i r b a s international r b a s

ISSN: 2308-7056 Fida, Hussain, Ullah & Salam (2021)

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www.irbas.academyirmbr.com July 2021

International Review of Basic and Applied Sciences Vol. 9 Issue.3

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Indexing of Learning Styles: A Case of the University Students

Across Faculties

Dr. ASFANDEYAR FIDA School & Literacy Department, Government of Khyber Pakhtunkhwa.

Email: [email protected]

KAUSAR HUSSAIN Lecturer, Department of Education, University of Chitral.


SAIF ULLAH M. Phil Graduate of Department of Education, AWKUM.


ABDUL SALAM M. Phil Graduate of Department of Education, AWKUM.


Abstract

Learning style is an imperative construct of learning and achievement. These days, it is highly focused by

researchers but in Pakistan there is scare research on learning styles. This study is an endeavor to probe

the distributions of learning styles in the local scenario. The aim of this investigation was to identify the

spread of learning styles of the university students across disciplines. It was further intended to locate

meaningful variations of learning styles across different faculties. The population of the investigation was

the students of five faculties of Abdul Wali Khan University, Mardan. The study was grounded on the

Felder-Silverman Model (1998) of learning styles. The scale, Index of Learning Styles was used for data

collection purposes. The data were analyzed through percentages, mean, ANOVA and Tukey tests. The

outcomes provided the dispersions of learning modes of the sampled students. It was further manifested

that the visual mode of learning was highly liked while the intuitive mode was less preferred. Variations

were found in the learning styles’ preferences of different faculties. The study advocated the improvement

of curriculum and enhancement of instruction on the basis of study findings. Finally, directions for future

investigation are also given.

Keywords: Learning Styles, Felder-Silverman Model, Index of Learning Styles.

Introduction

Teaching – learning is an inclusive process influenced by various bahvioural, social and cognitive elements

such as interest, motivation, aptitude, environment, teacher‟s persona, intelligence, memory and individual

differences etc. Nowadays, there is shift of investigation on learning from achievement, memory,

intelligence, classroom climate to certain new factors like meta-cognition, reflective thinking and adapting

technologies. One of such important factors is learning style.

Literature indicated that learning styles means the process to acquire information. Student will face

obstacles if their favourite learning styles do not harmonize with the teachers‟ instructional modes (Felder

& Silverman, 1988; Felder & Spurlin, 2005). Recognizing the learning style of the students and using

mailto:[email protected]





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classroom resources in accordance with the particular learning style guarantee superior outcomes

(Gadzella, et al., 2002; Wynd & Bozman, 1996). Bajraktarevic, Hall and Fullick (2003) showed that

students facing discord between their courses and learning styles provide poor results. Similarly,

Manochehr (2007) and Zapalska and Dabb (2002) have viewed that recognizing learning styles of the

students is critical for learning as well as teaching.

The aforesaid discussion revealed that learning style is an important aspect of learning. There is limited

research work on the notion of learning style in the context of Pakistan, therefore, this investigation is

considered of worth. This study was focused to identify the learning styles of the university students.

Further, the nature of subjects and courses may affect the learning approach of the students; therefore, the

learning approaches of the students of various faculties were also taken into consideration.

Objectives

To identify the distribution of different learning styles of the university students.

To highlight the distribution of learning styles across faculties.

To find out the highly preferred and the less preferred learning styles.

To explore the significant difference across faculties in terms of learning preferences.

Research Questions

What are the distribution pattern of learning styles of university students?

What are the differences of learning styles across faculties?

What are the highly preferred and less preferred learning styles?

Is there exist any significant differences of learning styles across faculties?

Review of the Related Literature

There is differences in students‟ approach to achieve best of their learning, for instance one student may

acquire a concept by taking notes while his class fellow may be interested in learning it through videos

(Kanninen, 2009). Similarly, students show higher performances in their favourite subject, for instance,

Mathematic or English. Further, some learners favour activities while other are interested in learning from

visual material. Likewise, some learners focus on minute details while some others prefer on the themes as

whole. This particular approach to deal learning material is known as learning styles.

Brown (2000) hold that learning styles represent the approaches to comprehend and execute information in

the learning environment. Learning styles reveals a person‟s preference or a premeditated maneuver for

knowledge acquisition (Cassidy, 2004). MacKeracher (2004) viewed that learning style is an individualized

cognitive, social, physical and affective behaviour that act as relatively established indicator of a learner‟s

perceptions, dealings and reactions in the learning environment.

Learning styles are thought to be flexible and capable of modifying with changes in the learning

environment (Peterson, Rayner & Armstrong, 2009b). A learner highly preferring one mode of learning

may sometimes practice certain elements of other learning styles as well. A learner with varied learning

approaches may put them together and get an appropriate blend for every learning opportunity (Kanninen,

2009). Slater, Lujan and DiCarlo (2007) viewed that learning styles linger stable minimum for an interval

of two years.


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Models of Learning Styles

In the literature there exist various conceptions about learning styles, for instance, Kolb‟s model (1984),

Visual-Auditory-Kinethetic (VAK) model (Sousa, 1995), Honey and Mumford‟s model (1982) and Felder

and Silvermans‟ (1988) model. Most of the learning style‟s conceptions tag people into various isolated

categories. But Felder-Silverman Model (1988) classifies individuals in a broad spectrum of learning

modes i.e. classify people on four continuum rather than isolated categories (Graf, Viola Kinshuk & Leo,

2006; Kanninen, 2009), which turns this model distinctive, broad and comprehensive.

The Felder-Silveman‟s (1988) model was made know by Felder and Silverman (Din, 2009). They hold that

learning style denotes the mode through which learners obtain and exercise new ideas, concepts and

theorems. It depicts the learners‟ cognitive reactions, connection and attainment of learning stuff (Felder &

Brent, 2005; Felder & Silverman, 1988; Felder & Spurlin, 2005).

Felder and Silverman (1988) suggested a model of four continuums. Each facet reveals the preferred

manner of a person for acquisition. The dimensions of learning mode are represented on four continuums as

shown in the Figure 1. An individual may fall somewhere between the two extremes.

Active Balance Reflective

Sensing Balance Intuitive

Visual Balance Verbal

Sequential Balance Global

Figure 1. The facets of learning styles are on opposite ends of four continuums.

The active – reflective facet considers how information are executed. Active learners are practice oriented

and „doer‟ who favour to learn through performing, experimenting and executing learning tasks (Bacon,

2004; Filippidis & Tsoukalas, 2009; Mestre, 2010). They are also fond of group study (Bacon, 2004; Din,

2009; Mestre, 2010). Conversely, reflective learners internalize ideas and conceptions after thinking for a

longer time. They are fond of isolation in study (Filippidis & Tsoukalas, 2009; Mestre, 2010; Leithner,

2011). They also enjoy seminars and lectures (Din, 2009).

The sensing-intuitive factor is subsumed under the „type of information‟ a learner want to perceive

(Leithner, 2011). They are inquisitive and pragmatic and prefer facts, originality, verity and workable ideas

(Din, 2009; Leithner, 2011; Mestre, 2010). They dislike vagueness and intricacy (Bacon, 2004; Din, 2009)

and novelty and challenges (Din, 2009; Mestre, 2010; Ultanir, et al., 2012). Conversely, intuitive learners

prefer to learn from theories, law and concepts. They prefer to draw assumptions and fundamental

meanings (Bacon, 2004; Filippidis & Tsoukalas, 2009; Ultanir, Ultanir & Temel, 2012). They are superior

in determining associations and probabilities among ideas (Leithner, 2011; Mestre, 2010).

The visual – verbal factor considers how data is obtained (Leithner, 2011). They have sharp observation

(Din, 2009) and favour learning by means of observing maps, charts, tables, pictures, diagrams, models and

video data ( Bacon, 2004; Filippidis & Tsoukalas, 2009; Leithner, 2011; Mestre, 2010; Ultanir et al., 2012).

On the other hand, verbal learners have high interest in text and oral data (Filippidis & Tsoukalas, 2009).

There are better in learning from what they read or listen, for instance, magazine, books, lectures and

speeches etc. (Bacon, 2004; Mestre, 2010).

The sequential – global aspect is associated with grasping of information (Leithner, 2011). Sequential

students prefer to learn in a linear mode or in a stepwise approach on rational grounds (Bacon, 2004;

Mestre, 2010). Conversely, global preferences denote the tendency to learn as a whole and disregard the

minutes (Bacon, 2004; Din, 2009; Filippidis & Tsoukalas, 2009; Leithner, 2011). They study in a random


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mode without taking notice of the association among ideas, concepts and make haste to get the conclusion

(Din, 2009; Mestre, 2010).

Index of Learning Styles (ILS)

Felder and Soloman (n.d.) introduced a scale grounded on the learning styles model of Felder-Silverman

(1988). It was named as Index of Learning Styles (ILS). Since, the Felder-Silverman Model (1988)

contains four continuums of learning styles, therefore, the score of an individual may be located somewhere

between the two extremes or on one of the extreme for all four facets (Felder & Spurlin, 2005; Clarke,

Lesh, Trocchio & Wolman, 2010; Zywno, 2003). In the beginning, this instrument was administered to the

graduates of Engineering (Felder, 1996). This tool is provided free of cost for research and academic

purposes (Ultanir et al., 2012).

The ILS contains 44 binary choice items. Each statement has got two options. There are 11 questions for

each facet. On each continuum, for instance active – reflective, the options „a‟ represent the responses for

active mode while options „b‟ stood for the responses associated with reflective mode of learning. The

learning preferences of the respondents are denoted either through -11 to +11 or 11a to 11b. The ILS is

provided with a scoring key (Graf et al., 2006; Green & Sammons, 2014). The obtained scores are depicted

as „balanced‟ (1 or 3), „moderate‟ (5 or 7) and „strong‟ (9 or 11) (Felder & Spurlin, 2005).

The ILS is a psychometrically sound tool. Alumran (2008) estimated that the test-retest consistency was .8

with an interval of 15 days, whereas Felder and Spurlin (2005) found it in the range .7 - .9 with a pause of 4

weeks. The internal consistency of the ILS has been found to be in the range (.41 -.76) by various

investigation (See for instance, Felder & Spurlin, 2005; Ultanir et al., 2012; Zywno, 2003). Researchers

like Hawk and Shah (2007), Van Zwanberg, Wilkinson and Anderson (2000) have concerns about the

lower reliability, but Tuckman (1999) make a distinction that a reliability of .7 is adequate for achievement

measures while reliability of .5 is sufficient for attitudinal scales. This argument is maintained by many

researchers like Felder and Spurlin (2005), Ultanir et al. (2012) and Zywno, (2003). Zywno (2003) is of the

opinion the ILS meets the adequate standard of construct validity and reliability. Felder and Spurlin (2005)

asserted that the ILS has been validated in ten universities of four countries by studying English oriented

engineering students. In addition, feedback from the learners and factor analysis endorse its validity. To

conclude, ILS is an approved instrument in the research arena regardless of some criticism (Akbulut &

Cardak, 2012; Al-Azawei & Badii, 2014).

Method and Procedure

Participants

It was a descriptive expedition involving quantitative survey of the distribution of learning styles of the

university students. The population included all male and female students of the Abdul Wali Khan

University Mardan. It consist of five faculties.

Table 1: Distribution of Sample

Faculty No of Students

Arts & Humanities 166

Business & Economics 158

Chemical & Life Sciences 159

Physical & Numerical Sciences 195

Social Sciences 150

Total 828


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The technique of cluster sampling was adapted for drawing sample. Each faculty of the university were

considered as cluster for drawing cluster sampling. The sample of the study was taken from five faculties as

shown in the Table 1. Only the students of the final year were the target of the study i.e. learners studying

in their 16th

year of education. The researchers visited different faculties themselves to collect the data on

the spot through ILS. Prior permissions were taken before visits.

Research Tool

Index of Learning Styles was used in original form for collected views respondents. It was pilot tested prior

to full scale administration. The reliability of the scale in the local context was found .63, which is good as

proposed by Tuckman (1999) who hold that a reliability of .5 is acceptable for attitudinal scales.

Data Analysis

The data analysis was carried through the statistical techniques of percentages, means, ANOVA and Tukey

test through SPSS.

Table 2: Overall Percentage Analysis of Learning Styles

Strong Moderate Balanced Moderate Strong

Active – Reflective 5.8% 23.6% 53.6% 15.6% 1.4%

Sensing – intuitive 3.6% 25.8% 57.2% 12.7% 0.6%

Visual – Verbal 10.3% 34.5% 39.0% 14.6% 1.6%

Sequential – Global 3.1% 24.8% 51.4% 19.0% 1.7%

Note: The t two columns from the left indicate the first styles of learning, for example, „visual,‟ the 3rd

column reveals the balance of score between two modes while 4th

and 5th

columns highlight the second

aspect of each continuum of learning like „verbal.‟

The percentage analysis represents the distribution of the categories of learning styles. The analysis indicate

that on all aspects of learning styles more than 50% respondents were on „balanced‟ level in their learning

approaches except on the visual – verbal mode where 39% of the learners were on the „balanced‟ level. The

highly preferred mode of learning was visual (moderate, 34.5%, strong, 10.3%). While, the least favoured

mode of acquiring information was intuitive (moderate, 12.7%, strong, 0.6%).

Table 3: Mean Distribution of Learning Styles

Mean Std Dev Variance

Active – Reflective 3.17 .809 .654

Sensing – Intuitive 3.19 .720 .518

Visual – Verbal 3.37 .909 .827

Sequential – Global 3.09 .790 .624

The findings from the mean data complement the percentage outcomes. It is evident from the Table 3 that

the highly proffered mode of learning was visual – verbal (M = 3.37, SD = 0.909). Similarly, the least

opted aspect was sequential – global (M =3.09, SD = 0.790). The highest variance on visual-verbal factor

indicates that majority of the learners were inclined to one direction i.e. visual. These outcomes are further

elaborated in the Figure 2.

It is apparent from the Figure 2 that on overall basis the visual – verbal score is quite above the „mean‟ line.

Similarly, the values of active –reflective and sensing –intuitive facets are close to the mean line.

Conversely, the value of sequential – global value is lower than the mean line. It can be drawn that the

„visual‟ mode of learning carries the highest preferences.


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Figure 2. The mean distribution of different aspects of learning styles of the overall university students.

Table 4: Mean Distribution of Learning Styles across Faculties

Faculty Active -

Reflective

Sensing –

Intuitive

Visual -

Verbal

Sequential -

Global

Arts & Humanities Mean 3.06 3.05 3.02 3.06

Std Dev .776 .844 .809 .728

Business & Economics Mean 3.24 3.20 3.59 3.11

Std Dev .643 .691 .991 .803

Chemical & Life Sciences Mean 3.21 3.31 3.44 3.06

Std Dev .845 .628 .846 .869

Physical & Numerical

Sciences

Mean 3.26 3.09 3.50 3.09

Std Dev .941 .619 .944 .836

Social Sciences Mean 3.03 3.35 3.29 3.12

Std Dev .755 .768 .832 .694

Note: The highest mean values for each fact are shown in bold form.

It is evident from the Table 4 that the students of Faculty of Physical and Numerical Sciences superior on

active-reflective factor (M = 3.26, SD = 0.941). It may be due to the factor that their courses are dominant

with activities and experiments. Further, the Social Science students carried the highest average values on

sensing-intuitive (M = 3.35, SD = 0.768) and on sequential-global (M = 3.12, SD = 0.694). In addition, the

learners of Business courses were better on the visual – verbal aspect with mean value (M = 3.59, SD =

0.991). These outcomes are further illustrated through Figure 3.


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Figure 3. The average of learning styles‟ distribution of the students of different faculties.

The graph manifests that higher mean scores are carried by the faculties of Business and Economics,

Physical and Numerical Sciences and Chemical and Life Sciences. Conversely, the mean values of the

Faculty of Social Sciences are lower than the mean line. In addition, the mean values of the Faculty of Arts

and Humanity are fairly below the mean point on all facets. It can be drawn that majority of their scores

falls at mid of the continuums of all facet of learning styles and are not sharply inclined to any specific

learning mode. Further, the higher peaks of graphical lines indicate that the visual – verbal facet of learning

is highly favoured.

Table 5: Faculty wise ANOVA for Learning Styles

Sum of Squares df Mean

Square

F Sig.

Active –

Reflective

Between Groups 7.517 4 1.879 2.899 .021

Within Groups 533.483 823 .648

Total 541.000 827

Sensing –

Intuitive

Between Groups 11.347 4 2.837 5.597 .000


Total 428.467 827

Visual –

Verbal

Between Groups 32.497 4 8.124 10.268 .000


Total 683.685 827

Sequential –

Global

Between Groups .442 4 .110 .176 .951


Total 515.739 827


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The ANOVA was performed to assess the factors of learning styles in relation to different faculties. There

were vital differences on active – reflective dimension as indicated by ANOVA results (F (4, 823) = 2.899,

p = 0.021 < 0.05) across faculties. Likewise, on sensing – intuitive factor the ANOVA score (F (4, 823) =

5.597, p = 0.000 < 0.05) also indicates meaningful disparity across faculties. Further, the ANOVA findings

for visual – verbal (F (4, 823) = 10.268, p = 0.021 < 0.05) reflects significant differences across faculties.

However, on the sequential – global element, the ANOVA (F (4, 823) = 0.176, p = 0.951 > 0.05) did not

indicate any considerable differences. It can be drawn that students with different disciplinary backgrounds

have diverse learning approaches. The findings are further elaborated through conducting Tukey test.

Table 6: Multiple Comparisons of Learning Styles across Faculties Tukey HSD

Dependent

Variable

(I) Faculty (J) Faculty Mean Difference (I-J) Sig.

Sensing –

Intuitive

Arts & Humanities Chemical & Life Sciences -.266* .007

Social Sciences -.298* .002

Chemical & Life

Sciences


Sciences

.222* .029

Social Sciences Physical & Numerical

Sciences

.254* .009

Visual –

Verbal

Arts & Humanities Business & Economics -.565* .000

Chemical & Life Sciences -.416* .000


Sciences

-.478* .000

Business & Economics Social sciences .295* .030

*. The mean difference is significant at the 0.05 level.

A Tukey test was employed to estimate the magnitude of inclination of learning styles among faculties.

The ANOVA score shows that there were considerable differences of learning styles on the active-

reflective element. But the Tukey test did not show the amount and direction of the differences. On the

sensing – intuitive factor, it is evident that the Faculty of Arts and Humanities is considerably lower than

the faculties of Chemical and Life Sciences (MD = - 0.266, p = 0.007) and Social Sciences (MD = - 0.298,

p = 0.002). Likewise, the faculties of Social Sciences (MD = 0.254, p = 0.009) and Chemical and Life

Sciences (MD = 0.222, p = 0.029) were better than the Physical and Numerical Science courses in dealing

with sensing –intuitive data.

Further, on the visual – verbal aspect, the Faculty of Arts and Humanities was meaningfully lower than the

faculties of Business and Economics (MD = - 0.565, p = 0.000), Chemical and Life Sciences (MD = -

0.416, p = 0.000) and Physical and Numerical Sciences (MD = - 0.478, p = 0.000). In addition, the Faculty

of Business and Economics was significantly better (MD = 0.295, p = 0.030) than Social Sciences faculty.

Conclusions

The percentage outcomes showed that over 50% of learners were found on „balanced‟ level on three out of

four facets of learning styles. The highly favoured mode of learning was visual followed by active.

Similarly, the least opted style was intuitive. The verbal style was the second least chosen. Similarly, the


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results of mean analysis demonstrated that the highly selected dimension was visual – verbal while the least

opted dimension was sequential – global.

The logic behind highly preferring „visual‟ style may be that learners taken high interest in visual data and

go well with the subject matter presented through tables, charts, graphs picture and videos etc. Likewise,

students want to be an active participant in the class and prefer learning by means of activities and

practices. Conversely, the least opted mode was intuitive. The reason behind it may that usually individuals

mostly trust on the senses rather than intuition. Further, in the „verbal‟ mode of learning the learners

passively listen to others which is often disliked.

In terms of discipline, variations in learning styles is existed. The faculties of physical sciences, numerical

sciences and Business studies dominantly preferred visual, active and sensing modes. On the other hand,

the faculties of Social Sciences and Arts and Humanities were inclined towards verbal, global and intuitive

styles of learning.

Discussion

The percentage analysis revealed that more than 50% students were found on balanced categories except

visual dimension. It is in congruence with the findings of Graf et al. (2007) but marginally differentiated by

Shuib and Azizan (2015), who found all categories of learning styles above 50%. Similarly, Ultanir (2012)

declared sequential style being the top preferred.

The study explored differences of learning modes across academic disciplines. It is supported by Vermunt

(2005) who identified academic discipline being an important factor of determining learning styles.

Likewise, Lau and Gardner (2018) declared that learning styles prominently differ across academic

disciplines.

The outcomes of faculty wise average score revealed that learners of Faculty of Physical and Numerical

Sciences were superior on the active – reflective element. It may be due to the fact that these courses are

abundant with activities and experiments. It is supported by Siddique (2014). The Faculty of Social

Sciences carries the leading average values on sensing-intuitive and sequential-global factors. The pupil of

Faculty of Business and Economics was superior with highest mean value on the visual – verbal facet. On

the other hand, the Faculty of Social Sciences carried lowest score on the active – reflective aspect.

Likewise, the Faculty of Arts and Humanities has lowest mean scores on visual – verbal and sensing –

intuitive aspects. These findings are recommended by Felder and Brent (2005).

The analysis showed that the nature of faculty courses affect the learning styles of learners. The courses of

science subjects are abundant with experiments and practices so majority of the learners of the Faulty of

Physical and Numerical Science opted for active-reflective dimension. These finding are in congruence

with results apprehended by Daud (2014). Similarly, the scholars of Business programs usually deals with

summarized and précised kind of data explained through charts, tables, pictures, graphs etc. Therefore, they

highly preferred visual mode of learning. The disciplines with theory based programs have least

preferences for these styles.

Further, the majority of the learners of the discipline of Social Sciences have selected the sequential –

global and sensing-intuitive facets as they like to learn by means of senses and in a linear fashion.

Similarly, the scholars of Arts and Humanities and Social Sciences were poor on the visual – verbal and

active-reflective modes. Again, it may be due to the nature of their courses which are abundant with laws,

principles and theories. The stud y of William et al. (2013) are in agreement with these findings. It is

contradicted by Stavros et al. (2009) who hold that the least preferred dimension is sequential-global.


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Recommendations

The result showed that most of the students favor visual style of learning. The second highly preferred style

is „active.‟ The curriculum developers may consider this point and suggest guidelines to enrich curricula

with visual data and practical projects. The subject matter may be made abundant with diagrams, pictures,

graphs and tables etc. In addition, there may be sufficient amount of practical assignments.

Similarly, teachers may also enrich their instructions with visual material, activities and learning through

performing, particularly, teachers of Social Sciences and Arts and Humanities. In addition, the researcher

may conduct investigation on how to properly enrich curricula with visual information and practical

assignments for various levels and different courses. The researches may also establish the psychometric

attributes of the Index of Learning Styles at different research venues.

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