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IDENTIFYING PREDOMINANT INTELLIGENCE AMONG PRE-
134 21st century, a significant number of Malaysian students do not meet the minimum proficiency standards required to produce globally competitive citizens, as English language results are the lowest of the core subjects at UPSR, PMR, and SPM levels [4]. Many comprehensive plans have been rolled out to transform the education system in ensuring every child is proficient in English, however, the operational proficiency in this language is still low. Most of the Malaysian students are constantly being challenged with poor English acquisition despite years of learning it in schools, with only 28%
of the students achieved a minimum credit in the 2011 SPM English paper against Cambridge 1119 standards [4]. This issue becomes worsened at the tertiary levels because the educators failed to address the language issues in secondary schools. Poor English proficiency among fresh graduates, since 2006, has been consistently ranked as one of the top five issues facing Malaysian employers [4]. This inability to use the language effectively is manifested by the fact that 48 per cent of employers reject students due to poor English [8].
The unchanged medium of instruction in the classrooms is one of the major contributing factors to the gradual decline in the standard of English of this country. In his inaugural lecture, Professor Datuk Dr Mohamed Amin Embi, the Director of Universiti Kebangsaan Malaysia (UKM) Centre for Teaching and Technologies said the problem with today’s education – at the tertiary levels – is that most educators teach the way they were taught in the past [8]. They could hardly escape the traditional mould of teaching and the familiar practice of spoon-feeding students with all the content [2].
Professor Dr Hairul Nizam Ismail, the Dean of School of Educational Studies of Universiti Sains Malaysia (USM) also adds that the Malaysian tertiary institutions still predominantly adopt the
“factory line” concept of teaching and learning where many university lecturers prefer the teacher- centred approach that emphasises delivering lectures during the duration of the class, while students listen passively in their seats [8]. The value of learning relies merely on securing excellent grades in language assessments and the conventional teaching pedagogy become the primary approach in teaching while students of this age need a newer platform with a different learning strategy [5].
Declining trends in the students’ achievement in many language areas show that the traditional practices are no longer suitable to the Gen Z- those born between 1996 and 2009 – who would prefer a wider and freer sense of expressions and to learn following their learning styles and pace.
As we enter the new education era, differentiation in classroom instructions has become enormously important in the process of teaching and learning in English classrooms [3]. Yet, many teachers fail to acknowledge the students’ differences and restrict themselves to teaching students with the only one-size-fits-all approach. This is probably due to the educators nowadays are still heavily inclined on the linguistic and mathematical intelligences, and students with this set of intelligence as their domains always excel when it comes to the examination. Those with different abilities and intelligence are often viewed as poor achievers. Given this landscape, educators need to be aware that every learner has different potential and abilities in acquiring new knowledge. Through Multiple Intelligence (MI) proposed by Gardner, teachers could identify the students’ domains and preferred learning styles so they can exploit the strength or intelligence of these students to learn the language more effectively. Acknowledging the differences in intellectual ability would also drive educators to change their strategies in teaching English by including different intelligences, thus enhancing the students’ language skills.
Gardner’s theory of Multiple Intelligences discusses nine different types of intelligences. Those nine domains are Bodily-Kinesthetic, Intrapersonal, Interpersonal, Verbal-Linguistic, Logical- Mathematical, Musical, Visual-Spatial, Naturalist, and Existential intelligences. This theory has helped educators to appreciate students’ abilities in learning by acknowledging their differences by planning out lessons that tap on each and everyone’s strengths and compensate for their areas of
135 weakness while learning using Multiple Intelligences [1]. By teaching the students according to their predominant intelligence, achieving the objectives in a lesson would be maximised because the students will be potentially more receptive to the teaching strategies and mode of presentations.
Although there have been numerous studies keenly interested in studying the cause-and-effect relationship between Multiple Intelligences and language skills development, only a few were conducted to investigate the predominant intelligence among the low-achieving students. This study attempts to extend our knowledge in terms of finding the significant domain of the low achievers, as to provide a foundation in understanding which approaches these students learn best. The finding would be served as an eye-opener for educators to reach weaker students more effectively as it focuses on identifying and helping these students to learn through their strengths. The main purpose of this study is therefore to identify which intelligence is significantly associated with the low English proficiency levels by identifying the predominant intelligence of the low-achieving students.
RESEARCH METHODOLOGY Sample Selection
Sixty pre-university students of the Science Foundation Programme participated in this study.Criteria for selecting the subjects were as follows:
a) Pre-university students studying at the Preparatory Centre of Science and Technology, Universiti Malaysia Sabah; and
b) Enrolled in UB0013 The Foundation of English I course.
Research Instruments and Data Collection Procedures
The main aim of this study is to determine which intelligence is predominated by the low- achieving students, thus subsequently provides a leeway for the educators to reach weaker students more effectively as it focuses on identifying and helping these students to learn the language through their strengths. The performance measure (Paper 800/4 Writing of MUET March 2016) was used to determine achievements through grades displayed by the students, while the Multiple Intelligence Profiling Questionnaire III (MIPQIII) was used for the data collection.
Data Collection
The Multiple Intelligences Profiling Questionnaire III or MIPQ III was specifically adapted to suit the need of answering the research question of this study. The MIPQ III is the latest six-point Likert scale self-rating questionnaire that is based on Howard Gardner’s Multiple Intelligence (MI) theory. It was profoundly developed and refined by Kirsi Tirri and Petri Nokelainen in 2007 with the addition of the ninth dimension of intelligence compared to the first and second version with only seven and eight intelligence dimensions [9]. This inventory contains 35 items, aimed to identify the students’ predominant intelligence. Each participant’s answers to each item in the MIPQ III were logged into the Statistical Package for the Social Sciences (SPSS) version 21.0 to generate the most significant intelligence of the targetted group. Alongside the grades obtained through their performance in the MUET Paper 800/4 Writing, a Correlation Analysis was conducted on these data to identify the predominant intelligence of the students, thus the intelligence which is predominated by the low-achieving students could be identified.
136 RESULTS AND DISCUSSION
The participating students had to be classified into two categories: high and low achievers. There is no rigid system on student’s classification into groups of proficiencies, but it could be done by considering the ±0.5 standard deviation on the scores obtained. To answer this research question, the high and low-achieving students were identified through their writing performance in the test. The group was categorized as the low achievers if the test scores achieved 22.7167 below the mean, whereas the students grouped in the high achievers’ category would score more than 22.7167 above the mean scores. By using the Descriptive Analysis, Table 1 explains the classification of students into groups of high and low achievers using the marks of the test.
Table 1. Classification of High and Low achievers Descriptive Statistics
N Minimum Maximum Mean Std.
Deviation Writing mark 60 11.00 34.00 22.7167 5.06932
Valid N (listwise)
60
High achievers: scores > mean =22.7167 / Low achievers: scores < mean = 22.7167
Before the attempt in answering this research question, the students with scores of 22.7167 below the mean marks were identified as low-achieving students. Of the 60 sample population, 33 of them belonged to the low achievers. To determine which type of intelligence is predominated by these students, each of the student’s significant intelligence was analysed. A Correlation Analysis was performed on them to describe the strength and direction of the linear relationship between the two variables through the Pearson correlation coefficient (r). It was also used to confirm any significant difference between the two variables by considering the data for p (two-tailed) values. The procedure of obtaining and interpreting a Pearson product-moment correlation coefficient (r) was presented along with the Spearman Rank Ordering Correlation (rho). Pearson r was designed and used for two interval level (continuous) variables, or one continuous (e.g. scores of the test) and the other dichotomous (e.g. sex: Male/Female). Since the data did not meet the criteria for the Pearson correlation, Spearman rho was considered to interpret the ordinal data (The accumulated scores of each intelligence was determined through the Six-Point Likert Scale. These scales of the MIPQ III were technically ordinal in which they consisted of a series of ordered categories. Thus, this variable was not continuous and did not meet the criteria of r. The analysis was done solely to find a particular intelligence or domain which predominates the low-achieving students. The result of the Correlational Analysis is shown in Table 2.
137 Table 2. The Correlation Between Intelligences and the Writing Performance Among Low
Achievers.
Writing mark (Low
Achievers)
Spearman's rho
Mean_L Correlation Coefficient -.692
Sig. (2-tailed) .072
N 33
Mean_LO Correlation Coefficient .653
Sig. (2-tailed) .081
N 33
Mean_SPA Correlation Coefficient .653
Sig. (2-tailed) .081
N 33
Mean_BK Correlation Coefficient -.426
Sig. (2-tailed) .143
N 33
Mean_M Correlation Coefficient .775
Sig. (2-tailed) .052
N 33
Mean_INT ER
Correlation Coefficient
.983 Sig. (2-tailed) .004
N 33
Mean_INTR A
Correlation Coefficient .508
Sig. (2-tailed) .119
N 33
Mean_EX Correlation Coefficient -.172
Sig. (2-tailed) .244
N 33
Mean_E
Correlation Coefficient -.853 Sig. (2-tailed)
N
.034 33 c. Listwise N = 33
The relationship strength could be determined by considering the size of the value of the correlation coefficient of each intelligence tested. The range from -1 to 1 would indicate the strength of the relationship between the two variables. A correlation of 0 indicates no relationship at all, a correlation of 1 indicates a perfect positive correlation, and a value of -1 indicates a perfect negative correlation [6]. Cohen (1988, pp. 79-81, as cited in [6]), suggest the following guidelines to interpret the values between 0 and 1:
Table 3. The Interpretation of the Relationship Strength Between the Two Variables Value of r Relationship Between Two Variables
r=.10 to .29 Small
r=.30 to .49 Medium
r=.50 to 1.0 Large
These guidelines apply whether or not there is a negative sign out the front of the r or correlation
138 coefficient value. The negative sign refers only to the direction of the relationship, not the strength.
The strength of the correlation between r=.5 and r=-.5 is the same. It is only in a different direction [6]. In the data presented in Table 2, the largest correlation exists between the Interpersonal intelligence and the test scores with its r=0.983, which is near to 1.0, suggesting a strong relationship between the Interpersonal domain and test scores.
The significance level (Sig. 2 tailed) was also being considered in this discussion. The level of statistical significance does not indicate how strongly the two variables are associated, but instead, it indicates how much confidence we should have in the results obtained [6]. In the data presented above, the p values (Sig. 2 tailed) for Interpersonal intelligence was lesser than the traditional p<.05 level at 0.004, suggesting that there is indeed a major difference exists between the two variables tested. Given that the p-value is less than .05, we can conclude that there is a statistically significant difference in the strength of the correlation between Interpersonal intelligence and the test scores of the low achievers. The result presented in Table 3 also indicates that the low-achieving students appear to be more interpersonally-inclined based on its correlation coefficient, r and statistical significance p (two-tailed), indicating that this intelligence should be highly considered when planning language activities to encourage weaker students to learn better.
This finding reveals that low achievers have high Interpersonal intelligence (People Smart), indicating that the struggling writers may be more intelligent ‘interpersonally’. Those who have strong Interpersonal intelligence are good at understanding and interacting with other people. They communicate effectively and enjoy participating in discussions and debates. Considering the outcomes of this study, their lecturers can extend their teaching repertoire to honour this intelligence, by introducing new topics/knowledge through the interpersonal lens. They learn more effectively if they are taught based on their particular strength. For example, educators can help these low achievers enhance and strengthen their language skills by asking them to work in cooperative groups to design and complete projects, work in pairs to solve problems or interview people with knowledge about content-area topics. Identifying the students’ cognitive domain could be an excellent reference for educators to start acknowledging this intelligence to improve their language skills. The point here is not that educators should only teach according to their predominant intelligence, but this offers an alternative for the educators to integrate Multiple Intelligences in the classroom by putting more emphasis on the students’ significant domain to help them learn more effectively.
CONCLUSION
In summary, Interpersonal intelligence was identified as predominant intelligence for low- achieving students of the Preparatory Centre for Science and Technology, UMS. Although it was considered as a small-scale study, this finding is hoped to inspire educators to start acknowledging the students’ differences in cognitive abilities and tailoring differentiated teaching approaches in a way that best suit their unique needs.
ACKNOWLEDGEMENT
I would like to thank Dr Asmaa AlSaqqaf and Associate Professor Dr Wardatul Akmam Din who have given me invaluable assistance and guidance. I appreciate the constant encouragement and constructive feedback you have invested in me in producing this article.