006 0.000 Post 0.169 0.168 0.000 Intercept 0.253371 Metric distance −0.000013 −0.335099 0.000 Organizational proximity 0.191584 0.260514 0.000 already used, this research focused on quantitative communication. The increasing coefficient of physical methods. This study therefore adopts spatial config - proximity and decreasing coefficient of organizational uration and spatial proximity as quantitative measures proximity of the pre- and posttreatment groups indi- to further explore the spatial layout effect on peer cate that physical proximity can affect peer relation- academic support relationships. An exploratory quasi- ships based on face-to-face academic communication experimental approach was conducted with a case and reduce the dependence on organizational proxi- using the learning spaces of the school of architecture mity, which is beneficial for the diversity of peer aca- at SCUT. The research is conducted by answering how demic support relationships. The findings suggest that spatial configuration affects peer academic support physical proximity also affects peer academic colla- networks on an organizational scale and how dyadic boration in first-year university students to some spatial proximity affects personal academic support extent. However, their academic collaboration relationships on a personal scale. depends more on organizational proximity. The effect The findings of the case study on an organizational of physical proximity on peer academic collaboration is scale show that the spatial configuration of the learn- especially obvious in the context of separated class- ing space affects peer academic communication rela- rooms. In other words, the distance brought by sepa- tionships in terms of frequency (a measure of how rated classrooms significantly segregates dyads who often communication happens) and density (a mea- might otherwise have engaged in academic collabora- sure of the number of communicating students). tion. Comparing the peer relationships in face-to-face A segmented spatial layout (lower NAIN) leads to academic communication and academic collaboration, lower frequencies of peer academic communication it is found that face-to-face academic communication and network density. Different floors, separated class- has a stronger correlation with physical proximity, rooms, and new furniture layouts are factors that affect while academic collaboration depends more on orga- spatial integration. In addition, the organization of nizational proximity. Therefore, to build a stronger space within the classroom to facilitate circulation peer academic support network, seat allocation should should reduce cul-de-sacs, and students’ seats have make use of the promoting effect of physical proximity a more positive effect when they are directly placed to bridge students without established organizational along paths of major movement, which increases spa- relationships. The effect of dyadic visual proximity is tial accessibility and thus improves communication examined as well. The results show high similarity with possibilities. physical proximity but have poorer performance when The results of the case study on the personal scale judged by statistical significance, indicating physical report a statistically significant correlation between proximity is a better spatial predictor for academic physical proximity and face-to-face academic support relationships. 14 M. JI ET AL. Allen, T., and G. Henn. 2007. The Organization and 6. Conclusion Architecture of Innovation. New York: Routledge. This research bridges disparate disciplines to explore Al-Sayed, K. 2014. Space Syntax Methodology. London: peer academic support relationships that are Bartlett School of Architecture, UCL. Borgatti, S. P., M. G. Everett, and L. C. Freeman. 2002. Ucinet 6 embedded in a specific spatial milieu. It complements for Windows: Software for Social Network Analysis. 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