Search for design intelligence: A field study on the role of emotional intelligence in architectural design studios

Abstract

The design studio is the core of the architecture curriculum. Interpersonal interactions have a key role during the processes of design and critique. The influence of emotional intelligence (EQ) on interpersonal communication skills has been widely proven. This study examines the correlation between EQ and architectural design competence. To achieve this, 78 architecture students were selected via a simple random sampling method and tested using an EQ test questionnaire developed by Bradbury and Greaves (2006) . The scores of five architectural design studio courses (ADS-1, ADS-2, ADS-3, ADS-4, and ADS-5) were used as indicators of the progress in design of the students. Descriptive and inferential statistics methods were both employed to analyze the research data. The methods included correlation analysis, mean comparison t -test for independent samples, and single sample t -test. Findings showed no significant relationship between EQ and any of the indicators.

Keywords

Emotional intelligence (EQ) ; Architectural education ; Design competence ; Design studio

1. Introduction

Architecture education is a design studio-based curriculum (Schön, 1983 ). Many researchers have described the design studio as the center (Schön, 1985 ) and the heart (Kuhn, 2001  ;  Oh et al ., 2013 ) of design education. Design studio models focus on learning by doing. All processes of solving open-ended problems in the studio are accomplished in lecture and critique sessions. Schön (1983) defines the architectural studio as a context wherein an active process of learning occurs through individual or group problem-based projects. Challenges include recognizing a problem, understanding its constraints, and using creativity, reasoned judgment, interpersonal abilities, and “reflection-in-action” to solve the problem. All these factors are present in the foundation of the architecture curriculum.

Most of the works on design studio (Ochsner, 2000  ;  Demirbaş and Demirkan, 2003 ) claimed that interpersonal interactions, including those between tutor–students or students–students, have a key role in the design process. Previous studies have discussed various factors and skills, including oral communication, changing an implicit understanding to an explicit one (Morton and O’Brien, 2005 ), oral presentation (Greusel, 2002 ), design social aspects (Cross and Clayburn, 1995 ), decision making (Fallon et al., 2014 ), and reduction of conflicts between students (Ghiabi and Besharat, 2011 ). The importance of the following factors to professional design projects has also been discussed: negotiation (Chen et al., 2014 ); leadership (Lee and Cassidy, 2007 ); presenting designs to clients in a convincing manner (Dias et al ., 1999  ;  Cross, 2008 ); and the relationship between communication among team members and the quality of their final product (Busseri and Palmer, 2000 ).

Design critique is a crucial part of the design process in design studios and is related to tutor-to-student and student-to-student interaction and communication. In terms of the kinds of dialog that transpire among people in the critique process, architectural design studios (ADS) have developed their own pedagogies, including desk critique (i.e., individual critique held on the desks of students, involving just a tutor and a student), design reviews, and design juries. In all these activities, interpersonal skills have a key role (Çıkış and Çil, 2009 ). Collaborative or cooperative design has also attracted considerable research interest (Peng, 1994 ). For example, Lu et al. (2000) emphasized the role of interpersonal skills in solving frequently occurring conflicts among collaborators of design and proposed negotiations to solve such problems.

However, research on effective and predictive factors and tools that influence communication among designers, students, and tutors remains limited despite the reiterated importance of communication in design studios. Emotional intelligence (EQ) can serve as a tool to measure the communication rate among actors involved in the design process. A common feature in the numerous various definitions of EQ is that it includes a set of emotions, social knowledge, and abilities that guide and reinforce the overall capability of an individual in responding appropriately to environmental factors and pressures. EQ also fosters optimal performance in four areas, namely consciousness, social awareness, relationship management, and self-management (Goleman et al., 2002 ). EQ is generally responsible for optimizing communication with others, self-control, compliance, and motivation for living. It improves intrapersonal and interpersonal skills, adaptability, flexibility, stress management, and public mood management; it can also increase the performance levels of people in academic and professional fields (Damasio, 2008 ).

This study aims to contribute to the literature on the correlation between, on one hand, EQ and architectural design competence and, on the other hand, the academic achievement of university students. A literature review is presented in the first part of the article to study the theoretical bases of the issue and to arrive at a reasonable hypothesis. The theoretical bases include ADS, collaborative design, the social and interpersonal aspects of design, EQ, the skills required in design, and the role of critique. After the hypotheses are developed, statistical analyses are performed with the software SPSS^® in the second part of the article. The research variables include architectural design skills, academic achievement, and EQ score. The correlation among the variables is tested, and simple random sampling is used to select the sample population.

2. Theoretical foundations

2.1. ADS

Design studio-based courses are central to architectural education programs in most universities. Relative to other courses, studio-based courses are equivalent to many academic hours every week of every semester. Other theoretical and technological courses have a supporting role in the design studio. The main goal of the design studio is to prepare architectural students to deal with open-ended questions and find creative and innovative solutions to these questions (Ibrahim and Utaberta, 2012 ).

ADS is a revised American version of the atelier training system in the École des Beaux-Arts in the 19th century Paris (Kuhn, 2001 ). Kuhn (2001) noted that the main characteristics of ADS include (a) finding solutions to open-ended and complex problems related to a project, (b) incredibly fast iterations in achieved solutions during the design stage, (c) frequency of informal and formal critiques, (d) heterogeneity of issues handled and the use of previous ideas and holistic thinking, and (e) the use of constraints in a creative manner. By presenting their works in the design studio, students receive comments and feedback from their tutors and other students, and they can revise their work step by step. This process is called “critique” (Oh et al., 2013 ).

The pedagogy of the design studio system has a long history (Boyer and Mitgang, 1996 ) and can function as model for other disciplines (Boyer and Mitgang, 1996 ). Schön (1983) noted that the studio-based system can be generalized and applied in the professional education of other disciplines. A model that employs the design studio was proposed by Shaffer (1997) in the field of mathematics.

Schön (1985) emphasized that the learning process in the design studio begin with encountering open-ended problems. He also believed that learning is developed through a “reflection-in-action” process. The design studio should function as both a learning space and a complex social organization (Deasy and Laswell, 1985 ) in which design students can communicate with one another and receive effective comments from their tutors (Kvan and Jia, 2005 ). Kvan and Jia (2005) pointed out that a wide range of learning methods can be employed in the design studio if the program begins with open-ended problems and consists of a collection of communication media.

According to Demirbaş and Demirkan (2003) , aside from beginning with open-ended problems, design studios should also be centered around the contents and relations of design education at a sociological level and its relations with other realms at an epistemological level. They also noted that the critique procedure in the design studio is not lecturer-based but an effective social communication among students and between tutor and students. Wendler and Rogers (1995) similarly contended that the substantial part of ADS is the verbal communication among its participants (i.e., student to teacher and student to student). Therefore, interaction and communication are key concepts in the design studio.

2.2. Interpersonal aspects of assessment/critique in design

Assessment and evaluation are major components of education and learning processes. Evaluation has an essential effect on learning processes. A proper assessment system can improve learning processes (Crooks et al., 1996 ). It helps tutors identify the learning abilities and levels of achievement of design students and determine which step to take next in the training process. It also helps students determine where they are in their progress and gives them an opportunity to modify their designs. Consequently, students acquire professional experience in designing. Appropriately designing and implementing evaluations and assessments can improve the learning of students in the design studio (Utaberta et al., 2011 ). Other important aspects of critique in ADS include which types of critique are specifically effective for a particular stage of the design and when is the proper time to critique to maximize control over the design process (Utaberta et al., 2010 ).

Assessment is a general term that refers to measuring the progress of students. For both tutors and students, “grades” in all types and formats are final tools for assessing and acknowledging the performance of the latter. Well-designed assessment methods provide students with good opportunities for self-monitoring and receiving feedback. Assessment has an indirect but essential influence on the quality of learning (James et al., 2002 ). Assessment allows students to identify their weaknesses and strengths, as well as the steps they must take to improve their practical and conceptualization abilities and their capacity to clarify and recognize their final intentions (Hickman, 2007 ).

Through a review of studies on critique processes in design studios, Oh et al. (2013) identified 11 main factors related to design critique. They categorized these factors into five methods and six conditions. The five methods are (1) critique settings, (2) tutor–student relationship, (3) communication modalities, (4) delivery types, and (5) delivery. The six conditions are (1) design phases, (2) individual differences, (3) knowledge/experiences, (4) student response types, (5) design artifacts, and (6) learning goals. Three of the five methods are related to communication and interpersonal relations, and three of the six conditions to EQ concepts.

2.3. Architectural collaborative design

If a product or service is created through the collective or shared attempts of several designers, then the design process is called “collective design”. Concurrent design, cooperative design, and interdisciplinary design are other commonly used terms for this kind of design (Wang et al., 2002 ). Similar to other human activities, designing and building require different professionals to work together as a team. Peng (1994) argued that two major reasons may explain why collaborative design has attracted considerable research interest. The first reason is that technical requirements render the gathering of different professions necessary in designing a building as in any other complex artifact. The second reason is related to critique and the main objective of the design process in relation to the final product. The fundamental common concern of team members is the improvement of proposed solutions. The diversity of teams increases the difference in points of view, thereby enhancing the critique process.

Sudweeks and Allbritton (1996) noted that participants in collaborative design do not really take part “collaboratively” or “equally”. From experience, anyone knows that people merely doing a particular work together or discussing a common subject does not immediately translate to a collaborative activity. According to Kvan (2000) , a successful collaborative activity is achieved only when a particular work that cannot be completed individually is fulfilled by a team.

Architects generally express their concepts and thoughts through their designs. Their concepts and ideas should be considered by their clients, peers, and the public (Sasada, 1995 ). On one hand, architectural collaborative design frequently occurs when architects communicate their ideas and concepts to their colleagues via verbal, typed, or graphical presentations. In the past, architects collaborated with their peers mainly through face-to-face (FTF) communication. When designers are in separate offices, their communication is affected by space, and sometimes, even by time. In such cases, interaction is typically accomplished through fax, telephone, and computer-mediated collaborative design (CMCD) (Gabriel and Maher, 2002 ). “Computer-supported cooperative work”, “virtual design studios”, and “collaborative design studios” are kinds of collaborative architectural design (Lan, 2004 ). Gabriel and Maher (2002) claimed that similar to FTF communication, the CMCD method is conducive to impulsive, constant, and recurrent discussions, including the iteration of verbal expressions.

On other the hand, collaborative design may also involve the participation of stakeholders with diverse motivations, experiences, background, and situations. In this case, the actions of a design team are affected by technical and professional decisions as well as by social communication and interactions with other groups. Conflicts constantly occur because of different dependencies. Collaborative design is not simply a “technical decision-making process” performed by an expert team. It can be viewed instead as a “socio-technical interaction process” among all stakeholders (Lu et al., 2000 ). An efficient and helpful method for rectifying existing conflicts among designers and among different disciplines is to implement negotiation processes to reach an agreement. Through negotiations, stakeholders and experts can arrive at a compromise or a solution (Chen et al., 2014 ).

2.4. Social and interpersonal aspects of design

In some pedagogies of ADS, only content-oriented evaluations are prioritized, and oral interaction skills are given less attention. However, in the majority of professional activities, architects must develop proficiency in both design and communication, if they are to effectively present their designs to their audiences (Morton and O’Brien, 2005 ). Oral presentation skills are central to ADS and to understanding “how to think and talk like an architect”. According to the president of the American Institute of Architects, presentation skills are among “the most important issues facing modern architecture practice” (Greusel, 2002 ). The premise of this idea is that assuming that the design will present itself is a mistake.

Architects must remain competitive in the market to acquire clients, and a substantial part of this practice consists of describing a particular design in a confident, assured, and convincing manner (Dias et al., 1999 ). Busseri and Palmer (2000) argued that a link exists between the efficiency of communication and interactions among design team members and the quality of the designs they produce. Schön (1985) observed that design studio tutors tend to “mystify their artistry”, i.e., to theorize the knowledge and proficiency of a design as something “either one has or has not”. He also argued that design studio practice must change implicit understanding into explicit understanding. Considering the significance of oral communication in the design process, finding that communication skills have a secondary role in architectural design practices in some organizations is surprising (Morton and O’Brien, 2005 ).

Social interaction is a key concept among design teams and leaders. Observing team activities during the design process, Cross and Clayburn (1995) concluded that the social aspects of design considerably interacts with its cognitive and technical processes. Thus, design methodology must identify the process of design as the union of three aspects, i.e., “as a technical, cognitive, and social process”. Lee and Cassidy (2007) discussed the key traits of “good design leaders”, which may enhance creativity among team members. They concluded that good design leaders influence designers through encouragement, facilitation, communication motivation, providing information, and giving chances for personal development.

2.5. Relationship between EQ and the skills required in the design studio

Factors, such as oral communication, decision making, teamwork, negotiation, leadership, interpersonal skills, and critique, are crucial in the design studio and design processes. This section elaborates how EQ is related to these factors. The links among the factors in the study of Ghiabi and Besharat (2011) show that as EQ scores rise, the rate of interpersonal problems among students is reduced. According to Fallon et al. (2014) , high EQ may lead to efficient decision making in stressful situations.

EQ is connected to the ability of an individual to preserve self-control, self-encouragement, enthusiasm, and persistence. It is defined by the following four dimensions: (1) knowing and managing one׳s own emotions; (2) motivating oneself; (3) managing one׳s relationships; and (4) knowing the emotions of others. EQ skills can be acquired and learned (Goleman, 2006 ).

Der Foo et al. (2004) investigated the relationship between negotiation skills and EQ and how high EQ may influence negotiation results. They argued that individuals with high EQ can benefit more in a negotiation process than those with low EQ. Generating value is frequently a challenge in communication processes. Negotiating parties must recognize the interests of others to determine an area of mutual interest, and therefore, make a deal that is acceptable to all of them. Naquin and Paulson (2003) believed that high EQ helps individuals recognize the role of emotions in negotiation and can be used to determine whether partners are satisfied or whether the interests of both parties are met.

Improving emotional and interpersonal merits can result in considerable assurance. Effective leaders and many other productive people gain efficiency possibly because they are capable of improving these competencies over time (Riggio and Lee, 2007 ). Nordin (2012) argued a positive, albeit moderate, correlation between leadership and EQ. Based on organization results and achievements, the findings of Cavazotte et al. (2012) showed that leadership efficiency is the immediate consequence of the “transformational behaviors” of leaders and is typically an indirect consequence of individual differences (e.g., intelligence, experience, and conscientiousness). They also observed that neuroticism has a negative influence on the effectiveness of leaders.

Druskat and Wolff (2001) distinguished between the EQ of members and the EQ of the group. They claimed that group EQ is more important than individual EQ because of the level of interactions and awareness that a group deals with. A team is related to the emotions of its members, the emotions and moods of the group, and the emotions of other individuals and the members of other groups. Dealing with emotions within a group differs from dealing with those outside a group. Druskat and Wolff (2001) investigated the effectiveness of teams and found that the self-awareness of group emotions have strengths and weaknesses in terms of interactions. The modes of interaction, which are an important part of team EQ, enhance the efficiency of a group. Interactions occur through both self-assessment and by obtaining feedback from others. Figure 1 summarizes the main probable relations between the concept of the design studio and EQ.

Figure 1. Relations between the main concept of the design studio and EQ.

2.6. Architecture education in Iran

The architecture undergraduate program in Iran takes at least 4 years. The total number of course credits is 140, and they consist of general, basic, professional, and optional courses. Each of the ADS (ADS-1, ADS-2, ADS-3, ADS-4, and ADS-5) is equivalent to 5 credits (Science, 2007 ). Each design studio course begins with the basic study of climatic conditions, architectural standards associated with design, and site studies and analyses. Students present the primary concepts, and a lecturer helps correct their work. The final project at the end of a semester consists of a 3D model and dimensioned plans, including floor plans, site plans, four main elevations, and sections, using both internal and external perspectives. The course is evaluated by lecturers through projects presented at the end of the semester and works accomplished during the semester. The critique system in Iranian design studios generally progresses from individual desk critique in ADS-1 to peer and jury critique in ADS-5. Individual desk critique involves a tutor and a student and is typically held at the desk of the student (Oh et al., 2013 ). That is, the number of comments that students receive from classmates, and even those from invited tutors outside of the faculty, increases as the complexity of open-ended problems increase (i.e., from ADS-1 to ADS-5).

ADS courses start from the fourth semester, and students cannot pass more than one ADS course in one semester. The main content and scope of each ADS course program are as follows:

• ADS-1: Learning simple space functions, such as a fruit market or a small terminal.
• ADS-2: Learning residential design and conditioning factors in an urban context.
• ADS-3: Cultural, artistic, and conceptual designs with simplicity in functional systems, such as museums and cultural centers.
• ADS-4: Complex functional system designs, such as a small hospital or a small airport.
• ADS-5: Residential complex design.

3. Hypotheses

To study the correlation between EQ and design skills, the following hypotheses are tested:

H1.

A significant difference exists between male and female students with respect to EQ.

H2.

A significant relationship exists between the EQ and mean ADS scores of architecture students.

H3.

A significant relationship exists between the EQ and respective scores for ADS-1, ADS-2, ADS-3, ADS-4, and ADS-5 of architecture students.

H4.

A significant relationship exists between EQ factors (self-awareness, self-management, social awareness, and relationship management) and the respective scores for ADS-1, ADS-2, ADS-3, ADS-4, and ADS-5, as well as the mean ADS, of architecture students.

H5.

A significant relationship exists between EQ and the academic achievement of architecture students.

4. Research method

4.1. Sample

The statistical population used in this study included all the male and female undergraduate architecture students in the Deylaman Institute of Higher Education, Lahijan, Iran. The total number of students was 184 (official data). The statistical population was finite; as such, a finite population sampling formula was used to estimate sample size.

According to the formula, the sample size was estimated at 64 students. With regard to access to the student population and to increases in the confidence level, additional students (78) were selected. Simple random sampling was used given the sampling framework and the community members. Participation in the study was voluntary, and the subjects were not asked for personal information. The subjects were informed that any information they provide will be used only for research purposes and that the data will be analyzed collectively. Of the 78 participants, 48 (61.5%) were female and 30 (38.5%) male.

The mean age of the students in the sample was 23.51 years with a standard deviation of 2.08 years (i.e., the scattering parameter is related to age). The skewness coefficient was 1.39, which indicates that the community is a right-shifted skewed community, i.e., the age differed from the normal distribution and curved slightly to the right. The maximum frequency tended toward ages lower than the mean age. Over 92.3% of the sample was aged 27 years or younger.

4.2. Research tools

The EQ test used in this research was developed by Bradbury and Greaves (2006) . The test consists of 28 items, which are scored according to a 6-point Likert scale. It measures four components – self-a0wareness, self-management, social awareness, and relationship management – and calculates an overall EQ score. Scores above 80 indicate a high EQ, whereas those below 60 indicate a low EQ. The four components may be described as follows:

• Self-awareness: The ability to accurately recognize emotions as they happen and to understand regular self-practice to exhibit reaction to people in different situations.
• Self-management: The ability to control emotions, which enables a person to remain flexible and react positively and effectively in different situations.
• Social awareness: The ability to recognize and understand the emotions of others, whether individually or as a group. This awareness is important to control and manage relationships.
• Relationship management: The ability to use awareness of one׳s own and others׳ emotions for the constructive and positive management of interactions and relationships.

In the study of Ganji (2011) , the undergraduate and graduate students of the Roodehen and Saveh branches of Azad University (Iran) were tested to determine the reliability of the test. The validity coefficients obtained for the scores of the two applications of the test on two separate groups, those for the EQ factors (self-awareness, self-management, social awareness, and relationship management), and the total EQ scores were .73, .87, .78, .76, and .90, respectively. All the coefficients were significant at a level of .99. The sample included 36 individuals.

Another test was performed on 284 subjects (145 males and 139 females) and was run only once. The reliability coefficients for the male and female groups were obtained, and the Cronbach׳s alpha for whole sample was .88. All the questions had a positive and significant correlation with the entire test. If none of the questions was omitted, then the reliability of the entire test was significantly increased. All the coefficients obtained were significant at .99.

This test was validated by associating it with the Bar-on EQ test (Bar-On, 1997 ) and applying it to a group of 97 people. A correlation coefficient of .68 was obtained, which was significant at .99. Thus, the reliability and validity of the test were confirmed. In addition, based on the qualitative assessment of designs by different tutors and by transforming their assessment into scores, the scores for the ADS-1, ADS-2, ADS-3, ADS-4, and ADS-5 courses and their mean score were used as the indicator of the progress of students in these courses. The grade point averages (GPAs) of students were used to measure their academic achievement.

4.3. Statistical analysis method

Descriptive and inferential statistics methods were both used to analyze data. Descriptive statistical tools, such as frequency tables and graphs, were used to investigate demographic data. For the inferential analysis of data and testing of the research hypotheses, inferential statistical techniques, such as correlation analysis, mean comparison t -test for independent samples, and single sample t -test, were used. SPSS^® was used to perform these analyses.

4.4. Results and discussion

4.4.1. EQ status of the surveyed students

The single sample mean statistical test was used to study the EQ status of the surveyed students. A score below 60 indicates an EQ lower than the average (i.e., an inappropriate EQ). A score between 60 and 80 indicates a moderate EQ, and a score above 80 indicates a high and appropriate EQ. Table 1 shows the EQ results of the students in detail.

Table 1. Descriptive indicators for the EQ variables (N =78).

Title	Minimum	Maximum	Mean	Standard deviation
Self-awareness	70.0	96.0	82.76	5.79
Self-management	36.0	92.0	69.53	13.61
Social awareness	62.0	93.0	80.67	7.16
Relationship management	45.0	93.0	73.35	10.01
Emotional intelligence	63.0	88.0	76.63	6.35

The null hypothesis is defined as follows:

• H0: (null hypothesis): μ =60 and
• H1: (the hypothesis): μ ≠60.

The results are shown in Table 2 .

Table 2. Results of single-sample t -test.

	t	df	Sig. (2-tailed)	Mean difference	95% Confidence interval of the difference
					Lower	Upper
EQ	23.10	78	.00	16.63	15.20	18.06

Test value=60.

The p -value is zero and is smaller than the value of α =.05. Thus, with a 95% confidence level, the null hypothesis that indicates that the average EQ score of the surveyed students is 60 is not confirmed. Furthermore, two numbers obtained in the 95% confidence interval column exhibits a mean difference. The interval between the two numbers does not contain zero; thus, this factor confirms the rejection of the null hypothesis. The positive upper and lower limits of this distance also show that the average EQ score is higher than 60. Therefore, based on the average EQ score, scores less than 60 are considered inappropriate, those between 60 and 80 are moderate, and those higher than 80 are appropriate. The overall result can be expressed as follows.

Regarding EQ and given the population mean (76. 63), all the students have moderate to high EQ. The statistic t -value is 23.1, i.e., its value is higher than the probability value of Tα /2.78 and is within the crisis area of the test. That is, the mean difference from the score of 60 is significant, and the EQ scores are higher than this score on the average.

4.4.2. EQ and ADS scores for male and female students

In investigating the EQ and ADS scores of the male and female students, the following hypotheses were considered:

• A significant difference exists between the EQ of male and female students.
• A significant difference exists between the ADS scores of male and female students.

The relevant tests can be expressed as follows:

• Hypothesis testing:
• H0 (null hypothesis): μ 1=μ 2 and
• H1 (opposite hypothesis): μ 1≠μ 2.

Table 3  ;  Table 4 show the details related to the EQ and mean ADS scores of the two gender groups (48 females and 30 males). Descriptive statistics were calculated separately for each group.

Table 3. Descriptive indicators for the ADS scores (N =78).

Title	Minimum	Maximum	Mean	Standard deviation
ADS-1	9	19.50	15.53	2.00
ADS-2	10	19.80	15.32	2.35
ADS-3	10.50	19.50	16.62	1.95
ADS-4	12	20	16.76	1.54
ADS-5	8	19.50	16.19	1.99
Mean ADS	11.20	18.90	16.08	1.32
GPA	11	18.80	15.65	1.49

Table 4. Descriptive group indicators.

		Levene׳s test for equality of variances		t -Test for equality of means
		F	p -Value	t	df	p -Value (2-tailed)	Mean difference	Std. error difference	95% Confidence interval of the difference
									Lower	Upper
EQ	Equal variances assumed	3.60	.06	−.33	76	.74	−.49	1.48	−3.46	2.46
	Equal variances not assumed			−.36	74.20	.72	−.49	1.38	−3.25	2.26
Mean ADS	Equal variances assumed	5.90	.02	.39	76	.69	.12	.31	−.49	.73
	Equal variances not assumed			.35	42.67	.72	.12	.34	−.57	.81

The average EQ score of females was 76.44 and that of males was 76.93. The standard deviation column also shows that the spread of the distribution of the EQ scores of females is greater than that of males. The 95% confidence interval for the mean also shows that, if 100 other samples with the same conditions and from the same social group are selected, then the EQ average of the samples will fall between the two numbers calculated as the lower and upper limits in 95 cases. The mean ADS score of the female group was 16.13 and that of the male group was 16.01. The standard deviations obtained suggest that the spread of distribution of the mean ADS score is greater for the males than for the females.

Figure 2 shows the plots of the indicators examined in the male and female groups. The figure indicates that a number of changes in the descriptive statistics, including the lowest and highest data, median, and quartile range, are associated with the distribution and scattering of data related to the EQ and mean ADS scores. The box diagrams clearly show that the average EQ and mean ADS scores are approximately equal for each group. Moreover, the data distribution of the EQ scores is higher in the female group than in the male group, whereas the data distribution of the mean ADS scores is higher in the male group than in the female group.

Figure 2. Box diagrams of EQ scores of male and female students.

The t -test mean comparison of independent samples was used to examine the relationship between gender and EQ and mean ADS. First, the variance equality test was performed on the distribution of scores of the female and male groups by using the F statistic and the p -value. Based on this test, the hypothesis of equality of averages between the two gender groups is accepted or rejected.

In the EQ test, p =.06>.05; thus, the null hypothesis is not rejected, and the equality of variances is confirmed. In the next step, assuming the equality of variances, the test related to the equality of mean EQ scores was performed in both groups. Assuming the equality of variances, p =.74>.05; thus, the null hypothesis is not rejected. These results lead to the conclusion that the difference between the male and female groups in terms of EQ is insignificant. That is, the average EQ percentage of the male and female students is the same at the 95% confidence level. Hence, the equality of variances hypothesis is also rejected for the mean ADS score (p =.02<.05). Thus, following the same premise at a 95% confidence level, the equality of means hypothesis is not rejected, and the difference is deemed insignificant (p =.72>.05).

This result confirms the findings of other studies on the role of gender in design studios. Researchers have argued that design studios must achieve a level of diversity in terms of gender, race, cultural background, and ideologies. Males and females equally take part in design studio activities (Koch, 2002 ).

4.4.3. Relationship between the EQ and ADS scores of the surveyed students

Given the research topic and objectives, the main research hypotheses are as follows:

• A significant relationship exists between the EQ and mean ADS scores of the surveyed students.
• A significant relationship exists between the EQ and the ADS-1, ADS-2, ADS-3, ADS-4, and ADS-5 scores of the surveyed students.

Correlation analysis can be used to examine the defined hypotheses as follows.

The Pearson correlation coefficient was used to test the hypotheses in this section. First, the hypothesis can be examined intuitively. Given that the p -value of the correlation between EQ and mean ADS scores is greater than .05 (p =.625), the null hypothesis is not rejected and the correlation is insignificant at a 95% level of confidence. Moreover, the p -value of the correlation between EQ and ADS-1, ADS-2, ADS-3, ADS-4, and ADS-5 scores are respectively .66, .66, .78, .55, and .13, all of which are bigger than .05, and thus, the correlations are insignificant.

Figure 3 shows a scatterplot of the EQ and mean ADS scores, which indicates an insignificant relation between the two variables.

Figure 3. Scatterplot of the EQ and mean ADS scores.

However, based on this plot, the absence or presence of a relationship between two variables cannot be explicitly concluded but can only be inferred from the correlation test and by calculating the correlation coefficient, which is discussed in the next section. Table 5 shows the results of testing the correlation between the EQ and ADS scores.

Table 5. Results of testing the main research hypothesis.

Hypothesis	Pearson correlation	p -Value (2-tailed)	Correlation
Relationship between mean ADS and EQ	.05	.63	No correlation
Relationship between ADS-1 and EQ	.05	.66	No correlation
Relationship between ADS-2 and EQ	−.05	.66	No correlation
Relationship between ADS-3 and EQ	−.03	.78	No correlation
Relationship between ADS-4 and EQ	.06	.55	No correlation
Relationship between ADS-5 and EQ	.17	.13	No correlation

Given that the sig of the correlation between the EQ and mean ADS scores is greater than .05 (p =.63), the null hypothesis is not rejected, and the correlation is insignificant at a 95% level of confidence. Moreover, the sig of the correlations between the EQ and ADS-1, ADS-2, ADS-3, ADS-4, and ADS-5 scores are respectively .66, .66, .78, .55, and .13, all of which are bigger than .05, which implies that the correlations are insignificant.

For a more detailed testing, the correlation between the four indicators of EQ – self-awareness, self-management, social awareness, and relationship management – and ADS scores management were measured. Table 6 presents the correlations among the p -values, Pearson correlation coefficients, and EQ quad indicators.

Table 6. Correlation between the quad factors of the EQ and ADS scores.

		ADS-1	ADS-2	ADS-3	ADS-4	ADS-5	Mean ADS
EQ	Pearson correlation	.05	−.05	−.03	.06	.17	.05
	p -Value (2-tailed)	.66	.66	.78	.55	.13	.62
Self-awareness	Pearson correlation	−.12	−.04	−.01	−.11	.05	−.06
	p -Value (2-tailed)	.29	.71	.90	.30	.63	.56
Self-management	Pearson correlation	−.07	.02	−.04	.19	.26*	.10
	p -Value (2-tailed)	.52	.84	.71	.08	.01	.38
Social awareness	Pearson correlation	.09	−.13	−.06	−.14	.10	−.04
	p -Value (2-tailed)	.41	.23	.59	.21	.38	.71
Relationship management	Pearson correlation	.21	−.05	.01	.07	−.03	.05
	p -Value (2-tailed)	.06	.62	.92	.53	.77	.64

Aside from the correlation between self-management and ADS-5, which has a positive correlation (p =.01<.05), no other significant relationship was observed between the EQ factors and the ADS scores.

4.4.4. Relationship between the EQ score and academic achievement of the surveyed students

The scatterplot relevant to the hypotheses shows no specific trend. Given that p =.86>.05, the null hypothesis is rejected, and the relationship between EQ and academic achievement is deemed insignificant. Moreover, given that all the p -values of the EQ four factors are greater than .05, no significant relationship exists between the academic achievement of the students and the four EQ factors (Table 7 ).

Table 7. Correlation coefficients between academic achievement and EQ scores and four EQ components.

		EQ	Self-awareness	Self-management	Social awareness	Relationship management
Academic achievement	Pearson correlation	.02	−.09	.01	−.04	.09
	p -Value (2-tailed)	.86	.42	.95	.68	.41

5. Discussion and conclusion

The design studio is the core of the architecture curriculum. Given the key role of interpersonal interactions in the design studio and in critique, we searched for and tested a predictive tool for these skills, and consequently, obtained improved results in the design studio. The following were our considerations in choosing EQ as the predictive tool: (a) the positive role of high EQ in skills, such as oral communication, negotiation, teamwork, and leadership; (b) the important role of these skills in the design studio; and (c) the universality of the EQ test.

The aim of this work was to study the relationship between, on one hand, EQ and architectural design skills and, on the other hand, the academic achievement of architecture students, including the role of gender.

Given the effective role of interpersonal relationships in the ADS category, the initial expectation of the survey was to confirm the presence of a significant relationship between EQ and the results of the different ADS scores (i.e., ADS-1–ADS-5 courses in this study). The five design courses differ in terms of design subject and the complexity of the open-ended design problem. The ADS scores used in this study were measured by qualitative assessment methods and critique. The composition of the evaluators and classmates also differed. This approach was expected to reveal the abilities in the design studio of the students. The results showed that all the students surveyed in this study have moderate to high EQ scores. Data scattering in EQ was exhibited by the female group more than by the male group. According to the independent t -test results on gender differences in the EQ and ADS scores, the differences between males and females are insignificant. This result is in agreement with the findings of studies on the role of gender in design studios ( Koch, 2002 ).

Testing the main hypothesis of the study for the presence or absence of a relationship between the EQ and mean ADS, ADS-1, ADS-2, ADS-3, ADS-4, and ADS-5 scores showed that no significant relationship exists between the former and the latter.

Measuring the correlation between each of the four factors of the EQ and ADS scores also led to no significant relation. The only correlation found was between the self-management factor and ADS-5.

Testing the research hypothesis for any relationship between EQ and academic achievement did not reveal any significant relationship.

The absence of any significant correlation between EQ and academic achievement confirmed the findings of a previous research (Woitaszewski and Aalsma, 2004 ). However, the absence of correlation between EQ and design studio results (i.e., with regard to the effect of EQ on interpersonal and social communication and the high EQ of the subjects in this study) may be debated. We formalize some inferences regarding the absence of such correlation.

• The tutors do not permit or manage good social communication during design studio sessions.
• The lack of space standards for design studios in our study reduces interactions among students (i.e., considering that the number of students per class is higher than the standards).
• The chosen critique method for each stage is inappropriate.
• The tutors do not interpret properly when they transform their qualitative assessment into scores.

Considering the slight correlation among ADS-1–ADS-5, and given the various critique methods, items (c) and (d) are more rational than the others. In the critique process, methods that employ high social interactions seem to work well with students with high EQ.

To gain further insights, the hypotheses of this research should be tested on design studio courses in different countries with various cultural backgrounds or on professional design teams or collaborative designs with different assessment methods.

A predictive test or a tool for predicting the necessary skills in the design studio must also be developed and derived from different tools.

Acknowledgement

This research is financially supported by the Portugal Calouste Gulbenkian Foundation (Grant number 129645) (Fundação Calouste Gulbenkian).

The authors most sincerely thank Mr. Dariuosh Poordadashi for his cooperation in the analysis of the statistical results and the staff of Deylaman Institute of Higher Education for their cooperation in preparing the research data.

References

1. Bar-On, 1997 Bar-On, R., 1997. EQ-i BarOn emotional quotient inventory: a measure of emotional intelligence: technical manual. Multi-Health Syst. Toronto, p. 216.
2. Boyer and Mitgang, 1996 Boyer, E.L., Mitgang, L.D., 1996. Building Community: A New Future for Architecture Education and Practice. A Special Report, ERIC.
3. Bradbury and Greaves, 2006 T. Bradbury, J. Greaves; Emotional Intelligence Test; Savalan Publication, Tehran (2006)
4. Busseri and Palmer, 2000 M.A. Busseri, J.M. Palmer; Improving teamwork: the effect of self-assessment on construction design teams; Des. Stud., 21 (3) (2000), pp. 223–238
5. Cavazotte et al., 2012 F. Cavazotte, V. Moreno, M. Hickmann; Effects of leader intelligence, personality and emotional intelligence on transformational leadership and managerial performance; Leadersh. Q., 23 (3) (2012), pp. 443–455
6. Chen et al., 2014 L. Chen, W. Wang, B. Huang; A negotiation methodology for multidisciplinary collaborative product design; Adv. Eng. Inform. (2014) http://dx.doi.org/10.1016/j.aei.2014.07.003
7. Çıkış and Çil, 2009 Ş. Çıkış, E. Çil; Problematization of assessment in the architectural design education: first year as a case study; Procedia – Soc. Behav. Sci., 1 (1) (2009), pp. 2103–2110
8. Crooks et al., 1996 T.J. Crooks, M.T. Kane, A.S. Cohen; Threats to the valid use of assessments; Assess. Educ., 3 (3) (1996), pp. 265–286
9. Cross, 2008 N. Cross; Engineering Design Methods: Strategies for Product Design; John Wiley & Sons, Chichester (2008)
10. Cross and Clayburn, 1995 N. Cross, A. Clayburn; Observations of teamwork and social processes in design; Des. Stud., 16 (2) (1995), pp. 143–170
11. Damasio, 2008 A. Damasio; Descartes׳ error: emotion, reason and the human brain; Vintage Digit (2008) Avon, New York, pp. 169–170
12. Deasy and Laswell, 1985 C.M. Deasy, T.E. Laswell; Designing Places for People: A Handbook on Human Behavior for Architects. Desginers, and Facility Managers; Whitney Library of Design, New York (1985)
13. Demirbaş and Demirkan, 2003 O. Demirbaş, H. Demirkan; Focus on architectural design process through learning styles; Des. Stud., 24 (5) (2003), pp. 437–456
14. Der Foo et al., 2004 M. Der Foo, H.A. Elfenbein, H.H. Tan, V.C. Aik; Emotional intelligence and negotiation: the tension between creating and claiming value; Int. J. Confl. Manag., 15 (4) (2004), pp. 411–429
15. Dias et al., 1999 P. Dias, A. Freedman, P. Medway, A. Par; Worlds Apart: Acting and Writing in Academic and Workplace Contexts; Routledge, New York (1999)
16. Druskat and Wolff, 2001 V.U. Druskat, S.B. Wolff; Building the emotional intelligence of groups; Harv. Bus. Rev., 79 (3) (2001), pp. 80–91
17. Fallon et al., 2014 C.K. Fallon, A.R. Panganiban, R. Wohleber, G. Matthews, A.M. Kustubayeva, R. Roberts; Emotional intelligence, cognitive ability and information search in tactical decision-making; Personal. Individ. Differ., 65 (2014), pp. 24–29
18. Gabriel and Maher, 2002 G.C. Gabriel, M.L. Maher; Coding and modelling communication in architectural collaborative design; Autom. Constr., 11 (2) (2002), pp. 199–211
19. Ganji, 2011 M. Ganji; Emotional Intelligence; Savalan, Tehran (2011)
20. Ghiabi and Besharat, 2011 B. Ghiabi, M.A. Besharat; Emotional intelligence, alexithymia, and interpersonal problems; Procedia – Soc. Behav. Sci., 30 (0) (2011), pp. 98–102
21. Goleman, 2006 D. Goleman; Emotional Intelligence; Random House LLC, New York (2006)
22. Goleman et al., 2002 D. Goleman, R. Boyatzis, A. McKee; Primal Leadership: Learning to Lead with Emotional Intelligence; Harvard Business School Press, Boston (2002)
23. Greusel, 2002 D. Greusel; Architect׳s Essentials of Presentation Skills; John Wiley & Sons, New York (2002)
24. Hickman, 2007 R. Hickman; Whippet-Fancying and Other Vices: Re-evaluating Assessment in Art and Design. The Problem of Assessment in Art and Design; Intellect, Chicago (2007), pp. 77–88
25. Ibrahim and Utaberta, 2012 N.L.N. Ibrahim, N. Utaberta; Learning in architecture design studio; Procedia – Soc. Behav. Sci., 60 (2012), pp. 30–35
26. James et al., 2002 R. James, C. McInnis, M. Devlin; Assessing Learning in Australian Universities: Ideas, Strategies and Resources for Quality in Student Assessment; Australian Universities Teaching Committee, Melbourne (2002)
27. Koch, 2002 A. Koch; The Redesign of Studio Culture: A Report of the AIAS Studio Culture Task Force; American Institute of Architecture Students (2002)
28. Kuhn, 2001 S. Kuhn; Learning from the architecture studio: implications for project-based pedagogy; Int. J. Eng. Educ., 17 (4–5) (2001), pp. 349–352
29. Kvan, 2000 T. Kvan; Collaborative design: what is it?; Autom. Constr., 9 (4) (2000), pp. 409–415
30. Kvan and Jia, 2005 T. Kvan, Y. Jia; Students׳ learning styles and their correlation with performance in architectural design studio; Des. Stud., 26 (1) (2005), pp. 19–34
31. Lan, 2004 Lan, J.-H., 2004. A Preliminary Study of Knowledge Management in Collaborative Architectural Design. CAADRIA2004, p. 47.
32. Lee and Cassidy, 2007 K.C.K. Lee, T. Cassidy; Principles of design leadership for industrial design teams in Taiwan; Des. Stud., 28 (4) (2007), pp. 437–462
33. Lu et al., 2000 S.-Y. Lu, J. Cai, W. Burkett, F. Udwadia; A methodology for collaborative design process and conflict analysis; CIRP Ann. – Manuf. Technol., 49 (1) (2000), pp. 69–73
34. Morton and O’Brien, 2005 J. Morton, D. O’Brien; Selling your design: oral communication pedagogy in design education; Commun. Educ., 54 (1) (2005), pp. 6–19
35. Naquin and Paulson, 2003 C.E. Naquin, G.D. Paulson; Online bargaining and interpersonal trust; J. Appl. Psychol., 88 (1) (2003), p. 113
36. Nordin, 2012 N. Nordin; Assessing emotional intelligence, leadership behaviour and organizational commitment in a higher learning institution; Procedia – Soc. Behav. Sci., 56 (0) (2012), pp. 643–651
37. Ochsner, 2000 J.K. Ochsner; Behind the mask: a psychoanalytic perspective on interaction in the design studio; J. Archit. Educ., 53 (4) (2000), pp. 194–206
38. Oh et al., 2013 Y. Oh, S. Ishizaki, M.D. Gross, E. Yi-Luen Do; A theoretical framework of design critiquing in architecture studios; Des. Stud., 34 (3) (2013), pp. 302–325
39. Peng, 1994 C. Peng; Exploring communication in collaborative design: co-operative architectural modelling; Des. Stud., 15 (1) (1994), pp. 19–44
40. Riggio and Lee, 2007 R.E. Riggio, J. Lee; Emotional and interpersonal competencies and leader development; Hum. Resour. Manag. Rev., 17 (4) (2007), pp. 418–426
41. Sasada, 1995 Sasada, T., 1995. Computer Graphics as a Communication Medium in the Design Process.
42. Schön, 1983 D.A. Schön; The Reflective Practitioner: How Professionals Think in Action; Basic Books, New York (1983)
43. Schön, 1985 D.A. Schön; The Design Studio: An Exploration of its Traditions and Potentials; Riba-Publ, London (1985)
44. Science, 2007 M.O. Science; General Characteristics Programme and Syllabus of Courses – B.Sc. of Architecture; Ministry of Science Research and Technology, Council of Planning for Higher Education, Tehran (2007)
45. Shaffer, 1997 D.W. Shaffer; Design, Collaboration, and Computation: The Design Studio as a Model for Computer Supported Collaboration in Mathematics; International Society of the Learning Sciences, Toronto (1997)
46. Sudweeks and Allbritton, 1996 Sudweeks, F., Allbritton, M., 1996. Working Together Apart: Communication and Collaboration in a Networked Group.
47. Utaberta et al., 2011 N. Utaberta, B. Hassanpour, N. Abdullah, M. Tahir, A. Che Ani; Developing sustainable architecture education approaches in Malaysia: a case study of critiques session in 2nd Year Design Studio of Architecture Department, National University of Malaysia (UKM); Appl. Mech. Mater., 71 (2011), pp. 221–227
48. Utaberta et al., 2010 N. Utaberta, B. Hassanpour, I. Usman; Redefining critique methods as an assessment tools in architecture design studio; WSEAS Trans. Adv. Educ., 359 (2010), pp. 359–364
49. Wang et al., 2002 L. Wang, W. Shen, H. Xie, J. Neelamkavil, A. Pardasani; Collaborative conceptual design – state of the art and future trends; Comput.-Aided Des., 34 (13) (2002), pp. 981–996
50. Wendler and Rogers, 1995 W.V. Wendler, J.S. Rogers; The design life space: verbal communication in the architectural design studio; J. Archit. Plan. Res., 12 (4) (1995), pp. 319–336
51. Woitaszewski and Aalsma, 2004 S.A. Woitaszewski, M.C. Aalsma; The contribution of emotional intelligence to the social and academic success of gifted adolescents as measured by the multifactor emotional intelligence scale‐adolescent version; Roeper Rev., 27 (1) (2004), pp. 25–30