Music Intelligence and Music Theory Learning: A Cognitive Load Theory Viewpoint PDF

www.ccsenet.org/ijps International Journal of Psychological Studies Vol. 2, No. 2; December 2010 ISSN 1918-7211 E-ISSN 1918-722X 150 Music Intelligence and Music Theory Learning: A Cognitive Load Theory Viewpoint Osamah (Mohammad Ameen) Ahmad Aldalalah School of Educational Studies, Universiti Sains Malaysia Minden 11800, Penang, Malaysia Tel: 60-1-7413-2557 E-mail: [email protected] Soon Fook Fong (Corresponding author) School of Educational Studies, Universiti Sains Malaysia Minden 11800, Penang, Malaysia Tel: 60-4-653-2968 E-mail: [email protected] Abstract The purpose of this study was to investigate the effects of cognitive load theory and the role of music intelligence on the learning of music theory among Jordanian primary pupils. The independent variable was music intelligence levels (high, low). The dependent variables were the post te st score. An analysis of covariance (ANCOVA) was carried out to examine the main effects of the independent variables on the dependent variables. The findings of this study showed that pupils with hi gh music intelligence pupils performed significantly better than low music intelligence pupils. Keywords: Music theory, Cognitive load theory, Music Intelligence 1. Introduction Over the recent decades of the 20th century, international ed ucational systems along with the important role of the teachers in community development have emphasized the need for quality education systems with specific focus on the preparation of the teacher in terms of mental aptitudes and abilities (Qtami, 2005). The great potentials of the human mind and how it may be developed demonstra tes the importance of having open minded learners who meet the expectations of their communities and perform effectively for the post-industrial community. This necessitates a higher level of cogn itive adaptability in the third millennium (Otoum, 20 03). To achieve such a goal, planning of curricula was sought in order to develop textbooks that are writtem based on the findings of academic research in psychology and the related cognitive psychology fields. Psychology research indicates that students differ in their intelligence and how they learn. The adoption of one traditional teaching style that depends on reiteration or lecturing would make students feel bored. Psychologists, however, have long been interested in developing many teaching strategies to meet different learning styles (Gnam, 2005). Among the newly established teaching methods is the one which is based on the multiple intelligence approach by Howard Gardner. Intelligence plays a significant role in human life si nce there is a strong relationship between intelligence and achievement implying that the higher the level of intelligence, the greater the achievement and hence, greater distinction and academic success. 2. Music theory Many scholars such as Nosir (1980) define music theory as the area in which music works are studied. It mainly deals with the language and notion of musi c where it is composed and interpreted. It assists to categorize the various music patterns and structures experienced in the process of composition throughout genres, styles or during historical periods. According to Chew (2005) mu sic is a language that possesses both universal context and notations. On the other hand, Aldalalah (2003) argues that music provides a unique structure for musicians to reveal their musical concepts. This is because it focuses on music notation is composed in terms of the components of the notation. Also, it involves basic musical concepts that may be observed in forms of the structure, the organization and the history (Smith, 2009). These musical concepts have an important role in establishing the necessary knowledge for inte rpreting the development stag es in music and the mode in which the notation is utilized in various situations. www.ccsenet.org/ijps International Journal of Psychological Studies Vol. 2, No. 2; December 2010 Published by Canadian Center of Science and Education 151 3. Musical Intelligence and Learning Musical intelligence is described as the feeling of musical pitches, sound rhythm an d tempo as well as being emotionally affected by such musical components (Gardner, 1983). Intelligence, however, is easily seen in the learners who can automatically remember melodies, identi fy pitches and rhythms. Learners are thus described as more inclined to hear music and are highly sensitive to sounds around them (Gardner, 2000). Gardner (2004) argued that musical intellig ence varies according to people as everyone has his own musical ability, while some others have nothing to do with music. Musical intelligence is related to the identification of tones, melodies, sounds, rhythm, and tempos, particularly, the sense of tone types, melody composition, and sensitivity to sounds as well as using charts for music hearing and understanding musical structure. Musical intelligence is the most emerging type of intelligence in the early stages of a person’s life (Al-Ahdal, 2 009). It can be identified by the followings characteristics: a disposition to mu sic hearing and attraction to songs, a tendency to read music related topics, playing musical instruments, making musical com positions, writing songs, recognizing consistent and inconsistent sounds, memorizing more songs and melodies, self-singing wh ile doing tasks, easily memorizing melodies, listening to bird sounds, imitating soun ds, and a desire to let others listen to the person’s voice (Gardner, 2003; Afanan &Alkzindar, 2004). The educational practice and the day-to-day interaction between the teachers and students at different school levels are helpful in identifying the stude nts’ intelligence types. Other entities such as family members can also assist in identifying their interests and preferences. In the foll owing section, a discussion of some behavioral indications that can be used to identify intelligence types in the learn ers is presented. Such indication s may be helpful for the students to accomplish a fruitful and affective learning experience (Dobbs, 2001). A number of researchers have been interested in the degree to which mu sic aptitude or music experiences are related to academic achievement. Using data from first and fourth graders, Lamar (1989) found a significant and positive relationship between music aptitude and reading and one that approached significance for math ematics. Music aptitude was also high related with academic achievement in eigh t to 12-year-old students. A positive relationship was found for those high schools whose bands participated in festival concerts and SAT scores (Johnson, 2000). According to Luiz (2007) music improves the development of our brains and helps to improve our abilities in other subjects such as reading and mathematics. From simple sums to complex functions, mathematical concepts form part of the world of music. Because in this connection, it is possible to establish a positive correlation between participation performance in music and cognitive development in mathematics. Gar dner’s theory of multiple intelligences incited several researchers to re-examine the relationships between musical experiences, music learning, and academic achievement. The majority of studies have found that th e most significant relationships are between music and mathematics, or to be more specific, between music an d spatial-temporal reasoning (important in mathematical concepts), and music and performance in reading. With regard to the former relationship, the assumption is based on a group of studies which explore the effects of lear ning to play the keyboard on spatial-temporal reasoning, suggesting that mastering a musical instrument helps one to develop an understanding of Mathematics. According to Gouzouasis, Guhn & Kisho r (2007), who examined the relationship between particip ation and achievement in music and achievement in academic courses, based on data from three consecutive British Columbia student cohorts, it was consistently found that music participation was associated with generally higher academic achievement across the three cohorts. Many studies confirmed the effect of music on achievement. Khalil (2005), for example, showed that Mathematics scores improved for 6th, 7th, and 8th grades students learni ng to play musical instruments in Saudi Arabia. However, students with musical in telligence and who have inclination for music, possess thinking skills that differ from normal students (Hussein, 2008) . Christopher and Memmott (2006) demonstrated that involvement of students with musical intelligence in various musical instru ctional courses, playing music or even listening to music would improve their achievement compared with their peers who have similar characteristics but lack such musical intelligence (Babo, 2004). Further, musical intelligence not only improves musical achievement but would also have perceived effect on achievement in many subjects (Gouzouasis, Guhn & Kishor, 2007). Gouzouasis, Guhm & Kishor (2007) further indicated that time allotted for musical activities helps academic superiority. On the other hand, listening to music while learning is a contributing factor to academic superiority basically in artistic fields that improves thinking to high er and deepened levels (Gur, 2009). Music improv es brain functionality and intrinsic skills of learners primarily in literacy and Mathematics (Luiz, 2007). This result would be accounted for by the outstanding features of music that develop many developmental aspects of children including cognitive development as proven by the multi-intelligence theory. Reportedly, music improves literacy among children, provides a repertoire of lyrics and so ngs that imply meaningful educational content (Omari, Alhirsh, Aldalalah, & Al-Ababneh, 2010). Finally, there is a strong relationship between multi-intelligen ces and musical aptitude. This www.ccsenet.org/ijps International Journal of Psychological Studies Vol. 2, No. 2; December 2010 ISSN 1918-7211 E-ISSN 1918-722X 152 result received support from Chan (2007) who conducted a study with talented students in Hong Kong. It is therefore essential to pay greater attention to musical intelligence of students in classrooms when studying different subjects including a music class. 4. Cognitive Load Theory and Learning Cognitive Load Th eory (CLT) suggests that the in structional methods should be dynamic based on the cognitive load that is imposed on th e working memory (Jeroen, Enboer & Sweller, 2005). Therefore, CLT employs the connections between the information structures and the human cognitive knowledge to establish an instructional design to reduce the redundant or irrelevant cognitive load. The reduction of the irrelevant cognitive load considers the relations between working memory and long-term memory (Sweller, Paas & Renkl, 2003). CLT also has its methods of adapting and respo nding to the needs of individual learners (Van & Ayres, 2005). Moreover, to effectively utilize the limite d capacity of an individual’s working memory, the CLT offers principles and methods to design and deliver efficient in structional environments (Paas, Renkl & Sweller, 2003). The CLT also assumes that the human processing memory consists of multiple memory stores including a very limited working memory and an exte nsive long-term memory. The working memory is limited in capacity and in time when dealing with novel information (Mayer & Moreno, 2003). However, the limitations of the working memory make it difficult for the learner to understand multiple information elements simultaneously (Artino, 2008). There are three types of cognitive load: intrinsic, extraneous, and germ ane (Deleeuw & Mayer, 2008). Since, the cognitive load interactivity is intrinsic, then altering the interactivity by instructional interventions is not possible. Extraneous cognitive load suggests that an inappropriate instructional design that requires a considerable amount of working memory resources may impose a heavy cognitive load and thus interferes with the learning process (Sweller, 2004). However, the extraneous cognitive load is detrimental and can be controlled by the instructor. The third type of cognitive load is the germane cognitive load, which occurs when working memory resources are engaged with learning. Howev er, the germane cognitive load is also detrimental and can be controlled by the instructor (Toh, 2005). Therefore, Paas & Gog (2006) suggests that allowing the available working memory resources to be dedicated to the germane cognitive load as well as, reducing the extraneous cognitive load will significantly help in achieving effective learning. As a result, recently , the CLT has been employed as a framework for designing instructional procedures and materials for complex learn ing by reducing extraneous cognitive load and increasing germane cognitive load (Mayer & Moreno , 2003). Long-term memory is unlimited and holds a permanent record of everything the learner has acquired. The long-term me mory can hold all the knowledge which in turn can be processed as a single element by the working memory because all learning activities require the working-memory capacity. However, if the requ ired working-memory capacity goes beyond the learner’s limit, his learning or problem-solving performance will be affect ed, which is known as a cognitive overload (Jun-xia, 2007). On the other hand, a high cognitive load may occur when the learner’s attention is split (i.e. when a learner is required to process unnecessary information ) which is known as the split-attention effect (Mayer & Moreno, 1998). Therefore, reducing the cognitive load of the materials is the only way to maximize the students’ learning because the cognitive load is subcategorized into intrinsic cognitive load and extrinsic cognitive load (Jun-xia, 2007). 5. Methods & Procedures 5.1 Sample The population of this study comprised all third grade primary pupils (2263) enrolled in the ALKORAH educational directorate in Irbid Governorate in the second semester for the 2008/2009 academic year. There are 37 primary schools in the ALKORAH ed ucational directorate in Irbid Governorate. In order to implement this study in a naturalistic school setting, existing intact classes were used (O’deh and Malkawi, 1992). Pupils are from different towns within the ALKORAH Education Directorate. The popu lation of this study is representative of almost all the existing so cial classes in Jordan in terms of gender, age, nationa lity and native language. They are in the age group ranging from 8 to 9 years and of both gender. They are also homogenous in terms of their nationality, mother tongue (Arabic), exposure to English as a foreign language, and educational system and cultural background. Pupils in the selected schools – as well as all ALKORAH Government schools - were from approximately equivalent socioeconomic status as defined by the Ministry of Education of Jordan. They are from the low income group Figure 1. The sample consisted of 405 pupils who studied in third-grade classes and were randomly selected from six different primary co-educational schools. According to Gay and Airasian (2003) ‘’all the individuals in the defined population have equal and independent chance of being selected’’. The six schools www.ccsenet.org/ijps International Journal of Psychological Studies Vol. 2, No. 2; December 2010 Published by Canadian Center of Science and Education 153 were also randomly selected from the primary schools where music was taught in heterogeneous classes with no grouping or ability tracking. 5.2 Instruments The music achievement test: that was administered on the participants of the groups in this study is adapted from the music theory competency test developed by the researcher. The music theory competency test consisted of 15 recall (remembering) and 15 understand ing items. The duration of the achievement music test was 35 minutes. The achievement test comes in the following arrangement: The test is composed of two types of items. The two types of items are based on multiple-choice items for rememberin g and understanding that are specifically designed to assess learners’ music achievement. The stability or what is commonly known as test-retest method is considered the most suitable approach of measuring the reliability that guarantees tests consistency over time (Tuckman, 1999). Nevertheless, this type of reliability has a major problem as to the pe riod of time that should pass between the two testing sessions (Gay & Aira sian, 2003). The time that elapses between the two tests was four weeks in order to measure the reliability of the music achievemen t. The pre-test and post-test given to the sample were the same to maintain consisten cy. The Music Achievement Test contains 30 items used to evaluate the students’ ability to gain what they understood from the lessons and the topics covered in the music theory unit by reporting their learning achievement scores in the test. The reliability of the test questions was calculated using the Cronbach Alpha procedure to calculate th e internal consistency. The Cronbach Alpha of the test was 0.80, the internal consistency of the test was 0.93. The Discrimination Index values ranged from 0.45–0.98 and the difficulty values ranged from 0.31–0.66. The total score of the music achievement test is 30. Students received a score of “1” for a correct answer and a score of “0” for an incorrect answer or for the case of no answer on each item. The Music Intelligence Test: that was administered on the participants of the groups in this study was adapted from the music intelligence co mpetency scale developed by the researcher. The Music Intelligence Competency Test consisted of 10 items. It comes in the following arrangement: The scale is co mposed of two types of items. The two types are based on multiple-choice items for rhythm and tone that are specifically designed to assess learners’ music intelligence. The duration of the music intelligence test is 20 minut es. The total score of the music intelligence test is 10. Pupils received a score of “1” for a correct answer and a score of “0” for an incorrect answer or for the case of no answer on each item. Music Intelligence pupils were divided into two levels based on the music intelligence test scores : Low and High. The levels are identified based on Equation (1) below: Levels of Nnumber Mark Lowest - Mark Highest Z ……. Equation (1) where Hmin = Highest Mark Lmin = Lowest Mark Z = The difference between a level and the other. L = Low, H = high L (range) = [Lmin - Lmin + Z] H (range) = (Lmin + Z - Lmin + 2 Z] Highest Mark = 9, Lowest Mark = 1, Number of Levels = 2 4 2 1 - 9 Z L (range) = [1 - 5] H (range) = (6 - 9] The reliability coefficient of th is instrument was computed by the implementation of Cronbach Alpha whereby it was 0.85 for the whol e scale. The internal consistency in this instrument was 0.88. The Discrimination Index values ranged from 0.57–0.85 an d the difficulty values ranged from 0.35–0.56. Instruments Validity: Validity of the instruments are important aspects that should be taken into account when conducting a research. Validity consists of two different aspects that is face and content validity. According to Gay and Airasian (200) face validity relates to ‘’ the degree to which a test appears to measure what it claims to measure’’. Face validity was ju dged by a panel of experts in the field of education and music. Content valid ity refers to the ‘’degree to which a test measures an intended content www.ccsenet.org/ijps International Journal of Psychological Studies Vol. 2, No. 2; December 2010 ISSN 1918-7211 E-ISSN 1918-722X 154 area (Gay & Airasian, 200). Content validity of the instru ments in this research was justified by the panel. The feedback and comments received from the panel of experts were employed to establish the necessary clarifications, changes, and modifications before and after piloting the study. 5.3 Research Variables The present research contains three ty pes of variables (independent, dependent and moderating variables) that are presented as follows: o Independent Variable Music Intelligence Lev els (High, Low) o Dependent Variable Post Test Scores (learning) 6. Results The analyses of the collected data were carried out through various statistical techniques such as the ANCOVA. The data were compiled and analyzed using the Statistical Package for the Social Science (SPSS 16) for Windows computer software. 6.1 Measure of Relationship between Pre-test Scores and Post-test Scores Table 1 shows the degree of relationship between the pretest score and post-test score. A correlation coefficient of R = 0.627** indicates a high positive relationship between the two variables. 6.2 Testing the two groups' equivalence The purpose of the pre-experiment al study was to test the assumption that the participants across the groups were equivalent in their remembering and understanding of th e music theory unit for third grade primary pupils. To achieve this purpose, a pre-test that measures pre-music theory was conducted before the beginning of the study. To examine the equality of treatment mode on the pre-scores, the t-test procedure wa s used. The values p = .463 showed that there is no significant difference in the pre- test scores in the various groups. This means that the two groups have the same level of prior knowledge of the unit on music theory for third grade primary pupils. 6.3 Music Intel ligence Distributions In the final distribution based on th eir scores in the music intelligence test, the samples were divided into two groups: low music intelligence and high music intelligence of the music intelligence scale based upon a score of 1 mark per music intelligence test on the 10 items. The maximum score of the music intelligence test is 9. The mean score is M = 5. The distribution of the group is tabulated in Table 1. 6.4 Frequency Distribution of Music Intelligence Figure 2 shows the frequency distribution of Music Intelligence of the 405 pupils involved in the study. There were 245 pupils and 160 pupils fo r the low and high music intelligence groups respectively. 6.4 Testing Homogeneity The results from Levene's Test for homogeneity of variance by comparing the dependent variables across the two. As p > 0.05, the results show that the groups were homogenous. 6.5 Testing of Normality A skewness range and kurtosis range present ed values reveal that the variables are normally distributed and have met the criteria for further analysis. 6.6 Description of the Post-test Scores of Pupils with Different Levels of Music Intelligence (LMI & HMI) Comparison was made between the two groups – pupils with low music intelligence level and pupils with high music intelligence level (LMI & HMI) - based on the mean of the post-test scores using the descriptive procedure (Table 1). From Table 2 it can be seen that the post-test score mean ( M = 21.5813) for high mu sic intelligence group is higher than the post-test score mean ( M = 17.9755) for the low music intelligence group. 6.7 ANCOVA of the Post-test Scores of Pupils with Different Levels of Music Intelligence (L & H) In order to reduce the statistical error, the pre-test scores were used as the covariate, and a comparison was made among pupils with different levels of music in telligence (LMI & HMI) using the ANCOVA procedure (Table 3). www.ccsenet.org/ijps International Journal of Psychological Studies Vol. 2, No. 2; December 2010 Published by Canadian Center of Science and Education 155 The values F (1, 402) = 5.243, Mean Square = 95.349, and p = .023 show a significant difference between the post-test scores of pupils’ with different level s of music intelligence (LMI & HMI). 7. Discussion Results of the study show that hi gh music intelligence pupils (HMI) attained significantly higher post-test scores than Low music intelligence (LMI) pupils. Various studies confirmed a strong relationship between multiple intelligence theory and academic performance across various subjects (Dobbs, 2001; Afana & Khazendar, 2004). Hussein (2008) reported that high musical in telligent pupils seem to have better memory. Seemingly, this could be attributed to the larger mental capacity in the working memory for cognitive processing. Johnson & Memmott (2006) demonstrated that American students who attended training classes on music showed significant improvements in learning Mathematics a nd English. This result is consistent with Gouzouasis, Guhn, & Kishor’s (2007) who found that music trainees in Britain sh owed improvements in their other academic achievements. Luiz’s (2007) study showed that learning music improved the students’ achievements in Mathematics. Gur (2009) reported that classical music positively impr oved the cognitive process of Turkish pupils in performing drawing-tasks. Babo’s (2004) study found a positive relationship between middle schoo l students’ participation in music activities and academic ach ievements in language, literacy, and arts. However, Al-Darris (2008) found no effect of musical intelligence on learning among primary students with learning difficulties. Literature reviews on musical intelligence was not able to throw any light on the apparent positive correlation of musical intelligence or even the blending of music in improving learning. Chan’s (2007) study reported the positive correlation of musical intelligence to students’ attitude in music. The working memory refers to an information processing system that provides temporary storage and manipulation of th e information necessary for complex cognitive tasks in music theory learning. The working memory requires sim ultaneous storage and processing of information and is therefore very important for processing musi cal theory by the pupils. In other words, pupils with a poor working memory capacity may have delayed learning of music th eory. From this study, pupils with high music intelligence seem to have a larger capacity in their working memory and hence could process and realign information better and that helps maintain information retention and storage in the long-term memory. Conclusion Apparently, high music intelligence pup ils have larger capacity in their working memory to accommodate and integrate incoming information. Hence the performan ces of the high music intelligence pupils are better. The researchers were not able justify for such a phenomenon and strongly recommend that more studies be conducted to determine other instructional designs that might help the “disadvantaged” low music intelligence pupils. References Afanan, I., & Akzndar, N. (2004). Multi intelligence levels among basic education students stage in Gaza and their relationship to achievement in ma thematics and orientation towards. Humanists Studies Journal, 12 (2), 323-366. Al-Ahdal, A. (2009). The efficiency of activities and approaches based on the multi-intelligence theory in improving the achievement of Geography and maintaining the learning effect among first year secondary students in Jeddah Province. Journal of Science Education and Human, 1 (1), 191-242. Aldalalah, O. M. (2003). Educational software effects in learning musical concepts for class rooms teachers, students and their attitudes toward them. Master Thesis (Unpublished), Yarmouk University, Jordan. Al-Drais, S. (2008). Multiple intelligences assist pr imary students who have academic difficulties . Master Thesis (Unpublished), Kuwait University. Artino, A. R. (2008). Cognitive load theory and the role of learner experience: An abbreviated review for educational practitioners. AACE Journal, 16(4), 425-439. Babo, D. G. (2004). The relationship between instru mental music participation and standardized assessment achievement of middle school students. Research Studies in Music Education, 22 (1), 14-27. Chan, W. D. (2007). Musical aptitude and multiple intelligences among Chinese gifted students in Hong Kong: Do self-perceptions predict abilities?. Personality and Individual Differences, 43 (6), 1604–1615. Chew, D. (2005). Computer-assisted instruction for music theory education: Rhythm in music. PhD Thesis (Unpublished), Californ ia state university. Christopher, J. M., & Memmott, J. E. (2006). Examination of relationships between participation in school music programs of differing quality and standardized test results. Journal of Research in Music Education, 54 (4), 293-307. www.ccsenet.org/ijps International Journal of Psychological Studies Vol. 2, No. 2; December 2010 ISSN 1918-7211 E-ISSN 1918-722X 156 Deleeuw, K. E., & Mayer, R. E. (2008). 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(2008). Pattern of Thinking Music Students. PhD Thesis (Unpublished), Gulf University. Jeroen. J., Enboer, V., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17 (2), 147-177. Johnson, D. A. (2000). The developm ent of music aptitude and effects on scholastic achievement of 8 to 12 year olds. Ph.D Thesis (Unpub lished), Kentucky: University of Louisville. Jun-Xia, G. (2007). Action research: The application of cognitive load theory to reading teaching. Sino-US English Teaching, 4 (4), 19-23. Khalil, A. (2005). Science of creativity [Online] Available: http://www.adabfalasteeni.org/artical.php. (Accessed 3 April 2009). Lamar, H. B. (1989). An examination of congruency of musical aptitude scores and mathematics and reading achievement scores of elementary children . Ph.D Thesis (Unpublished), University of Southern Mississippi. Luiz, C. S. (2007). The learning of music as a means to improve mathematical skills. International Symposium on Performance Science Published by the AECAll rights reserved. Mayer, R. E., & Moreno, R. (1998a). A split-attention effect in multimedia learning: evidence for dual processing system in working memory. Journal of educational psychology, 90 (2) 312- 320. Mayer, R. E., & Moreno, R. (2003). Nin e ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38 (1), 43–52. Nosir, S. (1980). Music theory. Egypt: Dar Alsabeel. Omari, M., Alhersh, A., Aldalalah, O., & Ababneh, Z. (2010). Effect drill and practice pattern in mathematics among first-grade students, compared with music anthems, and traditional way. Journal of Science Education and Human (Umm al-Qura University). 21 (1). (in pres) Otoum, A. (2004). Cognitive psychology . Amman: Dar Almaserh. Paas, F. & Gog, T. (2006). Optimising worked example in struction: Different ways to increase germane cognitive load. Learning and Instruction, 16 (2), 87-91. www.ccsenet.org/ijps International Journal of Psychological Studies Vol. 2, No. 2; December 2010 Published by Canadian Center of Science and Education 157 Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: recent developments. Educational Psychologist, 38 (1), 1-4. Qtami, U. (2005). Educational psychology and thinking . Jordan: Dar Hanen. Smith, K. H. (2009). The effect of computer-assisted instruction and field independence on the development of rhythm sight-reading skills of middl e school instrumental students. International Journal of Music Education, 27 (1), 59-68. Sweller, J. (2004). Instructional design consequences of an analogy between evolution by natural selection and human cognitive architecture. Instructional Science, 32(1), 9–31. Sweller, J., Paas, F., & Renkl, A. (2003). Cognitiv e load theory and instructional design: recent developments. Educational Psychologist, 38 (1), 1-4 Toh, C. S. (2005). Recent advances in cognitive load theory research: implication for instructional designers. Malaysian Online of Journal of Instructional Technology (mojtt), 2 (3), 106-117. Van, J., & Ayres, P. (2005). Research on cognitive load theory and its design implications for E-learning. Educational Technology, Research and Development, 53 (3), 5-13. Table 1. Distribution of Music Intelligence Group Levels of Musical IQ Frequency Percent Low 245 60.5 High 160 39.5 Total 405 100.0 Table 2. Post-test Scores of Pupils with Different Levels of Music Intelligence (LMI & HMI) Music Intelligence Mean Std. Deviation N Low 17.9755 5.18899 245 High 21.5813 5.24191 160 Total 19.4000 5.49464 405 Table 3. ANCOVA of the Post-test Scores of Pupils with Different Levels of Music Intelligence (LMI & HMI) Source Type III Sum of Squares df Mean Square F Sig. Corrected Model 4886.354a 2 2443.177 134.342 .000 Intercept 689.686 1 689.686 37.924 .000 pre-test 3627.951 1 3627.951 199.489 .000 Music Intelligence 95.349 1 95.349 5.243 .023 Error 7310.846 402 18.186 Total 164623.000 405 Corrected Total 12197.200 404 www.ccsenet.org/ijps International Journal of Psychological Studies Vol. 2, No. 2; December 2010 ISSN 1918-7211 E-ISSN 1918-722X 158 Figure 1. Educational Director ates in Irbid Governorate Figure 2. Frequency Distributi on of Music Intelligence

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