Simulation of fuzzy adaptation of cognitive/learning

INTERNATIONAL JOURNAL OF ELECTRONICS; MECHANICAL andMECHATRONICS ENGINEERING Vol.4 Num 1 pp.(701 - 712)

Simulation of fuzzy adaptation of cognitive/learning styles touser navigation/presentation preferences by using MATLAB

Ilham N. HUSYINOV

Department of Software Engineering, Faculty of Engineering, Istanbul Aydin University, Florya,Istanbul/ Turkey,

E-mail: [email protected]

Abstract. The purpose of this paper is to present a simulation methodology of a fuzzy adaptive interface in anenvironment of imperfect, multimodal, complex nonlinear hyper information space. A fuzzy adaptation of user’sinformation navigation and presentation preferences to cognitive/learning styles is simulated by using MATLAB. Tothis end, fuzzy if-then rules in natural language expressions are utilized. The important implications of this approachis that uncertain and vague information is handled and the design of human computer interaction system is facilitatedwith high level intelligence capability

Keywords: adaptive interface, fuzzy logic, cognitive/learning styles, navigation/presentation preferences,linguistic modelling.

1. INTRODUCTION

The power of hypermedia of webtechnology is in its capability to supportnon-linear navigation in hyperspace andmultimedia presentation of the web content.Web Based Hypermedia adaptive Systems(WAHS) offers an alternative to thetraditional “one-size-fits-all” hypermediaand Web systems by adapting to the goals,interests, and knowledge of individual usersrepresented in the individual user models[1]. WAHS aims to minimize cognitiveoverload faced by users, to alleviate thedisorientation problem of users, to enhancethe usability and the utility of the system byapplying intelligent information adaptation(personalization) techniques foruser/system interactions that take intoaccount individual differences of users [2].Adaptation involves two key activities: (i)a user modelling activity to develop a usermodel and (ii) an adaptation activity thatleverages a ‘rich’ user-model to personalizethe information content, the informationpresentation style and the navigation pathof the system to the user [3]. One of theways to enhance the efficiency of WAHS is

to build accurate user models. It can beachieved by taking into account humanfactors (or individual differences) that havesignificant effects on human computerinteraction and on the learning process [4].Research into individual differencessuggests cognitive/learning styles havesignificant effects on student learning inhypermedia systems [5].

The purpose of this paper is to presenta simulation methodology of an adaptiveinterface in an environment of imperfect,vague, multimodal, complex nonlinearhyper information space. To this end, afuzzy adaptation strategy tocognitive/learning styles is simulated byusing MATLAB. Fuzzy if-then rules areutilized to adaptively mapcognitive/learning styles of users to theirinformation navigation and presentationpreferences through natural languageexpressions. The important implications ofthis approach is that uncertain and vagueinformation is handled and the design ofhuman computer interaction system isfacilitated with high level intelligencecapability

The paper is organized as follows.The description of cognitive and learning

SIMULATION OF FUZZY ADAPTATION OF COGNITIVE/LEARNING STYLESTO USER NAVIGATION/PRESENTATION PREFERENCES BY USING

MATLABIlham N. Huseyinov

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styles is given in Section 2. Navigation andpresentation preferences of users arepresented in Section 3. The adaptationprocess, examples of fuzzy granulation ofinput and output linguistic variables, and aninference mechanism of adaptation arepresented in Section 4. Section 5 is devotedto description of a simulation example toillustrate the proposed approach. Finally, inSection 6 we present conclusions and futurework.

2. COGNITIVE/LEARNINGSTYLES

The nature of cognitive styles (CL) isstudied by cognitive psychology. CS dealwith the form of cognitive activity(thinking, perceiving, remembering), not itscontent. Learning styles (LS), on the otherhand, is seen as a broader construct, whichincludes cognitive along with affective andphysiological styles. A key factor indetermining cognitive styles with respect tolearning is the field dependency factor.Field dependency refers to an individual’sability to perceive a local field as discreteform of its surrounding field. It is a singlebi-polar dimension ranging from Fielddependent (FD) individuals at one extremeto Field independent (FI) individuals at theother [6]. Characteristics of users inrespect to CS are described in [5] and canbe modelled by a hierarchy type treestructure given in Figure 1.

However, cognitive/learning styles(CLS) are disputable concepts that are notfully accepted by the whole community.

Figure 1. Hierarchical type tree structure of CS

where FD – field dependent, PA – passiveapproach, GT – global tendency, ED –externally directed, FI – field independent,AA – active approach, AT – analyticaltendency, ID – internally directed.

In most of the systems CLS is assessedthrough psychological questionnaires andpsychometric tests or in the form of self-report. This kind of measures of CLS isbased on subjective judgment users makeabout themselves. For that reason, CLScharacteristics of users are intrinsicallyimprecise. Fuzzy logic and granulationmethods are suggested in Section 4 tohandle this imprecision.

1. NAVIGATION ANDPRESENTATIONPREFERENCES

Navigation preferences of users in respectto cognitive styles characteristics presentedin [5] can be shown in the form:

Figure 2. Navigation preferences

where NP – navigation preference, LO –link ordering, LH – link hiding, AL –adaptive layout, DF – depth firth path, FI –field independent, BF – breadth-firth path,FD – field dependent, RL – rich links, DL– disabled links, AI – alphabetical index,HM – hierarchical map.

Presentation preferences are modesof delivering the content using a variety ofmultimedia techniques such as text,graphics, image, audio, video and etc.



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1. Fuzzy adaptationcognitive/learning styles tonavigation/presentationpreferences

1.1.Fuzzy multilevel granulationof input linguistic variables

Let us introduce linguistic variables [7]CS, FD, PA, GT, ED, FI, AA, AT, ID,associated with the concepts CS, FD, PA,GT, ED, FI, AA, AT, ID, respectively,which are described in Figure 3. Initialmembership functions of fuzzy sets of theselinguistic variables are supposed to be in thetrapezoidal form. The first level ofgranulation applied to the root CS of thetree yields [8]:

Figure 3. Crisp sets for linguistic variable CS

Figure 4. Granulation of CS

The second level of granulation is appliedto the nodes of the tree that are in the secondlevel, for example, for FD we have:

Figure 5. Crisp sets for FD

Figure 6. Granulation of FD

Finally, we granulate the nodes from thethird level through linguistic qualifierspoor, good, and excellent as follows:

Figure 7. Granulation of linguistic variable PA

Next, we can use hedges for the nextgranularity level. Hedges are linguisticmodifiers operated on membershipfunctions. They are expressed by adjectivesand/or adverbs such as very, somewhat,slightly, more or less, quite, extremely,fairly, below and etc. For example, hedgevery applied to qualifier poor modifies itsmembership function as follows:

2)( poorverypoor µµ �

The visual/verbal dimension of LSmodelled by linguistic variables VI/VB,respectively, can also be granulated in thesimilar way.

2.1.Fuzzy multilevel granulationof output linguistic variables

Based on Figure 4 we introduceoutput linguistic variables NP, LO, LH, AL,DF, BF, RL, DL, AI, and HM associatedwith the concepts NP, LO, LH, AL, DF, BF,RL, DL, AI, and HM, respectively.Applying multi level granulation method,similar to the one described above, we canform output membership functions foroutput linguistic variables LO, LH, and ALin the form shown in Figure 8, where for thelinguistic variable LO - A stands for DF, Bstands for BF; for the linguistic variable



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LH - A stands for RL, B stands for DL; andfinally, for the linguistic variable AL - Astands for AI, B stands for HM.

Figure 8. Granulation of linguistic variablesLO, LH, and AL .

Finally, we create one more outputlinguistic variable MMM associated withthe concept presentation preferences andrelate it with the input linguistic variablesVI/VB. The multimedia mode ofpresentation includes text, image, audio,video, graphics, games, animation and anycombination of these elements. Onegranulation option of linguistic variableMMM can be shown as follows:

Figure 9.. Granulation of the linguisticvariableMMM

Again, we can use linguistic qualifiers andmodifiers, as we did with input linguisticvariables, for the next granulation levels.The linguistic qualifiers low, medium,high are used for output linguistic variablesand shown in Table 1.

1.1.Fuzzy inference

Fuzzy inference is a method thatinterprets the values in the input vector and,based on some set of if -then rules, assignsvalues to the output vector.The fuzzypredicates are associated with linguisticterms, and the proposed model is, in fact, aqualitative description of the system usingrules like: IF input linguistic variable CLSis poor THEN output linguistic variableNPP is high. Such models are often calledlinguistic models [9].

More formally, let CLS and NPP arelinguistic variables defined by fuzzy sets onthe universes of discourse that containgranular values described in sections 4.1and 4.2. Denote membership functions of

linguistic variables CLS and NPP by CLSµ

and NPPµ , respectively. Then the adaptationprocess can be characterized by a mapping

NPPCLSf µµ �: . Granulation of

function f is a fuzzy graph that isdescribed as a collection of if-then rules. Afuzzy if-then rule can be defined as a binaryfuzzy relation R considered as a fuzzy setwith membership function:

),( NPPCLSR f µµµ � . Using thecompositional rule of inference, we canformulate the inference procedure in fuzzy

reasoning in the form: NPP�CLS ,R�where the sign � denotes a fuzzycomposition operator, consisting of a t-norm operator, followed by a t-conormoperator. The FIS for the adaptation of CLSto NPP is shown in Table 1. Some examplesof rules at a variety of granulation level arepresented below:

Level 1: IF CLS is FD, THEN NPP is DL;IF CLS is AA , THEN NPP is DF; IF CLSis poor, THEN NPP is high; IF CLS is verypoor, THEN NPP is extremely high.

Level 2: IF FD is good, THEN DL ismedium; IF FI is quite good, THEN AI issomewhat medium; IF VI is excellent,THEN MMM is video; IF VI is good andVB is good, THEN MMM is video andaudio.

Level 3: IF PA is good and AA is good,THEN DF is medium and BF is medium; IFAA is very good, THEN AI is quite high.



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Table1. FIS for adaptation cognitive/learning styles to navigation/presentation preferences with avariety of granulation levels

5. Simulation example

To illustrate the proposed methodology, weconsider constructing fuzzy linguisticmodels using MATLAB simulationsoftware. Mamdani-type FIS [10] is used,

which is well suited to human input, haswidespread acceptance and is intuitive. Letus have a case study in the form:

Linguisticvariables

Type Terms Range

VisualVerbalCognitivestyleMultimodalLinkordering

InputInputInput

OutputOutput

Poor GoodExcellentPoor GoodExcellentFD Mixed FI

Audio Mixed VideoDepth-first MixedBreadth-first

0 – 100 – 100 – 10

0 – 300 – 30



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Sample knowledge rule base is:

1. IF (Visual is excellent) OR (Verbalis poor), THEN (Multimodal isvideo)

2. IF (Visual is good) OR (Verbal isgood), THEN (Multimodal ismixed)

3. IF (Visual is poor) OR (Verbal isexcellent), THEN (Multimodal isvideo)

4. IF (Cognitive style is FD), THEN(Link ordering is breadth-firth)

5. IF (Cognitive style is FI), THEN(Link ordering is depth-firth)

6. IF (Cognitive style is mixed),THEN (Link ordering is mixed)

Simulation of this case study in MATLABgenerates the fuzzy linguistic model withthe following characteristics. The FISstructure information obtained by getfis ( )function from command line is as follows:

>> getfis (a)

Name = multimodal

Type = mamdani

NumInputs = 3

InLabels =

Visual

Verbal

Cognitive style

NumOutputs = 2

OutLabels =

Multimodal

Link Ordering

NumRules = 6

AndMethod = min

OrMethod = max

ImpMethod = min

AggMethod = max

DefuzzMethod = centroid

The following screenshots are FISinformation structure, membershipfunctions of input/output linguisticvariables, behaviour of the model by ruleviewer, and interaction of variables ofvisual and verbal for surface view ofvariable of multimodal.



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Figure 10. FIS structure information diagram

Figure 11. Membership function for linguistic variable visual



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Figure 12. Membership function for linguistic variable verbal

Figure 13. Membership function for linguistic variable cognitive style



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Figure 14. Membership function for linguistic variable multimodal

Figure 15. Membership function for linguistic variable link ordering



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Figure 16. Behaviour of model with rule viewer

Figure 17. Interaction of visual and verbal for surface view multimodal



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More complex models may incorporate a higherlevel of granularities, and yet the nature of themethodology does not change.

7. Results and conclusion

The Fuzzy Logic Toolbox in MATLAB isused to compute fuzzy adaptation of user’snavigation/ presentation preferences tocognitive/learning styles The aim is to observethe behaviour of the proposed model and tuneits parameters such as: input and outputlinguistic terms and their membership functionshapes; relevance and weights of rules; the typeof inference mechanism – Mamdani type max-min composition or Takagi-Sugeno type linearbounded-sum ��; the type of defuzzification(centroid, middle of maximum, largest ofmaximum, and smallest of minimum). TheMamdani type FIS expects the membershipfunctions of output linguistic variables to befuzzy sets and requires the defuzzification stepwhile Takagi-Sugeno type FIS expects outputmembership functions to be singletons. In thisexample, the Mamdani type FIS is choosen. Asa future work, a prototype of the model shall bedeveloped to validate the proposed linguisticmodel within the efficiency of user/systeminteractions in terms of the usability and theutility of the system.

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Simulation of fuzzy adaptation of cognitive/learning