Factors Mining in Engaging Students Learning Styles Using Exploratory Factor Analysis

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Procedia Economics and Finance 31 ( 2015 ) 722 – 729

Peer-review under responsibility of Universiti Teknologi MARA Johor doi: 10.1016/S2212-5671(15)01161-2

ScienceDirect

INTERNATIONAL ACCOUNTING AND BUSINESS CONFERENCE 2015, IABC 2015

Factors Mining in Engaging Students Learning Styles Using

Exploratory Factor Analysis

NurIzzahJamil

a

_{, Farrah Nadia Baharuddin}

b*

_{and TengkuSharifeleaniRatulMaknu}

b

a_{Faculty of Computer and Mathematical Sciences, UniversitiTeknologi MARA Negeri Sembilan,72000 Kuala Pilah, Negeri Sembilan, Malaysia} b,c_{Faculty of Business Management,UniversitiTeknologi MARA Negeri Sembilan,72000 Kuala Pilah,Negeri Sembilan}_{, Malaysia}

Abstract

A number of studies have been conducted to investigate the underlying causes of effective learning through learning styles that may help to improve performance and achievement in the classrooms. Due to its importance, assessing learning styles in the academic fields have been the main focus for future research. Thus, it is paramount to study the preferences of learning styles that create effective learners. Exploratory factor analysis has been used in this study for statistical purposes. Therefore, it is hope that this study could help to produce more students with better academic performance and reduce the failure rate. This study is a preliminary research to assess the learning key factors (LKF) among UniversitiTeknologi MARA students who took Statistics subject. Forty nine (49) respondents were selected using stratum proportion and they were then selected from each stratum using simple random sampling. The exploratory factor analysis of this research suggests that there are five learning key factors (LKF) which account for 67.404% of the total variance with considerably reduce the complexity of the data set by using these components with 33% loss of information. Kaiser-Meyer-Olkin value is 0.621 and small values of the significance level of Bartlett's test of sphericity (0.000) indicate factor analysis is feasible for this data set. Varimax rotation with Kaiser normalization was performed and five factors solution was revealed labelled as attention and concentrating, visual learners, audio learners, kinaesthetic learners and cognitive factors. Main findings suggested that the result of 15-items scale was much more reliable instruments than the initial 27-items scale with Cronbach’s alpha correlation coefficients of 0.735.

Peer-review under responsibility of UniversitiTeknologi MARA Johor.

Keywords:Exploratory factor analysis ; Cronbach’s alpha ; Kaiser-Meyer-Oikin ; Bartlett’s test ; Varimax rotation, ; Learning key factors (LKF).

* Corresponding author. Tel: +606-4832155 E-mail address:[email protected]

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1. Introduction

Education is a very interesting topic for research because it has great importance in developing human capital and knowledgeable workforce. In order to achieve a noteworthy human capital and knowledgeable workforce, an assessment of teaching methods in creating effective learners should be effectively implemented. Due to its importance, numerous studies have been carried out to investigate the effective teaching methods as well as effective learning from the perspective of researchers and teachers (Ramsdon, 2003; Wilkin, 1995).

According to Kolb Experiential Learning Theory (Kolb,1984) defines learning “as a process whereby knowledge is created through the transformation of experience”. Dunn (1990) defines learning styles as “the way in which the individuals begin to concentrate on, process, internalize and retain new and difficult information.

It is believed that teaching can facilitate in effective learning as such teachers need to know on learners’ learning styles (Zhao & Ting, 2013). Teachers’ teaching methods should suit with learners’ learning styles. Thus, it is vital for teachers to be aware of the learning styles and understand the importance of learning styles based on subject as well as discipline and it can be changed accordance to learning activities and situation (Fatt, 1993). According to (Sternberg & Zhang, 2010) psychologists and educators also believed that academic success and failures of an individual is depended and supported by the differences in abilities.

The mismatched of teaching styles and learning styles could be disadvantages to effective learners (Pask, 1977). Therefore, teachers should understand different types of learning styles that suit their students. The learning activities should be more enjoyable and comfortable with learners’ preferred learning styles in contrast with learners forced to adapt with teachers’ teaching styles.

2. Literature Review 2.1. Learning Styles

Learning styles are defined as characteristic of cognitive, affective as well as psychological behaviours that relates on perception of learning activities, interaction and respond to learning situation (National Association of Secondary School Principals, 1979). Each of learners has their own preferred learning styles and learners could adapt learning styles according to tasks given. Adaptation of different learning styles is referred as a versatile learning style (Pask, 1977).

Learning style is defined as a psychological theory (Smith et al., 1989) and its result to determine of educational achievement (Dunn and Dunn, 1993). “Learning style is the way in which learner begins to concentrates on,

process, and retain new and difficult information” (Dunn & Dunn, 1993).Learning also defined as a circular process

that is viewed as a series of experiences with cognitive attributes namely concrete experience, reflection and observation, abstract concepts and generalizations as well as active experimentation (Kolb, 1984). There are four basic of learning styles, namely, theorist, reflector, pragmatist and activist (Honey and Mumford, 1986). A theorist is an analytic learner, a reflector is an imaginative learner, a pragmatist is a common-sense learner and an activist is a dynamic learner. A learner can choose any preferred learning style and match it with other learning styles at different times and different situation.

The accuratematch on teaching styles and learning styles encourage students’ learning activities and process (Abbas, 2012). The mismatch of teaching as well as learning styles would have affected students’ motivation and their attitudes toward learning activities. Both teachers and students should understand their own styles and match it well (Reid, 1995 and Oxford et al., 1992). As such, teachers should understand their students’ learning styles and match the teaching styles which best suiteach of their students’ preferred learning styles (Sprenger, 2003).

Visual, auditory and kinaesthetic (VAK) learning styles model consists of three main learning styles, namely, visual learning styles, auditory learning styles as well as kinaesthetic learning styles (Chislet, and Chapman, 2005). Visual learners are people with preference using sight in learning, auditory learners love to use hearing sense in learning, kinaesthetic learners love to use body movement and touch in learning (Sousa, 2006).

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2.2. Visual Learners

There are two categories of visual learners that are visual-linguistic learners and visual-spatial learners. The visual-linguistic learners prefer to learn through written language while visual-spatial learners prefer to learn through demonstrations, charts, videos and other visual materials (Clark, 2000).

2.3. Auditory Learners

Auditory learners prefer to talk and move their lips while reading (Clark, 2000). Students prefer to talk into tape recorder or another person as compared to reading and writing tasks. Students in this category are most effective in learning when using both of speaking and hearing activities (Philp, 2008).

An auditory learners work best on things done through listening activities,such as audio sound, spoken words, noises and so forth

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2.4. Kinesthetic Learners

Kinaesthetic learners best learn through physical experiences such as hand-on training, doing, touching, holding and so on (Clark, 2000). There are two categories of kinaesthetic learners, known as, kinaesthetic (movement) as well as tactile (touch). The best way on learning for this type of learners is when physical activities involved with the environment, namely, performing demonstrations and playing games (Philp, 2008).

A kinaesthetic learners work best on things done through physical experiences such as feeling, hands-on experiences, doing, touching, holding and so on.

2.5. Cognitive Factors

Cognitive word will associate with brain and the way how a person thinks. It usually involves with physical activity simultaneously using the mental ability in making decision. According to Messick (1984) cognitive style is described as ``consistent individual differences in preferred ways of organizing and processing information and experience''. It usually relates with ability of thinking, memorizing and how to solve a problem.

Vast research carried out on cognitive have brought a similar understanding that is “cognitive style differences influence learning, problem solving, decision making, communication, interpersonal functioning, and creativity in important ways” (Hayes and Allinson, 1994; Kirton, 2003; Sadler-Smith, 1998). Sternberg and Grigorenko (1997) stated that it is ``a bridge between what might seem to be two fairly distinct areas of psychological investigation: cognition and personality''. Cognitive style as “an individual’s preferred and habitual approach to organise and represent information” Riding and Rayner (2000).

3. Methodology

A total of 49 diploma students from UniversitiTeknologi MARA, Seremban 3 Campus who enrolled for statistics subject were selected as the respondents. A set of self-administered questionnaires were distributed in class during learning session and the data was gathered.

Sample size of this study was determined through the use of an approach of Krejcie& Morgan (1970). The Table for Determining Sample Size from a given population was used to get the total sample size required for this study. There were three (3) groups available for statistics subject that is Group 1, group 2 and group 3. Each group consists of 29, 27 and 29 students respectively.

A sample size of 70 from a population of 85 elements was used for this study by using stratum proportion (also known as proportional allocation). Representatives were chosen from each stratum using simple random sampling. Out of Seventy (70) questionnaires distributed only 49 questionnaires were returned, resulting in 70% response rate (Table 1). Sample size: N1 = 29, N2 = 27 and N3 = 29

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Table 1.Number of respondent selected to participate in the study Class N n A 29 24 B 27 22 C 29 24 Total 85 70 N: Population

n: Sample size required

Various techniques exist for finding factors. Some commonly used is factor analysis. The advantages of factor analysis are reduction of number of variables, by combining two or more variables into a single factor or more factors. Factors generated consist of variables that are highly correlated among them. Figure below portrays the framework of this study.

Figure 1.Theoretical Framework of learning key factors (LKF)

The preliminary analysis consists of reliability analysis (Cronbach’s alpha), communalities, KMO and Barlett’s test. Internal consistency coefficients (Cronbach’s alpha) were required to assess the reliability of the scale. This study employed the use of reliability analysis in determining the retention or removal of items on the scale. In addition, any item with small value of extraction communalities was marked as a component to be deleted. As for the result, the Cronbach’s alpha coefficient of the scale increased when such items were deleted. KMO and Barlett’s test were employed to indicate whether factor analysis is feasible or not. Thus, the higher the value of KMO and small values of the significance level of Bartlett's testsphericity indicate that the factor analysis is feasible.

Eigenvalues was more than 1 considered as acceptable and cumulative amount of score variance for different factors was performed for the factor extraction. It was used to determine the optimal number of components useful to describe the data. The higher the cumulative amount of score variance, the less information will be missed out

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(lost). For the next stage, Principal Component Analysis with Varimax rotation was performed for the purposes of obtaining simple and interpretable factors. Finally, factor identification and labeling based on the higher loading factor for each component were also utilized in this study.

4. Analysis and Results

4.1. Demographic profile

Respondents were diploma students of UniversitiTeknologi MARA, Seremban 3 Campus. 80% of the respondents of this study (N=49) were female and 98% of the participants stayed in hostel. 59% of the participants’ parents income are more than RM2, 000 per month and the remaining 41% with the parents’ income of less than RM2, 000 monthly.

The pie charts in Figure 2 and Figure 3 below show that there were an increased percentage of 54% to 61% of students who preferred mathematics or statistics subject during universities as compared to school days.

Figure 2. At schools Figure 3. At universities

The survey questions consist of two sections; demographic profile and 27-item scale were measured on a five-point Likert scale from “strongly disagree” to “strongly agree”.

4.2. Reliability Test

This study used Cronbach’s alpha to measure reliability and the result was 0.735, as depicted in Table 2, suggesting that the items have relatively internal consistency. Therefore, the responded (selected) samples are suitable for the purpose of identifying the learning key factors (LKF).

Extraction communalities are estimated of the variance in each variable accounted for by the factors in the factor solution. Small values indicate variables that do not fit well with the factor solution, and should possibly be marked as a component to be deleted and dropped from the analysis.

Table 2.The summary of 3 runs of component of Learning Key Factors (LKF)

Items Number of Item

Removed Cronbach’s Alpha KMO

*27 7 0.673 0.311

20 5 0.674 0.463

15 0 0.735 0.621

Based on Table 2, the results of 3 runs of reliability analysis showed that 13 items were removed by considering the cronbach’s alpha, KMO and Bartlett’s test value.

Less like 12% Like 54% Most like 34% Like 39% Most like 61%

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Table 3.KMO and Bartlett’s Test

Kaiser-Meyer-Olkin Measure of SamplingAdequacy .621

Bartlett's Test of Sphericity Approx. Chi-Square 192.936

Degree of Freedom 105 Significance .000 Table 4.Communalities Components Extraction 1 .694 2 .755 3 .687 4 .580 5 .695 6 .522 7 .596 8 .478 9 .758 10 .676 11 .767 12 .768 13 .618 14 .769 15 .748

Based on Table 3, Kaiser-Meyer-Olkin value was 0.621 and small values of the significance level of Bartlett’s test of sphericityindicate factor analysis was feasible. The extraction communalities for this data set wasalso acceptable (Table 4).

Table 5.Eigenvalues

Component Total Percentage of variance Cumulative percentage

1 3.781 25.206 25.206

2 2.112 14.079 39.285

3 1.652 11.014 50.299

4 1.472 9.810 60.109

5 1.094 7.295 67.404

Extraction Method: Principal Component Analysis

Refer to Table 5, five (5) factors were extracted since the eigenvalue > 1. The first eigenvalue was equal to 3.781, and corresponds to 25.206% of the variance in the original data. The second eigenvalue 2.112, corresponding to the second factor, is associated with 14.079% of the variance in the original data. The next eigenvalues are 1.652, 1.472, 1.094 respectively, corresponding to the total of 67.404% of the variance in the original data. Therefore, the cumulative percentage of the total variance explained by the factors extracted was 67.404% with considerably reduced the complexity of the data set by using these components with 33% lost of information.

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Table 6.Rotated Component Matrix

Items Component

1 2 3 4 5

I need to see the teacher's body language and facial expression to fully understand the

content of a lesson .637 .394

-.291 .116 -.188 I tend to prefer sitting at the front of the classroom to avoid visual obstructions

.844 -.080

-.060 .008 .180 I’m prefer to take detailed notes to absorb the information and to review lateragain

.759 .189 .235 -.048

-.134 I often would rather listen to a lecture than read the material in a textbook

.515 .149 .340 .229 -.149 I learn best from visual displays including: diagrams, illustrated text books

.118 .712

-.008 .009 -.023 I frequently require explanations of diagrams, graphs, or maps

-.009 .841 .015 .141 .176 I typically prefer information to be presented visually, (e.g. Flipcharts or chalkboard) .366 .643 .369 .102 .271 I often prefer to listen to the radio than read a newspaper

-.027

-.166 .799 .074 -.054 I typically follow oral instructions better than written ones

.118 .311 .807

-.054 .059 I learn best through discussions, talking things through and listening to what others have

to say .229

-.024 .334 .696 .217 I am verbally articulate and enjoy participating in discussions or classroom debates

-.145 .043

-.184 .621 .571 I learn best through a hands-on approach .345 .231 .340 .526 .123 I need to actively participate in an activity to learn how to do it

-.066 .109

-.144 .828 -.159 I feel the best way to remember something is to picture it in my head .197 .079 .172 .137 .709 I am excellent at finding my way around even in unfamiliar surroundings

-.265 .133 -.117

-.090 .713 Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

Principal Component Analysis with Varimax rotation that produced the final 15-components suggested five learning key factors (LKF) as shown in Table 6. Four items that loaded onto Factor 1 were labeled as “Attention and concentrating”. Three items that were loaded onto Factor 2 were labeled as “Visual learners”. Two items that were loaded onto Factor 3 were labeled as “Audio learners”. Four items that were loaded onto Factor 4 were labeled as “Kinesthetic learners”. Two items that were loaded onto Factor 5 were labeled as “Cognitive”.

5. Conclusion

The exploratory factor analysis of this research suggests that there are five learning key factors (LKF) which account for 67.404% of the total variance with considerably reduced the complexity of the data set by using these components with 33% loss of information. Kaiser-Meyer-Olkin value is 0.621 and small values of the significance level of Bartlett's test of sphericity (0.000) indicate that factor analysis is feasible for this data set. Varimax rotation with Kaiser normalization was performed and five factors solution was revealed labeled as attention and concentrating, visual learners, audio learners, kinesthetic learners and cognitive factor. Main findings suggest that the resulting of 15-items scale is much more reliable instrument than the initial 27-items scale with Cronbach’s

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alpha correlation coefficients of 0.735. As a conclusion, it is important for teachers to be aware of the learning styles and understand the importance of learning styles based on their subject, thus learning activities could be more enjoyable and comfortable.

Acknowledgements

The authors would like to thanks and acknowledge UiTM for financial support of this research work.

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