A LEARNING STYLE CLASSIFICATION MECHANISM ...

A LEARNING STYLE CLASSIFICATION MECHANISM USING BRAIN DOMINANCE AND VAK METHOD IN m-LEARNING ENVIRONMENT V. B. DESHMUKH1,∗ , A. B. KOTI1 , S. R. MANGALWEDE1 AND D. H. RAO2 1

Research Centre Gogte Institute of Technology, Belgaum, India. 2 Dean-Faculty of Engineering, VTU, Belgaum, India. e-mail: [email protected], [email protected], [email protected], [email protected]

Abstract. To take care of the personal learning needs of the students in an adaptive m-learning environment, a learner classification method is proposed. The classification is based on the thinking style and learning style of the learner. Students learn in different ways and instructors need to design their courses to meet the requirements of the students. The purpose of this investigation is to determine the learning styles of MBA students. A Brain dominance and VAK (Visual, Auditory and Kinesthetic) questionnaire was administrated to the students who were enrolled for m-learning course and learning styles of these students with dominant hemisphere were determined. This will provide a guide for instructors to develop the content to suit the personal needs of the students and deliver appropriate contents depending upon the learning styles. This adaptive learning environment promotes active learning and enhances learning experience of the learner. In the proposed system data from 75 learners is collected and learner’s thinking style and learning style are determined. The experimental results indicate that there is a need to design and deliver contents to suit the thinking and learning styles of the learner. Keywords: Brain Dominance, VAK Questionnaire, Learning Style, Dominant Hemisphere, Adaptive Learning Environment.

1. INTRODUCTION With the popularization of internet technology and wireless communication, the demand for m-learning has greatly increased. A lot of research work for learner classification, adaptive content delivery for enhancing teaching-learning quality is going on. Researchers have indicated that adaptive learning is a critical requirement for enhancing learning experience and promoting the performance of the learner. Adaptive learning leads to adaptive content delivery, content development, learning strategies, learning activities and/or courses according to a learner’s learning needs. To successfully address the needs of the individual learner and achieve an adaptive learning environment, it is necessary to identify learner’s learning needs and learning style and then deliver the contents to suit the needs. There are many classification techniques based on different aspects of learning process. Among them are Allinson and Hayes’ Cognitive Styles Index (CSI) [24], Apter’s Motivational Style Profile (MSP), Dunn and Dunn model and instruments of learning styles, Herrmann’s Brain Dominance Instrument (HBDI), ∗

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Honey and Mumford’s Learning Styles, Index of Learning Styles (ILS) [25], Felder, Silverman, Solomon [26], Fleming’s VARK learning style [20], Kolb’s Learning Style Inventory (LSI) [1,27], Memletics Learning Styles, (MLS), Myers-Briggs Type Indicator (MBTI), Paragon Learning Style Inventory (PLSI) [28]. These mechanisms need to be based on a large number of learner’s samples. In this paper a new learner classification method considering both the thinking style and learning style of the learner is proposed. Thinking style is an indicator of how a learner thinks (logical or creative), processes information (sequential or random) and expresses (verbal or nonverbal), learning style is an indicator of how a learner prefers to learn by visual, auditory or kinesthetic means [1,2]. Thinking style depends upon the right and left hemispheric activity of the brain [4] or brain dominance and learning style on the personal choice to perceive and process information [6]. We are administering the Brain Quiz developed by Catawba community college [21] for identifying hemispheric dominance and VAK Learning Style Inventory developed by V Chislett & A Chapman 2005 [22] for identifying preferred learning style. Section 2 provides insight into the Brain dominance and VAK learning style, section 3 discusses about the proposed system architecture. The results are discussed in section 4 and in section 5 the conclusions are presented. 2. BACKGROUND 2.1 Brain Dominance Each learner has distinct and preferred way of perception, organization and retention of information acquired. Students learn differently from each other and it has been established that brain activity influences this acquisition mechanism [3]. It has also been shown that different hemispheres of the brain contain different perceptive avenues. The left and right hemispheres of the brain process information in different ways [4]. We tend to process information using our dominant side of the brain. A lot of research work is carried out to study the correlation between hemispheric dominance and personality types, or between scores on the performance test and personality type [5]. The hemispheric dominance did correlate with performance, left brain learners scored better on both the routine and creative tasks assigned. However, when each of the groups was assessed individually, left brain learners scored higher on the routine tasks than on the creative tasks while right brain learners scored higher on the creative tasks than on the routine tasks Huston (1995). Considering the mass of the brain, scientists divide the brain into four areas called lobes. They are occipital, frontal, parietal, and temporal. The occipital lobe is in the middle back portion of the brain. It is primarily responsible for vision. The frontal lobe is the area around the forehead. It is involved with purposeful acts like judgment, creativity, problem-solving, and planning. The parietal lobe is the top back portion of the brain. Its duties include processing higher sensory and language functions. The temporal lobes are on each side, above and around the ears. These are primarily responsible for hearing, memory, meaning, and language. As a whole, the brain consists of two cerebral hemispheres, the left and right,that are connected by a bundle of nerve fibers known as the corpus callosum [5]. The corpus callosum has about 250 million nerve fibers and allows each side of the brain to exchange information more freely. In general, the left hemisphere is predominately involved with logical thinking, mathematical analysis, verbal functions etc. Its mode of operation is mainly linear and sequential, it moves from one point to the next in a step-by-step manner. If the left hemisphere specializes in logical thinking, the right hemisphere is more holistic. This hemisphere is primarily responsible for visual and special processing, our orientation in space, body image, recognition of faces, artistic endeavour, and creativity. While each side of the brain processes things differently, some of these earlier assumptions about the left and right brain are outdated. For example, experienced or “natural” musicians process music in their left hemisphere, not right as a novice would. Higher-level mathematicians, problem-solvers, and chess players have more right hemisphere activity when involved in these tasks, while 500

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beginners in those activities usually are left-hemisphere active. The right recognizes negative emotions faster and the left positive emotions faster. Brain lateralization refers to the activity of using one side (hemisphere) of the brain more than the other. However, to be more accurate, the term “relative lateralization” has been used, because a person is usually using at least some of the left and right hemisphere at the same time (whole brain mode). Because of this, it is important for instructors to have knowledge of brain hemisphericity in order to identify the advantages and disadvantages in their teaching techniques and understand when and how to develop and use certain techniques. In addition, knowledge of brain hemisphericity can assist them in becoming more flexible and effective in teaching in the classroom. Learning can be defined as any increase in knowledge, extracting meaning from what we do, processing an understanding from the experience and deciding on what course of action to take, if any [6,7]. The key to getting smarter (or increasing intelligence) is growing more connections between brain cells and not losing existing connections. Today, consensus tells us that heredity provides about 30 to 60 percent of our brain’s wiring, and 40 to 70 percent is environmental impact. Learning can change the brain because it can build new pathways and “rewire” itself with each new stimulation, experience, and behaviour. Scientists are not sure how this happens, but they have some idea of what happens. 2.2 VAK Style In broad terms, Learning Style (LS) can be defined as individual learning preferences and learning differences. Some of the learning styles that are present in literature are Dunn and Dunn, Kolb, Felder and Soloman and Fleming’s learning style. Many LSs were suggested in addition to the above mentioned ones, and studies were conducted about them [12] and [13]. VAK LS is a model that can be considered as basis among the said learning styles. This model appears as a LS that is based on individuals’ seeing, hearing, touching and working with moving objects. VAK LS was designed by Sarasin [23] and developed by V Chislett & A Chapman 2005 [22]. Learning styles are considered relevant for the adaptation process in the user model, and have been used as a basis for adaptation in [8,10] and [11]. This classification, proposed by Neil Fleming, divides the population into three classes [19,20] namely visual, auditory and kinesthetic. The visual learners prefer quiet and order around them otherwise they have difficulty maintaining concentration. They remember colours, drawings, graphs and faces and the position of objects in space as well. They have a problem with remembering names, and titles [14,15]. Visual learners remember best what they see in the form of text, video, graphics, and plots. They like to make handwritten notes, prefer the visual arts. The auditory learners like to talk, sing, and whistle. They learn by listening to lectures, reading aloud, and discussions. They remember well music and the conversations however may have problems with reading the graphic forms, such as maps, geometry. They prefer to speak about the action rather than watching it. They require silence to learn, music and noise do not allow them to focus. They learn language easily. The Kinesthetic learner feels best in motion. They become tired sitting at the desk, listening to a lecture. During the speech they often gesticulate. They require a break between learning sessions. They like to work in a group. Because of its simplicity, the VAK Learning Style Inventory (VAK LSI) is widely recognized in education. These characteristics define learner’s model in online educational system [9]. An adaptive learning system is usually a web-based application program that provides a personalized content delivery in m-learning environment for each learner [16,17] and [18]. 3. BASIC SYSTEM ARCHITECTURE The adaptive m-learning environment can enhance the learning experience and performance of the learner. In an adaptive m-learning environment the type of the content, rate of content delivery, selection of learning ATEES-2014

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Figure 1.

Architecture of classification mechanism.

activities etc. can be personalized to suit the needs of the learner. In this paper we propose a learner classification method considering both thinking style and learning style of the learner. The thinking style of the learner is identified by administering Brain dominance test and the learning style by VAK test as shown in Figure 1. On the basis of the two tests the learner is grouped into one of the six groups. The proposed system architecture has following blocks. The Learner Group consists of a sample of 75 adult learners studying MBA course. They are administered the brain dominance test to know their thinking style. 3.1 Brain Dominance Test The learner group registering to the m-learning course takes Brain Dominance test [21], which classifies them on the basis of their thinking style into two groups namely Left brain (L) or Right brain (R) dominant group. The left brain dominant group is logical, sequential, reality based and verbal. The right brain dominant group is intuitive, random processing, fantasy oriented and non verbal. 3.2 VAK Inventory In this both the left brain dominant and right brain dominant groups are administered the VAK learning style inventory [22]. This further classifies the two groups into three groups each. Thus the entire learner group is classified into six specific learning styles, namely Visual Left (VL), Visual Right (VR), Auditory Left (AL), Auditory Right (AR), Kinesthetic Left (KL) and Kinesthetic Right (KR). The reliability and validity of the questionnaire is tested. 4. EXPERIMENTAL RESULTS The experiment is divided into two phases. 502

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A Learning Style Classification Mechanism using Brain Dominance and VAK Method Table 1. Percentage of left, right and bilateral brain dominance in students. Sl. No. 1 2 3

Figure 2.

Brain Dominance Bilateral(B) Left(L) Right(R)

Total 75 75 75

No. of Students 01 39 35

Percentage 01.40 51.30 47.30

Percentage of left, right and bilateral brain dominance in students.

Table 2. Percentage of visual, auditory and kinesthetic learning styles in left brain dominant students. Sl. No. 1 2 3

Figure 3.

Learning Style Visual-Left (VL) Auditory-Left (AL) Kinesthetic-Left (KL)

Total No. 39 39 39

No. of Students 14 11 14

Percentage 35.89 28.20 35.89

Percentage of visual, auditory and kinesthetic learning styles in left brain dominant students.

4.1 Brain Dominance Test The Brain dominance test classifies the learner group into two dominant hemispheric groups namely Right brain dominant and Left brain dominant groups on the thinking style of the students. The brain Dominance test of Catawba community college Brain Quiz [21] was conducted on a sample of 75 college level adult students to identify their dominant hemisphere. The results are shown in Table 1. 4.2 VAK Inventory The two dominant groups when subjected to VAK Learning Style Inventory, each group is further classified on the basis of preferred learning style into three groups. Thus after the VAK test the entire learner group is classified into six specific groups. The results are tabulated in Table 2 and Table 3. ATEES-2014

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V. B. Deshmukh, et al. Table 3. Percentage of visual, auditory and kinesthetic learning styles in right brain dominant students. Sl. No. 1 2 3

Figure 4.

Learning Style Visual-Right (VR) Auditory-Right (AR) Kinesthetic-Right (KR)

Total No. 35 35 35

No. of Students 08 18 09

Percentage 22.85 52.42 25.71

Percentage of visual, auditory and kinesthetic learning styles in right brain dominant students.

5. CONCLUSIONS AND FUTURE WORK The purpose of the study was to classify 75 MBA students on the basis of their thinking style and learning style. The brain dominance and VAK tests classify the learner group into six classes. From the results of brain dominance test we can conclude that the percentage of bilateral group is only 1.40%, 51.40% are left brain dominant and 47.30% are right brain dominant as shown in Figure 2. From the VAK test conducted on the right and left brain dominant group we get six groups of learners as in Figure 3 and Figure 4. After considering the percentage of students in each group it is clear that no group can be ignored as the minimum group size is 11% of the overall student population. To enhance the learning experience and promote effectiveness of the knowledge acquisition the content development and delivery to suit the learning needs and preferred learning style of the individual group of students has to be done. To implement the adaptive m-learning successfully, for every unit the instructor has to prepare as many as six different versions of the same content. To reduce the high costs associated with both preparation of appropriate tools (m-learning platforms), and teaching materials (m-content) that would meet the requirements of the model, the Learning Objects such as LOM (Learning Object Module ), SCORM (Sharable Content Object Reference Model), which allow reusing lesson units for various courses on different platforms are to be prepared and delivered. REFERENCES [1] Kolb, D. A., Experiential learning: experience as the source of learning and development Englewood Cliffs, NJ: Prentice Hall 1984. [2] Richmond, A. S. and Cummings, R., Implementing Kolb’s learning styles into online distance education. International Journal of Technology in Teaching and Learning, 1(1), 45–54, 2005. [3] Diana Laurillard, London Knowledge Lab In: Pachler, N. (ed) (2007) Mobile learning: towards a research agenda. London: WLE Centre, IoE. [4] Diana J. Muir, Adapting Online Education to Different Learning Styles, National Educational Computing Conference, “Building on the Future”, 1, July 25–27, 2001—Chicago, IL. [5] Steven D. Bielefeldt, An Analysis of Right- and Left-Brain Thinkers and Certain Styles of Learning The Graduate School University of Wisconsin–Stout, May 2006. [6] Rajshree S. Vaishnav, Learning Style and Academic Achievement Voice of Research, Vol. 1 Issue 4, March 2013, ISSN No. 2277-7733.

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A Learning Style Classification Mechanism using Brain Dominance and VAK Method [7] Neil Fleming and David Baume, Learning Styles Again: Varking up the right tree, Educational Developer and Educational Developments, SEDA Ltd., Issue 7.4, Nov. 2006, p. 4–7. [8] Marek Woda and Konrad Kubacki-Gorwecki, Students Learning Styles Classification For e-Education, ICIT 2011 The 5th International Conference on Information Technology. [9] Burcu Devrim Ictenbas and Hande Eryilmaz, Determining Learning Styles of Engineering Students to Improve the Design of a Service Course. Procedia - Social and Behavioral Sciences, 28 (2011) 342–346, Science Direct. [10] Abbas Pourhossein Gilakjani, Lahijan Branch, Islamic Azad University, Lahijan, Iran Visual, Auditory, Kinaesthetic Learning Styles and their Impacts on English Language Teaching Journal of Studies in Education, ISSN 2162-6952, 2012, Vol. 2, No. 1. [11] Fong, A. C. M., Hui, S. C. and Lau, C. T., Nanyang Technological University On-Demand Learning for a Wireless Campus 1070-986X/04/$20.00 ©2004 IEEE Published by the IEEE Computer Society. [12] Mary C. Rothenberger, The Effect of Learning Styles on Success in Online Education. [13] Norasmah Othman and Mohd Hasril Amiruddin, Different Perspectives of Learning Styles from VARK Model International, Conference on Learner Diversity 2010 Science Direct. Procedia Social and Behavioral Sciences 7(C) (2010) 652–660 Available online at www.sciencedirect.com. [14] Jegatha Deborah, L. Baskaran, R., Kannan, A. and Vijayakumar, P., Intelligent Agent Based Pair Programming and Increased Self-Efficacy through Prior-Learning for Enhanced Learning Performance. pp. 87–100, Malaysian Journal of Computer Science, Vol. 26(2), 2013. [15] Dekson, D. E. and Suresh, E. S. M., Learner Centered Adaptive and Intelligent E-Portfolio Architecture for Learning (AIEPAL). [16] Brusilovsky et al. [17] Abbas Pourhossein Gilakjani, Lahijan Branch, Islamic Azad University, Lahijan, Iran Visual, Auditory, Kinaesthetic Learning Styles and Their Impacts on English Language Teaching, Journal of Studies in Education, ISSN 2162-6952, 2012, Vol. 2, No. 1. [18] Al-Dahoud, A., Walkowiak, T. and Woda, M. Dependability aspects of elearning systems. Proceedings of International Conference on Dependability of Computer Systems, DepCoS - RELCOMEX 2008, Szklarska Por¸eba, Poland, 26–28 June, 2008 IEEE Computer Society. [19] Meryem Yilmaz-Soylu and Buket Akkoyunlu, The Effect of Learning Styles on Achievement in Different Learning, Environments The Turkish Online Journal of Educational Technology – TOJET October 2009, ISSN: 1303-6521, Volume 8, Issue 4, Article 4. [20] Flemming N. VARK, Questionnaire (accessed Dec 2010) http://www.vark-learn.com/english/page.asp?p=questionnaire [21] Catawba Community College Brain Quiz. http://www.cvcc.edu/ [22] Chislett, V. Msc, and Chapman, A., 2005 From www.businessballs.com. VAK test questionnaire. [23] Lynne Celli Sarasin, At wood: Madison Learning Style Perspectives: Impact in the Classroom WI, 1998. ISBN 1-891859-22-6. [24] Graf, S., Adaptivity in Learning Management Systems Focusing on Learning Styles, Ph.D. Thesis, Technische Universitat Wien, 2007. [25] Kanninen, E., Learning Styles and E-Learning, Master of Science Thesis, Tampere University of Technology, 2009. [26] Kartin, L., E-Learning: The Quest for Effectiveness, Malaysian Online Journal of Instructional Technology, Vol. 2, No. 2, p. 61–71, 2005. [27] Kolb, A. and Kolb, D., The Kolb Learning Style Inventory, Case Western Reserve University, 2005. [28] Kinshuk and Taiyu Lin, Improving mobile learning environments by applying mobile agents technology, Massey University, Palmerston North, Third Pan-Commonwealth Forum on Open Learning, 2004, New Zealand.

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