2015 4th International Symposium on Emerging Trends and Technologies in Libraries and Information Services
Fuzzy Approach to Detect Learning Style Using Mccarthy Model as a Tool for E-learning System Mukta Goyal Department of Computer Science Jaypee Institute of Information Technology Noida, India
[email protected]
Divakar Yadav
Alka Tripathi
Department of Computer Science Jaypee Institute of Information Technology Noida, India
[email protected]
Department of Mathematics Jaypee Institute of Information Technology Noida, India
[email protected]
Another approach to find out the learning style in elearning system is the learner’s behaviour on the basis of content visit, exercise visit, quiz visit, performance in the quiz, forum, navigation, etc [3].
Abstract -- This study suggests a soft computing approach to
extract knowledge which can decide learner’s learning style in elearning environment. The approach uses the rules of the learning style instrument(LSI) designed by Marlene Lefever which is based on the Mccarthy Model. There are e-learning system which detect the learning style of a particular learner in a crisp manner. Due to the fuzzy nature of the learner a fuzzy approach is used to decide the learning style of the learner. The proposed method has used the McCarthy Model as a tool to detect the learning style of the student. This increase the efficiency of the learning because most of the systems are based on the notion that each student has only one learning style which is not true.
Fuzzy set theory which was introduced by Prof. L. Zadeh [5] in 1965. Fuzzy sets defines the membership and non membership function which says an element is not a strictly a member or a member of a set. Hence, a set is called fuzzy when its membership function takes values in the unit interval [0,1] rather than in the {0,1} as in the classical logic. In this work a fuzzy approach is used on Mccarthy Model [4] as tool to detect the learning style. Mccarthy Model is divided in to four type of learning style i.e innovative, analytical, commonsense and dynamic. Innovative which is defined as charming sociable, listen, and share, supportive, slow decision makers. They like team work, change themselves in others company. They like to take initiative, enjoy brainstorming session to solve problems, puzzles. They do not enjoy computer assisted education, timed tests. Analytical are the one who ruled by mind are logical, work on facts, who are theoretical by nature. They do not like team work. They like to work on facts. They are not dynamic. They are systematic and do not want to be in panic mode. Commonsense people like simulation. They do not want to memorize the things, working in a group and writing assignment. They like logical problem solving. Dynamic learners are imaginary, visionaries, inspire with confidence. They enjoy assignment requiring originality and case studies.
Keywords – LearningStyle, Marlene-Lefever, Innovative learning, e-learning environment, fuzzy approach. I.
INTRODUCTION
The rapid growth of web based courses for education and training impose challenges to e-learning systems to generate content according to the level of the learner. Learning styles is not necessarily a measure of his intellect and learning abilities. Learning styles are important for knowledge performance and should be considered for effective learning[1]. Each person has individual learning style. Learning style not only measures the intellectual and learning abilities but also suggest a preference for the manner that the person wants to learn. It means that a person might prefer some learning style over others but also use aspects of other styles. The learners possess several learning styles and can mix them together to obtain the most suitable combination for each learning event.
II.
Learning styles, not only measure the intellect and learning abilities of a student, it also suggest a preference for the manner in which the user want to learn a concept in an elearning system[3,7,8]. Table 1 represent the learning style which represents the learner’s psychological categorization and assessment instrument [7].
Validity is one of the issue of the learning styles which are based on self report measures.[2]. There are several learning style models, each with their own assessment instrument in the form of questionnaire. Learning style questionnaires include various amounts of questions about personality, study, attitude and behaviour.
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LITERATURE SURVEY
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Most of the model classifies the learner’s preferences on scales driven from psychological categorization. Table 2 represent the Adaptive Educational hypermedia systems considering learning styles to present the content in front of user [8]. Our proposed approach uses fuzzy model to decide learner’s learning styles using the rules due to the mixed trait of learning style so that mixed content can be present to the student. It will increase the learning capacity of the learner.
TABLE I.
III.
METHODOLOGY
This section presents the proposed method for evaluating the learner’s learning style using fuzzy approach. After calculating the mixed trait of learning style learner could be suggested to study appropriate type of learning material (theory, example, etc…) according to his/her learning style. In this work there are inputs with respect to their learning
TABLE 1: LEARNING STYLES WITH ASSESSMENT INSTRUMENT [7]
Name
Learner’s Categorization
Assessment Instrument
Kolb Learning Style Inventory
Divergers (Concrete, reflective), Assimilators(abstract, reflective), Convergers (abstract/active)
Learning style inventory(LSI, consisting of 12 items in which subjects are asked to rank 12 sentences describing how they best learn.
Accommodators(concrete/active) Dunn and Dunn- Learning style Assessment Instrument
Environmental, Emotional, Sociological, Physical Factors
Learning Style Inventory(LSI) designed for children grade 3-12; (ii) Productivity Environmental Preference Survey(PEPS)-adult Version of the LSI containing 100 items
Felder- Silverman Index of learning Styles
Sensing-intuitive,Visual-Verbal, Indicativedeductive, Active, reflective, Sequential Global
Soloman and felder questionnaire, consisting 44 questions.
Rididng- Cognitive Style Analysis
Wholists- Analytics, Verbalisers-Imagers
CSA( Cognitive styles Analysis tets, consisting of three subtest based on the comparision of the response time to different items.
Honey and Mumford Learning Style Questionnaire
Theorist, Activist, Reflector, Pragmatist
Honey and Mumford learning style questionnaire(LSQ) consisting of 80 items with true false answers
Gregoric- Mind Styles and Gregoric Style Delineator
Abstarct Sequential, Abstarct Random, Concrete Sequential, Concrete Random
Gregoric Style Delineator containing 40 words arranged in 10 columns with 4 items each the learner is asked to rank the words in terms of personal preference.
Mc Carthy- 4 Mat System
Innovative , Analytic, Commonsense , Dynamic
Gardner-Multiple Intelligence Inventory
Linguistic,Logical, Mathematical, Musical, Bodily – Kinesthetic, Spatial, Interpersonal, Intrapersonal
An instrument consisting of 8 quetsions
Grasha- Riechmann Student Learning Style Scale
Competitive- Collaborative, AvoidantParticipant, Dependent, Independent
90 items self report inventory measuring the preferences of both high school and college students
Hermann- Brain Dominance Model
Quardant A(left brain cerebral) Quardant B(left brain limbic) Quardant C (right brain,limbic) Quardant D( right brain cerebral)
120 questions that refer to four profile preference codes corresponding to each quadrant
Myers- Briggs- Type Indicator
Extroversion, Introversion, Sensing, Intutions, Thinking, feeling, judgement, Perception
MBTI(Myers Briigs Type indicator)(ii) Kiersey Temperament Sorter I, and (iii) Kiersey Character Sorter II
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TABLE II.
TABLE 2: ADAPTIVE EDUCATIONAL HYPERMEDIA SYSTEMS CONSIDERING LEARNING STYLES [8]
Adaptive system
Learning Style model
Adaptation method of learning style
Student Modelling Approach
Developed
CS383 (Carver, Howard, and Lane, 1999)
Felder-Silverman learning style model.
Ordering of multimedia objects
ILS questionnaire
1999
Multimedia Asynchronous Networked Individualized Courseware (MANIC)
Combination of learning preferences
Use of strechtext(hiding and presenting additional content)
Automatic approach by using a Naïve Bayes Classifier and population data
1997
Intelligent Distributed Environment for Active Learning (IDEAL)
Determined by teacher
Ordering inclusion and selection of learning materia
Questionnaire of the considered learning style model
2001
MASPLANG
Felder-Silverman learning style model.
Adaptation interms of choosing the relevant media formats. Instructional strategies and navigational tools
Index of learning style 2004 questionnaire for initializing a nd a case based reasoning process for fine tuning.
Learning Style Adaptive System (LSAS)
Sequential/Global dimension of FelderSilverman learning style model.
Hiding / presenting additional links and course elements
Index of learning style questionnaire
2003
iWeaver
Presentation preferences and psychological preferences with respect to Dunn and Dunn learning style
Link ordering and link hiding for selecting different presentation modes of learning tools.
Building Excellence inventory: automatic approach is planned.
2003
Intelligent System for Personalized Instruction in a Remote Environment (INSPIRE)
Honey and Mumford Learning style model
Method and order of the content presenattion
Questionnaire by Honey and Mumford or intitializing / updating the student model manually.
2003
Task-based Adaptive learNer Guidance On the Web (TANGOW)
sensing/intuitive and the sequential/global dimensions
Order of tasks and order of elements within tasks
Index of learning style for initializing and an automatic student modeling approach for revising the information in the student model.
2001
AHA
Determined by the teacher
Adaptation interms of selection of items present, ordering information and creating different navigation paths.
Manually initialized and updated by determined instructional meta strategies.
2005
style and four outputs of linguistic variables are being considered. The input linguistic variable’s are representing
the student’s learning style score: Group interaction scores, Timed Test scores, Role Play scores, Dramatize scores,
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2015 4th International Symposium on Emerging Trends and 298Technologies in Libraries and Information Services
Practical scores, Creative scores, Evaluate scores, Investigate scores, and Theoretical scores. The output linguistic variables are Innovative, Analytical, Commonsense, Dynamic shown in Table 3.
which is written in front of each question such as 1st question has four option. We have divided all questions in different input parameter such as 1st question refers to Group Interaction. Here 1st option refers to Innovative, 2nd refers to analytical, 3rd refers to commonsense, and 4th option refers to dynamic. A learner will have to mark each option into a scale of 1, 2 or 3 where 1 represent low, 2 represent medium and three represent high. If the answer to the questionnaire gives membership value high for group interaction, role-play and creative, then we say innovative parameter is high (Being input parameters for innovation). On the basis of this we have designed the rules and proposed mathematical formula for the same.
The input fuzzy set expressed by
While the output expressed by ) Mccarthy Model has been used as a tool to detect the learning styles of the student. Mccarthy model is based on Kolb's model of learning styles and the concept of brain hemisphericity. It consist of four learning style a) Innovative, b) Analytical c) commonsense and d) dynamic. A questionnaire designed by Marlene Lefever [6] is given to the students based on the four learning style. Marlene Lefever, by using McCarthy’s 4MAT system, restates and clarifies four learning styles from the Christian perspective. A Learning style instrument (LSI) designed by Lefever consist a 23-item questionnaire in which respondents attempt to describe their learning style preferences. The respondent is asked to rank each item in order -(1) Low (L), (2) Medium (M), (3) High(H) corresponding to the four learning model. we determine the membership function of fuzzy set . The numerical ranges of each membership function are Low Æ [ 0 -- 0.4] Med Æ [ 0.3 -- 0.7] High Æ [ 0.6 -- 1] Parameters have been designed on the basis of Lefever learning style instrument in Table 3. TABLE III. TABLE 3: DESIGN OF THE PARAMETERS BASED ON THE MARLENE LEFEVER [6] ASSESSMENT QUESTIONNAIRE. Output Parameters
Input Parameter
Innovative
Group interaction roleplay creative
Analytical
Timed Test Theoretical
Commonsense
Practical Evaluate Investigate
Dynamic
Dramatize creative roleplay Evaluate
A sample questionnaire is shown in fig1; each question has four options. Each option refers to different learning style
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Fig. 1: Questionnaire designed by Marlene Levefer [6]
3) If (
med and
Table 4 represent rules that detect the learning style of the learner. TABLE IV.
Max (G,R, C) H
Max (TT,TH ) L
Max (P,E,I) M
Max (D,C,R, E) L
L
H
H
M
H
L
M
is low and
high or
Output-Learning style I A C D
5) If (
is high or
is med or
H
H
H
H
H
H
M
M
H
H
H
C. Fuzzy rules for Commonsense are
H
H
L
M
H
H
1) If (
or
is high or
is med or
is low and
is high or
is high ) or (
is med) then is low and
is high or
is high or is low and is med or
is high or
is high) or(
is
is med or
is med
is med) then is med and
is high or
i is high) then
is low) or (
is low and
is med or
is low ) or (
is
is low and
is low and
is low ) or
is low and
is med and
is high) or
6) If (
is Med and
is Med and
is high)
( is high or is high or is high or is high or is high or is high) then is high.
is med. is high) or
D. Fuzzy Rules for Dynamic Learner are
is is high or
1) If (
is high.
is med or
is high or
is
low ) or ( is med and is low and is high and is low and is med and is med )then is high and
is high and
is med and is low and
is high )or (
( is med or is med or is med or is med or is med or is med) then is med.
is
is high or
is med or
5) If (
is high
is med or
is med and
( is high or is high or is high or is high or is high or is high) then is high.
is med. is low) or (
is high or
is low and
4) If (
is
2) If (
is low
is med and
is med ) then
is high. is low and is low) or (
and is med and is med) then is high 2) If ( is low and is high ) or ( is high and low and
is med or
is high
is high.
med and is low and is med and is med and is low and is med) then is med.
is high) then is high. is med and f is high) or
is high or
is high or
is low and
is high or
is high) then
is high or
3) If (
B. Fuzzy rules for Analytical learner are med and
is
is low and is med and is med and is low and is low and is high) then is low.
is
is low and
is med and
med or is med or 6) If ( is med and
1) If (
is high or
is high or
2) If (
is high and
is low and
is low and is low and
high or
is high or
med and is low and is high and is low and is med and is med )then 3 is high.
is low and is med and is med is low. 3) If ( is med or is med or is low )or ( is
(
is med
is med or
L
is med and is med) then is high 2) If ( is low and is med and is high) or (
(
is med or
M
A. Fuzzy Rules for Innovative learner are
or 5) If (
is high or
L
med and
is
med or is med or is med ) then is med. 6) If ( is med and is med) or ( is high or
L-Low, M-Medium, H-High
and 4) If (
is med and
H
med and
is low and
is high or is high) then is high. is low and is med ) or ( is med and
is high or
1) If (
is low )or (
and is low and is med Xi is med) then is med. 4) If is low and is low) or ( is high or is
EXAMPLES OF FUZZY RULES
Input (Different parameters)
is med or
med and then 3) If (
is
is low.
299
is low and
is low and
is low. is med or
low )or (
is med and
is med and
is low and
is med or
is med and
is high and is high)
is low or
is low and
is
is
is med
2015 4th International Symposium on Emerging Trends and Technologies in Libraries and Information Services
and
is med and
is med. 4) If ( is low and
is low and
V.
is med ) then
is low and
The proposed method will be able to increase the efficiency of the learning in the e-learning system because most of the e-learning systems are based on the notion that each student has only one learning style which is not true. Due to the fuzzy nature of the learner, the proposed method will decide the different learning style from the learner so the content will be available to them in that manner.
is low and
is low )or ( is high or is high or is high or g is high or is high or is high) then is high. 5) If ( is low and is low )or ( med or
is med and is med or
is med or
then is med. 6) (If is med and is low) or ( high or then
is high and
is med or
is med or
is
is med ) REFERENCES
is med and is high or
is high or
is high and
is high or
is high or
[1]
Y. Gülbahar A. Alper,” Learning Preferences and Learning Styles of Online Adult Learners,”International Education Journal vol 4, no 4, 2004,
[2]
A.Altun,., & M. Cakan,”Undergraduate Students’ Academic Achievement, Field Dependent/Independent Cognitive Styles and Attitude toward Computers”,. Educational Technology & Society, vol. 9, no. 1, pp. 289-297, 2006.
is is high)
is high.
The above rules can also be obtained mathematically with the help of following formula Max(Max(
,
,
),Min(
Max(Min(
), Max(
Max(Min(
),Min(
,
, ,
,
))
,
,
,
[3] Graf, S., Kinshuk, & Liu, T.-C. “Supporting Teachers in Identifying Students' Learning Styles in Learning Management Systems: An Automatic Student Modelling Approach”. Educational Technology & Society, vol. 12, no. 4, pp. 3–14, 2009
,
))
,
[4] Uyangör, Sevinç.M,”The effectiveness of the 4MAT teaching model
))
[5] [6] [7]
Here we have normalized each learning style value and then given the membership values. These membership values will be used in decision making with the help of above formula.
[8]
IV. TABLE V.
EXPERIMENTS AND RESULTS
COMPARISION BETWEEN THE FUZZY RULE BASED METHOD AND THE CONVENTIONAL METHOD
Data Input( Student learning style(%) Max (G,R, C)
Max (TT,T H)
0.58
0.8
0.67
Max Max (P,E (D,C ,I) ,R,E )
Fuzzy Rule Base Method(%) I
Conventional Method
A
C
D
1
A
C
D
0.75 0.58 0.58
0.8
0.8
0.58
1
0
0
0
0.8
0.67 0.67 0.67
0.8
0.67
0.67
0
1
0
0
0.5
0.7
0.83 0.67 0.67
0.7
0.83
0.83
0
0
0
1
0.42
0.67
0.92 0.5
0.67 0.92
0.5
0
0
0
1
0.5
G- Group Interaction,R-Roleplay,C-Creative, TT- Timed Test,Th – Theoritical,P-Practical,E- evaluate, I_ Investigate,D-Dramatize, C-Creative, E-
CONCLUSION
Evaluate,
Table 5 shown the comparision between fuzzy rule base method and conventional Learning style evaluation. In this method it shows that the student have different learning style with a certain percentage of membership rather than having one particular learning style.
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upon student achievement and attitude levels”, International Journal of Research Studies in Education vol. 1 no. 2, pp. 43-53, 2012. L.A Zadeh,” Fuzzy Sets”, Information and Control”,8, 338-353,1965. M.Lefever, Learning Style, David C Cook Publishing Company, E.Kanninen,” Learning Styles and E-learning,”, Master of Science Thesis, Tampere University Of Technology S. Graf,”Adaptivity in Learning Management Systems Focussing on Learning Styles”, Ph.D Thesis, Vienna University of Technology