The Analysing of the Emotional Intelligence Scores of the Special Education Teacher Candidates for the Predictor of Multiple Intelligences Areas

: This study aims to analyse the emotional intelligence scores of the special education teacher candidates for the predictor of multiple intelligences areas. This study was conducted through relational scanning model. 211 teacher candidates, 106 females and 105 males, participated in the study. Data were collected through Personal Information Form, Teele Multiple Intelligence Inventory and Schutte Emotional Intelligence Scale. Logistic regression analysis was used to find whether the gender and grade level variables, and optimism, utilization of emotions and the scores of emotion evaluation are statistically significant in defining predominant intelligence areas or not. SPSS 24.0 was used in the data analysis process. The results revealed that while the gender variable and optimism scores are significant variables predicting the determination of individuals in which predominant intelligence is both interpersonal and not, optimism and evaluation of emotions scores are significant variables predicting the determination of individuals whose predominant intelligence is both visual and not. However, the results suggested that demographic variables (gender and grade level) and emotional intelligence scores did not affect kinesthetic, musical, intrapersonal, logical and verbal intelligence areas which were found as the predominant intelligence areas of teachers. 413 The reliability and validity of the inventory were conducted by Teele in 1992-1993. The results of the study have done in a primary school revealed that the inventory is valid and the reliability of all the intelligence areas is significant at .01 level. In Turkey, reliability studies were conducted by Oklan-Elibol (2000), Gogebakan (2003), Terzioglu (2005), Ozdemir (2006); and validity study was done by Ozdemir


Introduction
The idea that intelligence is the main ability which tells people apart causes people to define intelligence differently. Therefore, there are different views about the effects of environment and heritage on the development of intelligence. According to the theoreticians who try to define intelligence in a traditional point of view, intelligence is a unidimensional, unchangeable and non-improvable structure (Kuzgun & Deryakulu, 2014). Howard Gardner who defends that the intelligence is such a multi-dimensional and complex structure which cannot be defined as a single one carried the definition of intelligence into a different dimension. Instead of this, intelligence is a problem-solving ability or an ability to produce a product which finds its value in its own culture (Baum, Viens & Slatin, 2005;Gardner, 2006;Hoerr, 2000). Howard Gardner who focuses on both biological and cultural aspects of intelligence stated that individuals cannot be seen as similar and the structure called intelligence is a series of mental skills or abilities (Murray & Moore, 2012).
Gardner stated that individuals have eight different intelligence areas. These are verbal intelligence which is the capability of using an effective language, logical-mathematical intelligence which is the ability to use number efficiently and to establish logical connections between events, spatial intelligence which puts the emphasis on the phenomenon such as place, time, color, shape etc., musical/rhythmic intelligence which is the capacity of perceiving, distinguishing and expressing musical forms, bodily/kinesthetic intelligence which is the ability to use his body to express his thoughts and emotions, intrapersonal intelligence which is the ability to know himself, interpersonal intelligence which is the capacity of understanding others' emotions and needs, and finally naturalist intelligence which is the ability of being sensitive to environment (Armstrong, 2003;Ayaydin, 2015;Fleetham, 2006;Saban, 2005). Although all individuals have these eight intelligence areas, their intelligence profiles which explain their strong and weak sides are never the same (Teele, 2000). However, it is impossible to label individuals as intelligent or successful with IQ tests which measure only one type of intelligence.
Possibility of other factors for the achievement of success has become a subject for discussion after Gardner changed the criteria for individuals' academic success. Being successful at business, family and social life is connected with emotional features such as motivation, empathy, and harmony. Only IQ is insufficient in determining whether the individual will be successful in different areas or not. For example, it is a mistake to suppose that people with a high IQ will be successful (Yuksel, 2006). Emotions and intelligence can unite to process complex information (Mayer, Dipaolo & Salovey, 1990) because most cognitive problems contain emotional information which must be processed and interpreted (Mayer & Salovey, 1993). Therefore, it is necessary to take a look at the emotional intelligence in a broad perspective.
Mayer and Salovey were the first ones to handle emotional intelligence theory (Goleman, 2000) and by building the model they developed on social intelligence they evaluate it as a subset of social intelligence Seal & Brown, 2010). They define emotional intelligence as the ability to monitor their own and others' emotions, distinguishing one from the other, and using this information to direct his emotions and behaviours (Mayer & Salovey, 1993). They examine the emotional intelligence model as a four-sub-dimensional structure. These are; the ability to identify emotions, the ability to use emotions to make the thinking process easier, the ability to understand emotions, and the ability to manage emotions (Aslan, 2009;Mayer & Salovey, 1997). Bar-On defines the emotional intelligence as a cross-section of social and emotional abilities which make someone understand himself and others, express himself, socialize with others and cope with his needs ( Bar-ON, 2006). Bar-ON's emotional intelligence model constitutes five sub-dimensions. These are; interpersonal abilities, adaptation ability, stress management, and psychological well-being ( Bar-ON, 2000;Koksal, 2007). According to Goleman (2012), emotional intelligence is the ability to take action, keep going in spite of the difficulties, delay gratification by controlling his drives, organizing his psychological well-being, not permit his anxieties to affect his thoughts, put himself in other people's place, and have hope. Goleman examined his emotional intelligence theory in five sub-dimensions. These are; self-awareness, self-regulation, empathy, social skills, and motivation (Mayer, Salovey & Caruso, 2004).
Qualifications of emotional intelligence can be discussed as social and personal abilities. While self-awareness and selfregulation are related to personal abilities, and empathy, social skills and motivation are related to social abilities (Pinarcik, Sali & Altindis, 2016). Although Goleman's emotional intelligence is associated with Gardner's intrapersonal and social intelligence areas, it involves many abilities (Bay & Lim, 2006). Individuals with high emotional intelligence have the ability to solve problems, develop empathy, understand other people's feelings, be happy, and manage stress (Izard, 2001). They do not allow negative feelings and thoughts to happen. According to Balci (2001), those individuals are the ones who aware of their feelings and thoughts, strengths and weaknesses, who avoid momentary success and pleasures, who maintain their optimism in the event of failure, and who are successful at resolving conflicts among people. Therefore, individuals whose emotional intelligence is dominant could form the necessary mental habits for improving their performances and efficiencies. Yesilyaprak (2001) states that if emotions are recognized and directed in a constructive way, they could improve mental performance. In other words, improving emotional intelligence could contribute to effective use of cognitive intelligence capacity (Lam & Kirby, 2002;Tufan, 2011). Therefore, emotional intelligence related to the mental abilities that individuals have and not placing emphasis on the improvement of emotional intelligence will cause the academic failure of the individuals. Emotional intelligence allows individuals to be successful in all areas and to be creative. Thus, emotional intelligence and multiple intelligence do not only complete each other in achieving success but they also interact with each other. Ozdemir-Yaylaci (2006) suggests that individuals need to rely on their insights and use feelings more effective and need a rationalistic brain to think more wisely. It can be seen that emotional intelligence is the identifier of how better one can use the intelligence and other abilities that he has.
The success at school, family, work and social life can be determined by both cognitive and emotional intelligence. Success and effective learning emerge from the effective use of both the intellectual and emotional areas (Kizil, 2014). Goleman (2012) states that emotional and cognitive minds are placed in different sides of the brain but they are connected with each other. While the left side of the brain is associated with concrete abilities such as arithmetic, language, and reasoning, the right side is associated with abstract abilities such as distinguishing, managing and revealing the feelings. It is necessary for any intelligence or strategies to be successful feelings must be a stimulant which motivates people and make them feel devoted (Yuksel, 2006). Giving students the knowledge which only provides academic benefits makes them vulnerable to the problems they would encounter during their education process. Feelings such as taking a different approach to the problems they encounter, finding solutions, making the right decisions, establishing good communication, empathy, and being thoughtful must be taught. The improvement of emotional intelligence will make students more successful in their academic life by positively affecting mental skills and abilities (Ada, 2009).
When the related body of literature is examined, it is seen that it has been researched to determine the multiple intelligence areas of teacher candidates. Yenice and Aktamis (2010) revealed that the teacher candidates' intelligence areas were 'moderately developed' in their studies in order to examine the multiple intelligence areas of the class teacher candidates according to their demographic characteristics. Eyyam, Menevis and Dogruer (2010) found out that only 2,50% of the teacher candidates had developed a verbal -linguistic intelligence area in their study in order to reveal the multiple intelligence areas of the teacher candidates in Turkish Teaching Department and to examine whether this has anything related to academic success or not. Aksu, Aktas, Gokmen, Ekici and Gulay-Ogelman (2012) revealed that teacher candidates used logical-mathematical the most; and used social-interpersonal intelligence the least, that the difference between the dominant intelligence areas and gender, class, the type of high school and the academic achievement levels was not meaningful in the study that they conducted in order to evaluate multiple intelligence domains of pre-service teacher candidates according to different variables. Cinkilic and Soyer (2013) have reached that visual-spatial intelligence average of the teacher candidates is the highest, the bodily-kinesthetic intelligence average is the lowest and there is a meaningful difference between multiple intelligence areas and problem solving skills according to gender, age, monthly income of family, the individual's monthly expense amount, living place variables in the study which they conducted in order to examine the relationship between multiple intelligence areas and problem solving skills of the physical education teacher candidates. Kozagac (2015) revealed that teacher candidates had the highest score from logical-mathematical intelligence area while they had the lowest score from verbal-linguistic intelligence area in his studies in order to determine the multiple intelligence areas and to examine the social skills of mathematics department teacher candidates and the attitudes of them towards teaching profession. Ozturk, Ozsoy, Vural and Baysan (2017) revealed that the visual-spatial intelligence areas of the painting teacher candidates; and bodily-kinesthetic intelligence area of physical education teacher candidates are relatively lower in their study in order to determine the perceptions of teacher candidates about multiple intelligences. Yenice, Ozden and Alpak-Tunc (2017) reached the result that science teacher candidates had high scores on the logical-mathematical bodily-kinesthetic intelligence area. Deveci and Aydin (2017) reached the result that the visual intelligence area was the most contributing intelligence area for science teacher candidates to predict the entrepreneurial characteristics in the study that they examined the entrepreneurial characteristics of multiple intelligence areas of science teacher candidates. However, it has been revealed that musical intelligence and naturalist intelligence areas do not contribute significantly in terms of predicting entrepreneurial characteristics.
When the literature is reviewed, it is seen that the researches examined the relationship between emotional intelligence and multiple intelligence areas were conducted mostly with different sample groups except for teacher candidates. In their research Bay and Lim (2006) examined the relationship between emotional and multiple intelligence. They examined 360 primary school students whose ages were 10-11. The results revealed that there was no relationship between the emotional intelligence and social intelligence of the students. Skourdi and Rahimi (2010) conducted a research with 66 students to examine the relationship between emotional intelligence and verbal intelligence. Results suggested a positive correlation between emotional intelligence and verbal intelligence. In another research Baba (2012) conducted a research on 447 students from the school of physical education and sports to examine the relationship between the internal and external locus of control of kinesthetic and emotional intelligence. Results revealed that there was a relationship between kinesthetic and emotional intelligence. It is seen that there is a limited number of studies which examines the relationship between the multiple intelligence areas and emotional intelligence and the studies conducted on teacher candidates are even more limited. Individuals need both emotional intelligence and cognitive intelligence to be successful in their life. Multiple intelligence theory based on the learning experiences which improve different areas of intelligence (Gardner, 2006) and this will contribute to the improvement of emotional intelligence with in and out-of-class activities (Yesilyaprak, 2001). Moreover, individuals' being aware of their strengths and weaknesses influence their success in their educational and work life. Individuals can perform better in one area according to their strengths than in other areas, and in this direction awareness of strengths intelligence helps them to succeed at both education and business life. Emotional intelligence is the predictor of success in daily life or in education, since academic success is not sufficient for solving problems encountered in daily life (Van Der Zee, Thijs & Schakel, 2002). Due to there is no study in terms of examining the relationship between emotional intelligence scores and multiple intelligence areas of special education teacher candidates in the related body of literature, it is thought that the current research will contribute to the body of literature. Especially in a profession that the emotional side should be dominant such as special education teacher, it becomes important for the teacher candidates to identify themselves and to determine the estimated variables that will activate the dominant intelligence areas.

Purpose of the Study
This study aims to determine the predictor of the multiple intelligence areas of special education teacher candidates' emotional intelligence scores and demographic variables. In accordance with this purpose, does the special education teacher candidates' emotional intelligence scores and demographic variables predict the multiple intelligence areas? is the problem of research.
Sub-problems 1. Is the gender variable statistically significant in predicting the predominant area of intelligence? 2. Is the grade variable statistically significant in predicting the predominant area of intelligence? 3. Are the optimism scores statistically significant in predicting the predominant area of intelligence? 4. Are the scores related to utilization of emotions statistically significant in predicting the predominant area of intelligence? 5. Are the scores related to evaluation of emotions statistically significant in predicting the predominant area of intelligence?

Research Model
In this study, the relationship between dependent and independent variables will be examined; therefore the relational scanning model among the screening models is used. The relational scanning model is used to determine the relationships between the variables and to estimate the possible results (Karasar, 2013). Multiple intelligence areas from variables in which the model was created (interpersonal intelligence, kinesthetic intelligence, musical intelligence, visual intelligence, intrapersonal intelligence, logical intelligence, verbal intelligence predicted (dependent) variable, emotional intelligence sub-dimensions (optimism, utilization of emotions, evaluation of emotions) and demographic characteristics (gender and grade level) was determined as a predictor (independent) variable.

Study Group
211 special education teacher candidates, 106 females and 105 males, participated in the study. 65, 58, 48 and 40 of the special education teacher candidates studies are in first, second, third and fourth grades respectively. The sample of the research is formed of teacher candidates studying at Necmettin Erbakan University Special Education Department. The appropriate sampling method has been adopted in the sample selection. The appropriate sampling method refers to the data collection from a sample that the researchers can easily reach (Buyukozturk, Cakmak, Akgun, Karadeniz & Demirci, 2015). This sampling method was preferred to prevent loss of time, money and labor in this study. Demographic information regarding the study group has shown in Table 1.

Personal Information Form
Personal Information Form developed by the researchers includes gender and grade level information of teacher candidates.

Teele Inventory of Multiple Intelligences (TIMI)
TIMI developed by Sue Teele (1992) can be used to measure multiple intelligence areas of people from all ages and it could measure seven areas of intelligence. Inventory is composed of a question book which contains 56 panda pictures, a teacher's guide, answer sheets and clear answer keys. There are two panda pictures in each question and the individual must choose one of the two pictures. There are no right or wrong answers in the inventory.
Inventory measures seven intelligence areas. Sue Teele has seven intelligence areas in the inventory because she thinks that the naturalist intelligence has a common feature of other intelligence areas. There are eight images in the inventory for each intelligence area. Therefore, the number that individuals can take in an area of intelligence is maximum eight. The highest score received by the individuals according to their answers to the questions refers to the dominant intelligence area. After the markings for all the questions are completed, the answers in each intelligence area are counted and the points received by the participants are determined. The scores from the inventory always give 28. It is possible to apply the inventory both individually and for small or large groups. This inventory identifies the strengths or dominant intelligence areas of each participant in the 2 to 82 age range (Calisandemir, 2010).
The reliability and validity of the inventory were conducted by Teele in 1992Teele in -1993. The results of the study have done in a primary school revealed that the inventory is valid and the reliability of all the intelligence areas is significant at .01 level. In Turkey, reliability studies were conducted by Oklan-Elibol (2000), Gogebakan (2003), Terzioglu (2005), ; and validity study was done by .

Data Collection
In order to determine the emotional intelligence and multiple intelligence areas of special education teacher candidates, the data collection tools were applied to 211 special education teacher candidates based on voluntariness. It is stated that the special education teacher candidates should not write their names on the questionnaires in order to reflect their thoughts. Thus, research was conducted with the teacher candidates who accepted to participate in the research. Firstly, they were asked to fill the scale of emotional intelligence and to answer each question. The application lasted 15 minutes. Secondly, TIMI was applied. TIMI, which is used to determine multiple intelligence areas of special education teacher candidates, was applied to the group. It is stated that candidates should choose one of the two panda images in each question in the question booklet. They were asked to choose one of the panda pictures that they saw close or identified with them. To focus on the panda images included in each question during the application process, the panda pictures in the other question were covered with a white paper. It is specifically mentioned that only one picture can be selected for each question. It was stated that teacher candidates should mark their chosen answers to the answer key. The application lasted 35 minutes.

Data Analysis
In order to find out whether the gender and grade variables and the scores of optimism, utilization of emotions and evaluation of emotions are statistically effective in predicting the predominant area of intelligence, logistic regression analysis or not. Logistic regression analysis; it is a method used to determine cause and effect relationship with explanatory variables in case of dependent variable is in the categorical, double, triple and multiple categories (Ozdamar, 1999). Seven different areas of intelligence were included in the study such as interpersonal, kinesthetic, musical, visual, intrapersonal, logical and verbal intelligence. After determining their predominant area of intelligence, individuals were grouped according to their predominant area of intelligence. Then by creating puppet variables for each area of intelligence, they were included in the analysis as separate dependent variables. SPSS 24.0 was used in data analysis. The results were interpreted with 95% confidence level. Table 2 shows that the results of the logistic regression analysis found out how effective are the gender and grade variables and the scores of optimism, utilization of emotions and evaluation of emotions in predicting individuals whose predominant area of intelligence is both interpersonal and not. The regression model has found significant statistically (χ 2 =13,05; Sd=5; p<0,05). It is understood from the Table 2 that among the variables included in the regression analysis only gender (B=0,578; p<0,05) and optimism (B=0,893; p<0,05) are the meaningful variables which predict the determination of individuals whose predominant intelligence is both interpersonal intelligence and not. The variables included in the regression analysis explain 7% of determination of individuals whose predominant intelligence is both interpersonal intelligence and not (Nagelkerke R 2 = 0,072). According to the results, being a man increases the possibility of having interpersonal intelligence as a predominant intelligence 1,782 (78,2%) times. One point of increase in optimism score increases the possibility of having interpersonal intelligence as a predominant intelligence 2,443 (144,3%) times. Table 3 shows that the results of the logistic regression analysis to find out how effective are the gender and grade variables and the scores of optimism, utilization of emotions and evaluation of emotions in predicting individuals whose predominant intelligence is both kinesthetic and not. The regression model has not found significant statistically (χ 2 =1,97; Sd=5; p>0,05). As seen in Table 3, all the demographic variables and emotional intelligence scores included in the regression analysis are not meaningful in predicting individuals whose predominant intelligence is both kinesthetic and not (p>0.05). The variables included in the regression analysis express 2% of determination of individuals whose predominant intelligence is both kinesthetic intelligence and not (Nagelkerke R 2 = 0,016). Table 4 shows that the results of the logistic regression analysis to find out how effective are the gender and grade variables and the scores of optimism, utilization of emotions and evaluation of emotions in predicting individuals whose predominant intelligence is both musical and not. The regression model is not found significant statistically (χ 2 =6,81; Sd=5; p>0,05). As seen in Table 4, all the demographic variables and emotional intelligence scores included in the regression analysis are not meaningful in predicting individuals whose predominant intelligence is both musical and not (p>0.05). The variables included in the regression analysis explain 9% of determination of individuals whose predominant intelligence is both musical intelligence and not (Nagelkerke R 2 = 0,094). Table 5 shows that the results of the logistic regression analysis to find out how effective are the gender and grade variables and the scores of optimism, utilization of emotions and evaluation of emotions in predicting individuals whose predominant intelligence is both visual intelligence and not. The regression model is found significant statistically (χ 2 =11,03; Sd=5; p<0,05). It is understood from the Table 5 that among the variables included in the regression analysis only optimism score (B=-1,455; p<0,05) and evaluation of emotions score (B= 1,208; p<0,05) are the meaningful variables which predict the determination of individuals whose predominant intelligence is visual intelligence and not. The variables included in the regression analysis explain 11% of determination of individuals whose predominant intelligence is visual intelligence and not (Nagelkerke R 2 = 0,107). According to the results, 1 point of increase in optimism score decreases the possibility of having visual intelligence as a predominant intelligence 0,233 (76,7%) times. The results also revealed that 1 point of increase in evaluation of emotions score increases the possibility of having visual intelligence as predominant intelligence 3,346 (234,6%) times. Table 6 shows that the results of the logistic regression analysis to find out how effective are the gender and grade variables and the scores of optimism, utilization of emotions and evaluation of emotions in predicting individuals whose predominant intelligence is intrapersonal and not. The regression model has not found significant statistically (χ 2 =8,20; Sd=5; p>0,05). As seen in Table 6, all the demographic variables and emotional intelligence scores included in the regression analysis are not meaningful in predicting individuals whose predominant intelligence is intrapersonal and not (p>0.05). The variables included in the regression analysis explain 8% of determination of individuals whose predominant intelligence is intrapersonal intelligence and not (Nagelkerke R 2 = 0,080). Table 7 shows that the results of the logistic regression analysis to find out how effective are the gender and grade variables and the scores of optimism, utilization of emotions and evaluation of emotions in predicting individuals whose predominant intelligence is logical intelligence and not. The regression model has not found significant statistically (χ 2 =9,71; Sd=5; p>0,05). As seen in Table 7, all the demographic variables and emotional intelligence scores included in the regression analysis are not meaningful in predicting individuals whose predominant intelligence is logical and not (p>0.05). The variables included in the regression analysis explain 14% of determination of individuals whose predominant intelligence is logical intelligence and not (Nagelkerke R 2 = 0,142). Table 8 shows that the results of the logistic regression analysis to find out how effective are the gender and grade variables and the scores of optimism, utilization of emotions and evaluation of emotions in predicting individuals whose predominant intelligence is verbal intelligence and not. The regression model has not found significant statistically (χ 2 =2,94; Sd=5; p>0,05). It is understood from the Table 8 that all the demographic variables and emotional intelligence scores included in the regression analysis are not meaningful in predicting individuals whose predominant intelligence is verbal intelligence and not (p>0.05). The variables included in the regression analysis explain 3% of determination of individuals whose predominant intelligence is logical intelligence and not (Nagelkerke R 2 = 0,031).

Discussion and Conclusion
In this study, it was tried to determine the gender and grade level variables and emotional intelligence scores predicting multiple intelligence areas of special education teacher candidates. According to the findings obtained from the study, the first dominant intelligence area of gender and optimism is meaningful variables predicting the interpersonal intelligence area. Thus, it is seen that teacher candidates are male and that the first dominant intelligence area increases the probability of being interpersonal intelligence. However, it has reached that only the optimism and emotion evaluation scores of the variables included in the regression analysis are meaningful variables predicting the first dominant intelligence areas of the teacher candidates to be visual intelligence. However, it has revealed that the demographic variables and emotional intelligence scores included in the regression analysis did not predict the first dominant intelligence areas of teacher candidates as being kinesthetic intelligence, musical intelligence, intrapersonal intelligence, logical intelligence and verbal intelligence. In the related body of literature, it is seen that there are research results that reached different results. In his research which examines the relationship between emotional intelligence and interpersonal relationship styles, Enginsoy (2002) found that there was a significant relationship between the emotional intelligence of university students and their interpersonal relationship styles, and the grade which they were studying in partly affected the emotional intelligence and interpersonal relationship styles, and the average scores of subjects differentiated according to gender. Bay and Lim (2006 has reached that there was no relationship between students' interpersonal intelligence and emotional intelligence. Shearer (2006) stated that there is a relationship between interpersonal intelligence and emotional intelligence in the study he examines the relationship between multiple intelligence and emotional intelligence. It is possible that emotional intelligence is predictor of interpersonal intelligence when it is considered that the individual has the ability to manage his own feelings, to show positive attitude in social relations and to understand his inner world (Goleman, 2006;Teele, 2000). Since emotional intelligence includes social abilities, it is known that these abilities are closely associated with sharing, communicating positively and maintaining this communication (Stein & Book, 2000). In other words, individuals who have the adaptation ability and high interpersonal intelligence can adapt to different environments quickly (Eren-Yavuz, 2004) and individuals who have high emotional intelligence participate in social activities more. These two facts show that socializing and emotional intelligence are associated with each other (Ergin, Kaynak, Pinarcik & Aslan, 2013). Individuals who have high emotional and interpersonal potential are expected to be optimistic. The results revealed that the increase in optimism scores also increase the possibility of having interpersonal intelligence as a predominant intelligence. Since the interpersonal intelligence is associated with social abilities which are seen as the ability to communicate and interact with others, it is known that it is also associated with understating and managing others' feelings (Bay & Lim, 2006). Because the individuals who have high interpersonal intelligence have an ability to approach other people's emotions, fears, and interests with empathy (Basaran, 2004), it is remarkable that this area of intelligence is considerably improved among teachers. Individuals who have high emotional intelligence are described as socially balanced (Kocayoruk, 2004) and optimistic. Moreover, considering that emotional intelligence can develop with different experiences and different needs in different environments according to a person's request, opportunities for the development of visual intelligence areas in educational environments show that emotional intelligence can predict this intelligence area.
In this study, it is seen that demographic variables and emotional intelligence scores did not predict the state of being kinesthetic intelligence, musical intelligence, intrapersonal intelligence, logical intelligence and verbal intelligence. There are researches that reached both similar and different results in the related body of literature. Ferrandiz, Ferrando, Bermejo and Prieto (2005) reached that there is no relationship between emotional intelligence and multiple intelligence areas apart from the relationship between the use of emotions variable and logical intelligence. Rahimi, Sadighi and Fard (2011) reported that there is no meaningful relationship between emotional intelligence and verbal intelligence of university students. Shearer (2006) reached that logical intelligence is associated with optimism. Emotional intelligence scores and demographic variables do not predict the areas of kinesthetic intelligence, musical intelligence, intrapersonal intelligence, logical intelligence and verbal intelligence can be explained that although each individual has each area of intelligence, the intelligence potential of each individual is different and each intelligence area has a different structure. The effect of cultural structure on the development of intelligence areas can explain the fact that demographic variables are not predictive of intelligence areas. Therefore, the use of emotional intelligence and multiple intelligence in educational environments should be included. Emotional intelligence is quite important for the teaching profession. If, as Gardner says, it is possible to improve individuals' intelligence areas, it may be suggested for curriculum developers to implement programs to improve teacher candidates' multiple intelligence areas and emotional intelligence in faculties of education where there are initial teacher training programs and teachers are raised. The present study was conducted merely on the special education teacher candidates. Considering the importance of the issue in this study, the answers of the teacher candidates from a special education department at a university to the data collection tools are assumed as their own sincere thoughts constitutes the limitations of the research. It may be suggested that similar studies can be conducted on teacher candidates who are studying in other areas and on the teachers.