510 © 2019 by the authors; licensee Asian Online Journal Publishing Group Asian Journal of Education and Training Vol. 5, No. 4, 510-517, 2019 ISSN(E) 2519-5387 DOI: 10.20448/journal.522.2019.54.510.517 © 2019 by the authors; licensee Asian Online Journal Publishing Group Investigation of the Effect of Cognitive Behavioral and Physical Development Levels on the Multiple Intelligence of University Students Mahmut Gülle Hatay Mustafa Kemal University, School of Physical Education and Sports, Hatay, Turkey. Abstract In this study, it was designed to examine the multiple intelligence levels, cognitive, physical and affective levels of the faculty students studying at Hatay Mustafa Kemal University. The population of the study was composed of students studying at Hatay Mustafa Kemal University in 2018 - 2019 academic year. The sample included 1142 students (Male = 603, Female = 539) chosen by random sampling method. In the study, “Cognitive, Behavioral and Physical” scale developed by Schembre et al. (2015) adapted to Turkish by Eskiler et al. (2016) and “Multiple Intelligence” scale developed by Gardner (1990) adapted to Turkish by Demirtas and Duran (2007) were used as a data collection tool. In order to test the hypotheses of the study, t-test (Mann-Whitney U test) and analysis of variance in multiple comparisons (Kruskal Wallis-H test) were performed in addition to descriptive statistics such as arithmetic mean, standard deviation, frequency/percentage, normal distribution test, (Kolmogorov-Smirnov test). As a result of the study, it was found that there was a significant difference between the participants' cognitive behavioral physical activity and multiple intelligence levels in relation to gender, income status and sporting variables. Keywords: Multiple intelligence level, Cognitive physical and behavioral level, Physical education and sport, University students. Citation | Mahmut Gülle (2019). Investigation of the Effect of Cognitive Behavioral and Physical Development Levels on the Multiple Intelligence of University Students. Asian Journal of Education and Training, 5(4): 510-517. History: Received: 5 July 2019 Revised: 12 August 2019 Accepted: 23 September 2019 Published: 31 October 2019 Licensed: This work is licensed under a Creative Commons Attribution 3.0 License Publisher: Asian Online Journal Publishing Group Funding: This study received no specific financial support. Competing Interests: The author declares that there are no conflicts of interests regarding the publication of this paper. Transparency: The author confirms that the manuscript is an honest, accurate, and transparent account of the study was reported; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. Ethical: This study follows all ethical practices during writing. Contents 1. Introduction .................................................................................................................................................................................... 511 2. Materials and Methods ................................................................................................................................................................. 511 3. Results and Interpretation ........................................................................................................................................................... 512 4. Discussion and Conclusion ........................................................................................................................................................... 515 5. Suggestions ..................................................................................................................................................................................... 516 References ............................................................................................................................................................................................ 516 http://crossmark.crossref.org/dialog/?doi=10.20448/journal.522.2019.54.510.517&domain=pdf&date_stamp=2017-01-14 http://creativecommons.org/licenses/by/3.0/ http://creativecommons.org/licenses/by/3.0/ http://asianonlinejournals.com/index.php/EDU/article/view/1085 https://orcid.org/0000-0002-9967-0703 http://asianonlinejournals.com/index.php/EDU/article/view/1085 https://orcid.org/0000-0002-9967-0703 http://asianonlinejournals.com/index.php/EDU/article/view/1085 https://orcid.org/0000-0002-9967-0703 Asian Journal of Education and Training, 2019, 5(4): 510-517 511 © 2019 by the authors; licensee Asian Online Journal Publishing Group Contribution of this paper to the literature This study aims to assess the concurrent validity of a new, self-report measure of Gardner‟s multiple intelligences by relating scores on this test to measures of personality, approaches to learning as well as crystallized intelligence and self-estimated scores of those „intelligences‟. 1. Introduction According to Matlin (2008) the mind-related areas cover philosophy, linguistics, anthropology, artificial intelligence and neuroscience. Accordingly, cognition, that is believed to be the product of an individual's acquisitions with innate endowments, seems to be understood as a fact by sensation, perception, intelligence, memory, emotions, and spirit, biological and cultural contexts. In this regard, Lektorsky (1998) who criticized Piaget's known cognition theory as “generalization of empirical, psychological and history of science data”, asks if “could be cognitive theory?” to point out the difficulty in definition of cognition. Intelligence is a period that continues a person's life and offers people choices in their lives. Within this period; Gardner stated that individuals do not have the same way of thinking, but as a result of taking individual differences seriously with education, differences can be created in all individuals. If individuals can identify different components of intelligence, they may be considered to be more successful in solving problems they will face (Gardner and Hatch, 1990). Gardner carried away his research to a very serious level by observing people with mental differences such as talented, genius, mentally handicapped, brain damaged etc. within the Zero Project at Harvard University. In the extent of the research, he pointed out the wrongness of the traditional approach to intelligence and the need for a renewal in this regard (Armstrong, 2003). Today, many researchers, quite independent of each other, believe that there is a large number of intelligence and their own strengths and limitations; that the mind is not boundless from birth; that the inner strengths of intelligence has natural lines (Smith, 2002). After 1990s, it was seen that there were serious researches (Hoerr, 1996; Campbell and Campbell, 1999; Armstrong, 2000; Silver et al., 2000; Demirel, 2006; Almeida et al., 2010; Ahmad et al., 2015) done on multiple intelligence theory. It is realized that serious problems have been experienced in revealing the level of intelligence differences of the students as a result of the technological developments which have increased the importance of the studies carried out especially for the young generation of today. While using intelligence levels, students show the level of intelligence in which they are most powerful, while keeping pace with group dynamics. It has been observed that the practices based on this theory have a positive effect on student achievement and that the active participation and motivation of the students increased in the courses designed according to multiple intelligence theory (Campbell et al., 1992). In the light of the developments in the education system, it is seen that new curricula are prepared based on multiple intelligence theory. Learning-teaching process is formed on the basis of multiple intelligence theory and lesson plans are prepared accordingly (Erdem and Demirel, 2005). In this context, teachers have to organize different teaching activities in the teaching process in order to reach the determined goals. Teachers also need to have some skills in organizing teaching activities (Green, 2005). Gardner (1997) emphasizes that if the activities that are not compatible with the students' mental fields, the traditional development belong to only language and mathematics will continue or only those who are interested in these fields will benefit from this. However, he argues that the aim of the school is to reach out to more students and that the learning styles of the students need to be known. Within this framework, besides examining the multiple intelligence levels of students completing university education, it is thought that the study of mental, affective and physical development levels of students will increase the importance of the research. It is possible to talk about various studies on psychological, environmental, behavioral and social factors which are affecting the status of participation in physical activities and based on different theoretical foundations about participation of individuals in physical activities (Plotnikoff et al., 2013; Schembre et al., 2015). Schembre et al. (2015) stating the measurement tools used in these studies, which are shaped by different theoretical approaches, contain various differences and similarities, argue that a new measurement tool is needed in which the strengths of each theory are evaluated. Individuals succeed as long as they struggle not only physically but also mentally and emotionally perform positively in their field. 2. Materials and Methods In the research, a method for descriptive survey and relational survey was used to reveal the current situation. Descriptive survey models are research approaches aiming at defining a past or present situation as it exists. As for relational screening models, these research models aim to determine the presence and / or degree of covariance between two and more variables (Karasar, 2014). 2.1. Population and Sample of Research The population of the study consists of undergraduate students of Faculty of Education, Faculty of Sport Sciences, Faculty of Arts and Sciences, Faculty of Economics and Administrative Sciences, Faculty of Medicine and Veterinary Faculty of Hatay Mustafa Kemal University in 2018-2019 academic year. The sample of the study involved 1142 students, 539 female and 603 male, using convenient sampling method from different departments of Hatay Mustafa Kemal University. 2.2. Scales Used in Research 2.2.1. Cognitive Behavioral Physical Activity Scale The scale, which was developed by Schembre et al. (2015) and adapted to Turkish by Eskiler et al. (2016) consist of 3 sub-dimensions: Outcome Expectation, Self-Regulation, Personal Barriers and a total of 15 items. All expressions in the scale are scored with a 5-point Likert-type rating such as = 1 = Strongly disagree, 5 = Strongly agree”. Rate of the total variance explained by the scale is 54.12%. The internal consistency coefficient of the scale Asian Journal of Education and Training, 2019, 5(4): 510-517 512 © 2019 by the authors; licensee Asian Online Journal Publishing Group was α = .84, and the scores for the sub-dimensions were Expected Result = .85, Self-Regulation = .79 and Personal Barriers = .64. 2.2.2. Self-Assessment Scale in Multiple Intelligences This data collection tool was developed by Demirtas and Duran (2007) to measure eight different intelligences using Gardner (1990). The reliability test and factor analysis of this scale were conducted by Demirtas and Duran (2007). For this purpose, the scale was applied to 76 students. The data obtained were analyzed in SPSS package program. As a result of this analysis, Cronbach alpha internal consistency coefficient of the measurement tool was calculated as 0.884. At the same time, factor analysis was applied to the questionnaire, and items with scores less than 0.40 were excluded from the survey. In the Self-Assessment Scale of Multiple Intelligences applied to reveal the dominant intelligence of the students, 5 questions were applied to each of the 8 different intelligence areas and a total of 40 questions were applied to the students. In order to determine the degree of participation of students in each item. They have selected one of 5 options as follow: 5 “Completely A”, 4 "Agree”, 3 “Partially Agree”, 2 "I disagree” and 1 “I strongly disagree”. 2.3. Data Analysis In order to test the hypotheses of the study, t-test (Mann-Whitney U test) and analysis of variance in multiple comparisons (Kruskal Wallis-H test) were performed in addition to descriptive statistics such as arithmetic mean, standard deviation, frequency/percentage, normal distribution test, (Kolmogorov-Smirnov test). After the analysis of variance, the Bonferroni correction method was used to prevent type I and type II errors that might arise from binary comparisons to determine which groups had significant differences (significance level for income level variable (0.05) was divided into the amount of Mann-Whitney U test and significance level was determined as 0.017). 3. Results and Interpretation The results of the analysis belong to the data obtained within the scope of the research are reported in this section. The results of the analysis of the demographic variables are presented in Table 1. Table-1. Statistical distribution of participants according to demographic characteristics. Variables N % Variables N % Gender Income level 1. Men 603 52.8 1. Low 129 42.5 2. Women 539 47.2 2. Middle 936 30.0 Total 1142 100.0 3. High 77 27.5 Sports status Total 1142 100.0 1. Participating 669 58.6 2. Not participating 473 41.4 Total 1142 100.0 When examined Table 1, it was seen that 52.8% of the participants were male and 47.2% were female according to the gender variable of the participants. According to the results of the participants' sporting status variable, it was found that 58.6% did sports and 41.4% did not do sports. According to the income level variable, the income level of the participants is in the low-income group with 42.5%, in the middle-income group with 30.0%, and in the high income group with 27.5%. Table-2. Comparison of participants' cognitive behavioral physical activity and multiple intelligence levels according to gender variable. Dependent variables Gender N Mean rank Rank total U p Result expectation 1. Men 603 583.19 351665.50 155457.500 .204 2. Women 539 558.42 300987.50 Self-regulation 1. Men 603 614.78 370709.50 136413.500 .003 2. Women 539 523.09 281943.50 Personal barriers 1. Men 603 560.03 337699.50 155593.500 .212 2. Women 539 584.33 314953.50 Naturalist intelligence 1. Men 603 596.13 359466.00 147657.500 .007 2. Women 539 543.95 293187.00 Personal inner intelligence 1. Men 603 558.51 336781.00 154675.000 .157 2. Women 539 586.03 315872.00 Visual spatial intelligence 1. Men 603 527.51 318090.00 135984.000 .002 2. Women 539 620.71 334563.00 Interpersonal social intelligence 1. Men 603 574.10 346180.50 160942.500 .777 2. Women 539 568.59 306472.50 Logical mathematical intelligence 1. Men 603 612.36 369253.00 137870.000 .009 2. Women 539 525.79 283400.00 Physical kinesthetic intelligence 1. Men 603 599.68 361609.00 145514.000 .002 2. Women 539 539.97 291044.00 Verbal linguistic intelligence 1. Erkek 603 538.82 324906.00 142800.000 .003 2. Women 539 608.06 327747.00 Musical rhythmic intelligence 1. Men 603 561.62 338656.50 156550.500 .283 2. Women 539 582.55 313996.50 *P<0,05; N (1142). Asian Journal of Education and Training, 2019, 5(4): 510-517 513 © 2019 by the authors; licensee Asian Online Journal Publishing Group The results of the t-test to find out the significant difference between gender and dependent variables are presented in Table 2. As a result of the Mann-Whitney U test conducted to test whether there is a significant difference between participants' cognitive behavioral physical activity and multiple intelligence categories according to gender, while among self-regulation (U = 136413.500, p <0.05), naturalistic intelligence (U = 147657.500, p <0.05) visual spatial intelligence (U = 135984.000, p <0.05), logical mathematical intelligence (U = 137870.000, p <0.05) ), physical kinesthetic intelligence (U = 145514.000, p <0.05) and verbal linguistic intelligence (U = 142800.000, p <0.05) levels were found to have significant differences, the results expectation (U = 155457.500, p> 0.05), personal barriers (U = 155593.500, p> 0.05), personal inner intelligence (U = 154675.000, p> 0.05), interpersonal social intelligence (U = 160942.500, p> 0.05) and musical rhythmic intelligence (U = 156550.500, p> 0.05) levels were statistically not significant. Table-3. Comparison of participants' cognitive behavioral physical activity and multiple intelligence levels according to the sporting status variable. Dependent variables Sports status N Mean rank Rank total U p Result expectation 1. Participating 669 639.41 427768.00 112784.000 .001 2. Not participating 473 475.44 224885.00 Self-regulation 1. Participating 669 676.50 452581.50 87970.500 .001 2. Not participating 473 422.98 200071.50 Personal barriers 1. Participating 669 518.28 346726.50 122611.000 .007 2. Not participating 473 648.78 305927.50 Naturalist intelligence 1. Participating 669 594.12 397467.50 143084.500 .006 2. Not participating 473 539.50 255185.50 Personal inner intelligence 1. Participating 669 564.22 377461.00 153346.000 .373 2. Not participating 473 581.80 275192.00 Visual spatial intelligence 1. Participating 669 564.31 377523.50 153408.500 .379 2. Not participating 473 581.67 275129.50 Interpersonal social intelligence 1. Participating 669 580.15 388123.50 152428.500 .289 2. Not participating 473 559.26 264529.50 Logical mathematical intelligence 1. Participating 669 583.72 390510.50 150041.500 .135 2. Not participating 473 554.21 262142.50 Physical kinesthetic intelligence 1. Participating 669 647.07 432893.00 107659.000 .002 2. Not participating 473 464.61 219760.00 Verbal linguistic intelligence 1. Participating 669 583.63 390450.50 150101.500 .138 2. Not participating 473 554.34 262202.50 Musical rhythmic intelligence 1. Participating 669 600.36 401641.00 138911.000 .004 2. Not participating 473 530.68 251012.00 *P<0,05; N (1142). The t-test results for determining the significant difference between the sporting state variable and dependent variables are presented in Table 3. According to the Mann-Whitney U test conducted to test whether there is a significant difference between participants' cognitive behavioral physical activity and multiple intelligence categories according to the status of doing sports, between the participants who do sports and those who do not do sports, results expectation (U = 112784.000, p <0.05), self-regulation (u = 87970.500, p <0.05), personal barriers (U = 122611.000, p <0.05), naturalistic intelligence (U = 143084.500, p <0.05), physical kinesthetic intelligence (U = 107659.000, p <0.05) and musical rhythmic intelligence (U = 138911.000, p <0.05) levels were found to have statistically significant difference, however, personal inner intelligence (U = 153346.000, p> 0.05), visual spatial intelligence (U = 153408.500, p> 0.05), interpersonal social intelligence (U = 152428.500, p> 0.05), logical mathematical intelligence (U = 150041.500, p> 0.05) and verbal linguistic intelligence (150101.500, p> 0.05) levels did not show any statistically significant difference. The results of the analysis of variance to determine the significant difference between the income level variable and dependent variables are presented in Table 4. As a result of Kruskal Wallis H test, which was conducted to test whether there was a significant difference between participants' cognitive behavioral physical activity and multiple intelligence categories according to income level variable, while result expectation χ2 (sd = 2, n = 1142) = 0.95, p> 0.05), personal barriers χ2 (sd = 2, n = 1142) = 2.325, p> 0.05), interpersonal social intelligence χ2 (sd = 2, n = 1142) = 0.912, p> 0.05) and musical rhythmic intelligence χ2 (sd = 2, n = 1142) = 0.532, p> 0.05) variables did not show any statistically significant difference, self-regulation χ2 (sd = 2, n = 1142) = 26.669, p <0.05), naturalist intelligence χ2 (sd = 2, n = 1142) = 11.920, p <0.05), personal inner intelligence χ2 (sd) = 2, n = 1142) = 33.647, p <0.05), visual spatial intelligence χ2 (sd = 2, n = 1142) = 21.371, p <0.05), logical mathematical intelligence χ2 (sd = 2, n = 1142) = 21.598, p <0.05), physical kinesthetic intelligence χ2 (sd = 2, n = 1142) = 17.703, p <0.05) and verbal linguistic intelligence χ2 (sd = 2, n = 1142) = 9.197, p <0.05) variables showed statistically significant difference. Mann Whitney U tests were applied to determine which income level cause the significant difference between the dependent variables. As a result of the tests, it was found that the statistically significant difference in the self- regulation variable was between high income participants and low (U = 3753.000, p <0.017) and medium (U = 23795.500, p <0.017) income level participants. Similarly, there were significant differences between; high income level participants and low income level (U = 2567.500, p <0.017) and middle income (U = 29577.000, p <0.017) level participants in the naturalistic intelligence variable; high spatial intelligence variable and low (U = 3104.000, p <0.017) and medium (U = 25133.500, p <0.017) income participants in the visual spatial intelligence variable; high-income level participants and low (U = 3022.000, p <0.017) and medium (U = 26139.000, p <0.017) income participants in the logical mathematical intelligence variable; high income level participants and low (U = 3446.500, p <0.017) and moderate (U = 25847.500, p <0.017) income participants in the physical kinesthetic Asian Journal of Education and Training, 2019, 5(4): 510-517 514 © 2019 by the authors; licensee Asian Online Journal Publishing Group intelligence variable; high income level participants and those with low (U = 3830.500, p <0.017) and moderate (U = 28744.500, p <0.017) income participants in the verbal linguistic variable. Table-4. Variance analysis results of participants' cognitive behavioral physical activity and multiple intelligence levels according to income level variable. Dependent variables Income N Mean rank sd χ2 p (I-J) Result expectation 1. Low 129 563.70 2 0.95 .953 2. Middle 936 572.15 3. High 77 576.68 Self-regulation 1. Low 129 608.78 2 26.669 .002 2. Middle 936 551.99 1-3, 2-3 3. High 77 746.53 Personal barriers 1. Low 129 610.93 2 2.325 .313 2. Middle 936 565.05 3. High 77 583.90 Naturalist intelligence 1. Low 129 510.27 2 11.920 .003 2. Middle 936 571.54 1-3, 2-3 3. High 77 673.55 Personal inner intelligence 1. Low 129 437.74 2 33.647 .004 2. Middle 936 579.40 1-2, 1-3, 2-3 3. High 77 699.55 Visual spatial intelligence 1. Low 129 536.67 2 21.371 .001 2. Middle 936 562.66 1-3, 2-3 3. High 77 737.28 Interpersonal social intelligence 1. Low 129 562.06 2 0.912 .634 2. Middle 936 570.07 3. High 77 604.79 Logical mathematical intelligence 1. Low 129 509.27 2 21.598 .002 2. Middle 936 567.43 1-3, 2-3 3. High 77 725.29 Physical kinesthetic intelligence 1. Low 129 556.96 2 17.703 .001 2. Middle 936 561.00 1-3, 2-3 Verbal linguistic intelligence 3. High 77 723.56 1. Low 129 557.85 2 9.197 .010 2. Middle 936 569.36 1-3, 2-3 3. High 77 597.81 1. Low 129 571.36 2 0.532 .766 Musical rhythmic intelligence 2. Middle 936 569.36 3. High 77 597.81 *P<0,05; ** P<0,017, N (1142). A statistically significant difference was found between the participants with high income level and the participants with low (U = 2723.000, p <0.017) and moderate (U = 28419.500, p <0.017) income level in the personal inner intelligence variable as well as between the participants with low income and those with moderate (U = 45360.500, p <0.017) income level. Table-5. Correlation test results between participants' cognitive behavioral physical activity and multiple intelligence levels. Dependent variables RE SR PB NI PII VSI ISI LMI PKI VLI SR .388** PB -.089** -.172** NI .183** .149** -.004 PII .135** .098** .058 .312** VSI .151** .022 .068* .291** .223** ISI .151** .027 .016 .137** .093** .233** LMI .153** .029 .034 .284** .244** .148** .221** PKI .242** .255** -.115** .293** .176** .274** .291** .271** VLI .074* .205** .018 .083** .178** .185** .049 .006 .071* MRZ .202** .092** .072* .218** .181** .320** .267** .129** .269** .205** *P<0,05; **P<0,01; N (1142). Table 5 shows the correlation test results for the determination of the relationship between the cognitive behavioral physical activity and multiple intelligence levels of the participants. As a result of the spearman correlation test which was used to test whether there was a significant relationship between the dependent variables, there were moderately positive correlation between the participants' result expectation variable and self-regulation variable (r = .388; p <0.01) and a significant positive correlation between the result expectation variable and natural intelligence (r = .183; p <0.01), personal inner intelligence (r = .135; p <0.01), visual spatial intelligence (r = .151; p <0.01), interpersonal social intelligence (r = .151; p <0.01), logical mathematical intelligence (r = .153; p <0.01), physical kinesthetic intelligence (r = .242; p <0.01), verbal linguistic intelligence (r = .074; p <0.05) and musical rhythmic intelligence (r = .202 ; p <0.01), a negative correlation between the expectation variable and personal barriers (r = -.089; p <0.01). There was low level negative correlation between self-regulation variable and personal barriers (r = -.172; p <0.01) variables and a low level positive correlation between the self-regulation variable with natural intelligence (r = .149; p <0.01), personal inner intelligence (r = .098; p <0.01), physical kinesthetic intelligence (r = .255; p Asian Journal of Education and Training, 2019, 5(4): 510-517 515 © 2019 by the authors; licensee Asian Online Journal Publishing Group <0.01), verbal linguistic intelligence (r = .205; p <0.01) and musical rhythmic intelligence (r = .092; p <0.01) variables of participants. Moreover, a low level positive correlation were found between the variables of personal barriers and visual spatial intelligence (r = .068; p <0.05) and musical rhythmic intelligence (r = .072; p <0.01) of the participants, a low level significant negative correlation between personal barriers variable and physical kinesthetic intelligence (r = -.115; p <0.01) variable, moderately positive correlation between naturalistic intelligence variable and personal inner intelligence (r = .312; p <0.01), a low-level positive significant correlation between naturalistic intelligence variable and visual spatial intelligence (r = .291; p <0.01), interpersonal social intelligence (r =. 137; p <0.01), logical mathematical intelligence (r = .284; p <0.01), physical kinesthetic intelligence (r = .293; p <0.01), verbal linguistic intelligence (r = .083; p <0.01) and musical rhythmic intelligence (r = .218; p <0.01) variables. In addition, a low-level significant positive correlation was found between visual spatial intelligence (r = .223; p <0.01), interpersonal social intelligence (r = .093; p <0.01), logical mathematical intelligence (r = .244; p <0.01), physical kinesthetic intelligence (r = .176; p <0.01), verbal linguistic intelligence (r = .178; p <0.01) and musical rhythmic intelligence (r = .181; p <0.01) variables with personal inner intelligence variables of participants. A low- level significant positive correlation was found between visual spatial intelligence and interpersonal social intelligence (r = .233; p <0.01), logical mathematical intelligence (r = .148; p <0.01), physical kinesthetic intelligence (r = .274; p <0.01) and verbal linguistic intelligence (r = .185; p <0.01) variables, and a moderate positive correlation between visual spatial intelligence and musical rhythmic intelligence (r = .320; p <0.01) variables of participants. Participants' interpersonal social intelligence variable and logical mathematical intelligence (r = .221; p <0.01), physical kinesthetic intelligence (r = .291; p <0.01) and musical rhythmic intelligence (r = .267; p <0.01) variables showed a low-level positive significant correlation. Also, a low-level significant positive correlation was found between the participants' logical mathematical intelligence variables and physical kinesthetic intelligence (r = .271; p <0.01) and musical rhythmic intelligence (r = .129; p <0.01) variables. Similarly, low positive correlation was detected between the participants 'physical kinesthetic intelligence and verbal linguistic intelligence (r = .071; p <0.05) and musical rhythmic intelligence (r = .269; p <0.01) variables, and the participants' verbal linguistic intelligence variable and musical rhythmic intelligence (r = .205; p <0.01). 4. Discussion and Conclusion When the mean rank values of the gender variable are analyzed, it was seen that male participants 'self- regulation naturalistic, logical mathematical intelligence and physical kinesthetic intelligence mean rank values were higher than the mean rank of female participants' self-regulation, naturalistic intelligence, logical mathematical intelligence and physical kinesthetic intelligence. On the other hand, it was found that the mean rank of visual spatial and verbal linguistic intelligence rankings of female participants were higher than the mean rank of visual spatial and verbal linguistic intelligence of male participants. According to the research conducted by Kuzgun and Deryakulu (2004) it was concluded that gender is not important in terms of physical intelligence score and social intelligence score (Kuzgun and Deryakulu, 2004). In the study conducted by Cinkılıç and Soyer (2013) on the multiple intelligence levels of physical education teacher candidates, they concluded that gender does not constitute significance. These results create an opposite situation with our study. Furnham et al. (2002) conducted a study with British, American and Japanese participants aimed at identifying intelligence types. As a result of this study, no significant difference was found between men and women in the field of verbal linguistic intelligence. It was parallel with the study done by David (2003). In this study, there was no significant difference between the fields of naturalistic intelligence according to gender. However, in a study conducted by Dogan and Alkis (2007) the results of the university students' use of multiple intelligence fields in the classes are parallel with our study. Loori (2005) found out that the participants were examined separately as men and women in the logical-mathematical intelligence type, and it was found out that the difference was in favor of men according to the averages in the field of logical- mathematical intelligence. When the mean rank values of the sporting status variable are examined, the mean rank of the result expectation, self-regulation, natural intelligence, physical kinesthetic intelligence and musical rhythmic intelligence of the participants doing sports were higher than means rank of the result expectation, self-regulation natural intelligence, physical kinesthetic intelligence and musical rhythmic intelligence of the participants who do not do sports. On the other hand, the personal barriers mean rank of the participants who do not do sports was higher than the personal barriers mean rank of the participants doing sports. Tekin (2009) compared the levels of male and female athletes in different types of intelligence in individual and team sports, and as a result of the study, it was found that male students had higher logical-mathematical intelligence and physical kinesthetic intelligence areas than female students. In this study, it was found that the athletes engaged in individual sports had higher social and inner intelligence areas than the athletes engaged in team sports. According to the class variable; in the 9th grade students who do sports, verbal linguistic intelligence, logical-mathematical intelligence, inner intelligence, musical rhythmic intelligence and visual spatial intelligence were higher than 11th grade students. Erturan et al. (2005) showed that the physical intelligence fields of the students who do sports and those who do not do sports are different from each other. In the studies done by Katz et al. (2002); Bayrak et al. (2005) and Tekin (2008) based on multiple intelligence theory, it was revealed that social intelligence creates significance in terms of sportive activity. Besides, it was revealed that the musical intelligence fields of the students who do sports and those who do not do sports are very close to each other (Bümen, 2004). In the studies conducted by Hoşgörür and Katrancı (2007). Tekin (2008) and Cengiz (2008) it was found that the naturalistic intelligence levels of individuals engaged in sports were high. In the studies conducted by Hoşgörür and Katrancı (2007) and Tekin (2008) it was revealed that the level of inner intelligence was positive in favor of the ones doing sports. It was also found that the logical intelligence fields of the students who do sports and those who do not do sports are very close to each other. Individuals with a field of logical intelligence are very sensitive and susceptive to logic rules, cause-and-effect relationships, making and Asian Journal of Education and Training, 2019, 5(4): 510-517 516 © 2019 by the authors; licensee Asian Online Journal Publishing Group questioning assumptions and similar abstract processes (Campbell, 1990; Saban, 2005). Tekin and Taşğın (2008) examined the relationship between creativity and multiple intelligence areas of secondary school students, who are doing sports and not doing sports. According to the results of the study, male students engaged in sports in secondary education had higher logical-mathematical intelligence and physical-kinesthetic intelligence areas than female students. When the mean rank values of the income level variable are analyzed, it was seen that the mean rank values of self-regulation, natural intelligence, personal inner intelligence, visual spatial intelligence, logical mathematical intelligence physical kinesthetic intelligence and verbal linguistic intelligence of participants with high income level were higher than the mean rank values of self-regulation natural intelligence, personal intrinsic intelligence, visual spatial intelligence, logical mathematical intelligence, physical kinesthetic intelligence and verbal linguistic intelligence of low income level participants. Similarly, when the mean rank values of the income level variable were examined, it was observed that the mean rank values of self-regulated, naturalistic intelligence, personal inner intelligence, visual spatial intelligence, logical mathematical intelligence, physical kinesthetic intelligence and verbal linguistic intelligence of participants with high income levels were higher than mean rank values of the natural intelligence, personal inner intelligence, visual spatial intelligence, logical mathematical intelligence, physical kinesthetic intelligence and verbal linguistic intelligence of participants with middle income level. When the mean rank values of personal inner intelligence of the low- and middle-income participants was examined, it was seen that the mean rank values of middle income participants were higher than the mean rank of the low income participants. In a study conducted by Altınok (2008) the kinesthetic intelligence of those with low income was found to be high. In the study of Karademir et al. (2010) they found no relationship between family income and intelligence levels in the study conducted on the candidates who took the physical education and sport exam. Abaci and Baran (2007) reported that there was no significant difference in the intelligence scores according to the income level variable of university students. In terms of musical intelligence, there was a significant difference in the field of musical intelligence, and candidates with poor economic status had higher scores than those with good and moderate economic intelligence. There is no other type of intelligence other than musical intelligence that was found to be significant (Cinkılıç and Soyer, 2013). According to the economic situation, the highest results were obtained in the field of social and physical intelligence. Although there was no statistical difference in these two sub-dimensions, the averages indicated highly developed intelligence level. This showed that those who are economically poor have good social and physical intelligence. In other words, it would not be wrong to say that economic insufficiency has no negative effect on social and physical intelligence. In a study, a significant difference was found in physical-kinesthetic intelligence and nature intelligence of multiple intelligence types in terms of economic status variable (Altınok, 2008). Again, Altınok (2008) did not find statistically any significant difference in the comparison of the scores related to logical-mathematical intelligence, verbal intelligence and musical- rhythmic intelligence of the sub-dimensions of multiple intelligence theory in terms of economic status. A statistically significant difference was found in the comparison of scores of physical-kinesthetic intelligence and natural intelligence in terms of economic status variable. Although this study shows some similar results with our study, a significant difference was found only in naturalistic intelligence in our study (Altınok, 2008). According to Gardner (2004) multiple intelligence theory, it was seen that individuals studying in physical education and sports departments had one or more intelligence areas and had differences between individuals according to gender, class, department, type of sports, income status and place of residence. It is thought that these areas of intelligence are affected by social, environmental, economic, etc. situations. 5. Suggestions It can be thought that multiple intelligence tests may be better for younger individuals in terms of age category and revealing individual differences. When talent selection is to be done, before the selection process of individuals, intelligence tests can be considered to help in the selection of branches and categories of athletes. Experimental studies to reveal the physical, cognitive and affective processes will contribute to the literature. References Abaci, R. and A. Baran, 2007. The relationship between multiple intelligence levels of university students and some variables. International Journal of Human Sciences, 4(1): 1-13. Ahmad, A.R., A.A. Seman, M.M. Awang and F. Sulaiman, 2015. Application of multiple intelligence theory to increase student motivation in learning history. Asian Culture and History, 7(1): 210-219.Available at: https://doi.org/10.5539/ach.v7n1p210. Almeida, L.S., M.D. Prieto, A.I. Ferreira, M.R. Bermejo, M. Ferrando and C. Ferrándiz, 2010. Intelligence assessment: Gardner multiple intelligence theory as an alternative. Learning and Individual Differences, 20(3): 225-230.Available at: https://doi.org/10.1016/j.lindif.2009.12.010. Altınok, E., 2008. Examination of multiple intelligence areas of physical education students according to some variables. Master Thesis, Selcuk University, Konya. Armstrong, T., 2000. Multiple intelligences in the classroom. 2nd Edn., Alexandria, Virginia USA: Association for Supervision and Curriculum Development. Armstrong, T., 2003. The multiple intelligences of reading and writing. Alexandria, VA: Association for Supervision and Curriculum Development. Bayrak, Ç., M.A. Celiksoy and S. Celiksoy, 2005. Students in the school of physical education and sports related to multiple intelligence theory of intelligence profiles and the relationship with the ability to apply exams. 4th National Symposium on Physical Education and Sports Teaching, Bursa, 66. Bümen, T.N., 2004. Child intelligence theory in school. Ankara: Pegem Publishing. Campbell, B., 1990. The research results of a multiple intelligences classroom. New Horizons for Learning on the Beam, 11(1): 254-261. Campbell, L., B. Campbell and D. Dickinson, 1992. Teaching and learning through multiple intelligences. Needham Heights, Massachusets 02194: Allyn, Bacon, A. Simon and Schuster Company. Campbell, L.M. and B. Campbell, 1999. Multiple intelligences and student achievement: Success stories from six schools. USA: Association for Supervision and Curriculum Development. Cengiz, Ş., 2008. The distribution of 8-10 years old children in multiple intelligence types and effects of football education on multiple intelligence levels. PhD Thesis. Gazi University, Institute of Health Sciences, Ankara. Asian Journal of Education and Training, 2019, 5(4): 510-517 517 © 2019 by the authors; licensee Asian Online Journal Publishing Group Cinkılıç, İ. and F. Soyer, 2013. Examination of the relationship between multiple intelligence areas and problem solving skills of pre-service physical education teacher candidates. Sports Management and Information Technologies, 8(1): 4-16. David, C.W., 2003. Multiple intelligences and perceived self-efficacy among Chinese secondary school teachers in Hong Kong. Educational Psychology, 23(5): 521-533.Available at: https://doi.org/10.1080/0144341032000123778. Demirel, Ö., 2006. Program development from theory to practice education. 9th Edn., Ankara: Pegem Publishing. Demirtas, Z. and A. Duran, 2007. Elementary school 6th, 7th and 8th grade students' development levels of multiple intelligence areas. Electronic Journal of Social Sciences, 6(20): 208-220. Dogan, Y. and S. Alkis, 2007. The views of prospective classroom teachers about using multiple intelligence fields in social studies courses. Uludag University Faculty of Education Journal, 20(2): 327-339. Erdem, E. and Ö. Demirel, 2005. Teachers' views on multiple intelligence theory. 14th National Educational Sciences Congress Proceedings. Denizli: Pamukkale University. pp: 984- 988. Erturan, G., U. Dundar, A. Constructor, H. Cakir and E. Bozyigit, 2005. Comparison of the primary school students' field of fitness and sports fitness. 14th National Educational Sciences Congress, Denizli, 1001. Eskiler, A., F. Küçükibiş, M. Gülle and F. Soyer, 2016. Cognitive behavioral physical activity scale: Validity and reliability study. Journal of Human Sciences, 13(2): 2577-2587. Furnham, A., T. Hosoe and T.L. Tang, 2002. Male hubris and female humility? A crosscultural study of ratings of self, parental, and sibling multiple intelligence in America, Britain, and Japan. Intelligence, 30(1): 101–115. Gardner, H., 1990. The theory of multiple intelligences, in: N. Entwistle (ed.) Handbook of Educational Ideas and Practices. London: Routledge. Gardner, H., 1997. Multiple intelligences as a partner in school improvement. Educational Leadership, 55(1): 20-21. Gardner, H., 2004. Multiple intelligence theory of mind frames, trans. Istanbul: Ebru Kilic, Alfa Publishing. Gardner, H. and T. Hatch, 1990. Multiple intelligences go to school: Educational implications of the theory of multiple intelligences. CTE Technical Report Issue NoA. Green, L., 2005. The music curriculum as lived experience: Children's “natural” music-learning processes. Music Educators Journal, 91(4): 27-32.Available at: https://doi.org/10.2307/3400155. Hoerr, T.R., 1996. Introducing the theory of multiple intelligences. NASSP Bulletin, 80(583): 8-10.Available at: https://doi.org/10.1177/019263659608058303. Hoşgörür, V. and M. Katrancı, 2007. The dominant intelligence areas of classroom and physical education and sports teacher students (Kırıkkale University Faculty of Education Case). Journal of Ondokuz Mayıs University Faculty of Education, 24: 33-42. Karademir, T., E. Döşyılmaz, B. Coban and M.E. Kafkas, 2010. Self-esteem and emotional intelligence in students who attend the special talent exam of physical education and sports department. Kastamonu Education Journal, 18(2): 653-674. Karasar, N., 2014. Scientific research method Ankara. Ankara: Nobel Publication Distribution. Katz, J., P. Mirenda and S. Auerbach, 2002. Instructional strategies and educational outcomes for students with developmental disabilities in inclusive “multiple intelligences” and typical inclusive classrooms. Research and Practice for Persons with Severe Disabilities, 27(4): 227-238.Available at: https://doi.org/10.2511/rpsd.27.4.227. Kuzgun, Y. and D. Deryakulu, 2004. Individual differences in education. 1st Edn., Ankara: Nobel Yayın Dağıtım. Lektorsky, V., 1998. Subject, object, cognition, trans. Istanbul: Sukru Alpagut, Social Transformation Publications. Loori, A.A., 2005. Multiple intelligences: A comparative study between the preferences of males and females. Social Behavior and Personality: An International Journal, 33(1): 77-88.Available at: https://doi.org/10.2224/sbp.2005.33.1.77. Matlin, M.W., 2008. Cognition. USA. 7th Edn., USA: John Wiley & Sons Inc. pp: 81. Plotnikoff, R.C., S.A. Costigan, N. Karunamuni and D.R. Lubans, 2013. Social cognitive theories used to explain physical activity behavior in adolescents: A systematic review and meta-analysis. Preventive Medicine, 56(5): 245-253.Available at: https://doi.org/10.1016/j.ypmed.2013.01.013. Saban, A., 2005. Multiple intelligence theory and education. 5th Edn., Ankara: Nobel Publications. Schembre, S.M., C.P. Durand, B.J. Blissmer and G.W. Greene, 2015. Development and validation of the cognitive behavioral physical activity questionnaire. American Journal of Health Promotion, 30(1): 58-65.Available at: https://doi.org/10.4278/ajhp.131021-quan-539. Silver, H., R. Strong and M. Perini, 2000. So each may learn “integrating learning styles and multiple intelligences. 2nd Edn., Alexandria, VA: ASCD. Smith, M.K., 2002. Howard gardner and multiple intelligences. The Encyclopedia of Informal Education, 15, 2012. Tekin, M., 2008. Investigation of creativity and multiple intelligences between sports and non-sports students among secondary school students. PhD Thesis, Gazi University Institute of Educational Sciences, Ankara. Tekin, M., 2009. Individual and team sports in boys and girls athletes comparing the level of different types of intelligence. Atatürk University Journal of Physical Education and Sport Sciences, 11(4): 29-51. Tekin, M. and Ö. Taşğın, 2008. Investigation of the relationship between creativity and multiple intelligence areas of students who do sports and not do sports in secondary education. Niğde University Journal of Physical Education and Sports Sciences, 2(3): 206-214. Visser, B.A., M.C. Ashton and P.A. Vernon, 2006. Beyond g: Putting multiple intelligences theory to the test. Intelligence, 34(5): 487- 502.Available at: https://doi.org/10.1016/j.intell.2006.02.004. Asian Online Journal Publishing Group is not responsible or answerable for any loss, damage or liability, etc. caused in relation to/arising out of the use of the content. Any queries should be directed to the corresponding author of the article.