Design of an interactive Web-based e-learning course with simulation lab: a case study of a fuzzy expert system course

Transcription

1 World Transactons on Engneerng and Technology Educaton Vol.8, No.3, WIETE Desgn of an nteractve Web-based e-learnng course wth smulaton lab: a case study of a fuzzy expert system course Che-Chern Ln, Ja-Fe Ln, Yu-Chuan Ln, Fan-Cheh Ta, Chen-Yn Wang, Chao-Yun Chen & Yu-Ju Ln* Natonal Kaohsung Normal Unversty, Kaohsung, Tawan Meho Unversty, Pngtung, Tawan Fortune Insttute of Technology, Kaohsung, Tawan* ABSTRACT: A four-week e-learnng course on desgn archtecture, combnng mult-meda learnng materals and a smulaton lab for fuzzy expert systems, s presented n ths artcle. The learnng materals of the course nclude html pages, mages, flash fles, Java applets and nstructonal vdeos. A smulaton lab desgned wth java programmng language s also employed for the students to buld ther fuzzy expert applcatons based on the scenaros they create. Sx core concepts of the fuzzy expert systems are ncluded n ths study: 1) membershp functon; 2) fuzzfcaton; 3) fuzzy rule; 4) fuzzy nference; 5) defuzzfcaton; and 6) fuzzy expert applcaton. The proposed course desgn s sutable for the courses related to fuzzy expert systems for unversty junor students, such as artfcal ntellgence, fuzzy systems, busness ntellgence, artfcal methods n fnance, etc. INTRODUCTION Multmeda learnng materals have been used n e-learnng for decades. Moreno and Mayer dscussed the prncples of multmeda learnng form the vewpont of the role of modalty and contguty [1]. To mprove learnng effcences, some prevous studes dscussed how to make the cogntve loadngs sutable for learners. An early study on reducng cogntve load by mxng audtory and vsual presentaton modes has been proposed by Mousav et al [2]. Gerjets and Scheter presented the goals and strateges relatng to nstructonal desgn and cogntve load, whch focused on hypertext t-based learnng [3]. Brunken et al provded a computer-based drect measurement of cogntve load n multmeda learnng [4]. Mayer and Moreno proposed nne ways to reduce cogntve load for multmeda course desgn [5]. Paas et al dscussed recent developments n cogntve theory and nstructonal desgn [6]. Moreno and Mayer studed the effect of anmaton n multmeda learnng [7]. Sweller et al concluded that dfferent levels of learners experences should be taken nto consderaton when desgnng multmeda courses [8]. Leahy et al conducted two experments to dscuss the approprate tmng to add audtory presentaton n multmeda nstructon [9]. Over past decades, due to the rapd development of computer technologes, ncludng software and broad networkng technologes, a large porton of learnng actvtes are conducted va the Internet. Wthout the lmtatons of tme and dstance, e-learnng, therefore, can play a very mportant role n daly learnng actvtes. Many computer-based learnng materal desgn tools were also developed to produce learnng materal. Popular learnng materal desgn tools nclude Flash, Drector, Blaze Meda Pro, MedaBlender and Dazzler. Further, some computer programmng languages provde graphcal tools for programmers to desgn vsual dsplays. Java applets are one of the most popular Web technologes used to buld vsualsaton components. Wth Java applets, course desgners easly can create graphcally nteractve learnng materals. In addton, one of the benefts of usng Java applets s that they are portable and can be ntegrated wth Java technologes. Ths makes t possble to combne computer programmng and learnng materal desgn. After completng a course desgn, nstructors need to put the learnng materals on an e-learnng platform. Most of the Webbased learnng platforms are bult for general purpose use. Examples nclude Moodle, ATutor, Clarolne, Dokeos, efront, LAMS and Fle3. It s somewhat dffcult to customse a well-bult e-learnng platform accordng to a user s specal needs. For example, to buld a smulaton lab for learners to smulate the scenaro-based learnng envronment, the course desgner may need to use some computer programmng technques. Java technologes provde good tools for desgnng graphcally nteractve learnng materals and buldng computer-aded smulaton labs. Artfcal ntellgence has been appled to dfferent areas, such as busness, engneerng, scence and medcne. Several technques of artfcal ntellgence are often used, ncludng neural network, genetc algorthm, fuzzy expert systems (FES) and data mnng. Ths study focuses on FES. A course desgn archtecture combnng mult-meda learnng materals and a smulaton lab for a course about FES s proposed. The course desgn from several aspects, ncludng learnng topcs and objectves, learnng materals, smulaton lab, learnng sequences and suggested learnng schedule, 283

2 wll be descrbed. Ths study uses Java technologes to buld an e-learnng platform for a FES course where Java applets are used to generate nteractve vsual components. Meanwhle, html pages, mages and flash fles are also adopted to produce learnng materal. In addton, a smulaton lab desgned, usng the java programmng language, s employed for the students to set up ther FES applcatons based on scenaros they create. The course desgned n ths study can be used as a 4-week lecture seres sutable for courses related to FES, such as artfcal ntellgence, fuzzy systems, busness ntellgence and artfcal methods n fnance. BACKGROUND ON THE FUZZY EXPERT SYSTEM Founded by Zadeh, fuzzy logc has been wdely used to defne varables whch have uncertan values. A tradtonal crsp set employs a bnary value (0 or 1) to represent whether an element belongs to the crsp set. In a fuzzy set, on the other hand, a value μ (0 μ 1) s used for an element to descrbe the degree or amount of partcpaton of an element n the fuzzy set. The process of fuzzfcaton s used to determne the value of μ for an element. Membershp functons descrbe the mappngs of nputs to outputs for a fuzzy set. Smlar to the thnkng process of human bengs, FES uses fuzzy rules to solve problems. Fuzzy rules are consttuted as an IF-THEN structure and often are obtaned from human doman experts. To obtan the outputs, a fuzzy nference process s employed to determne the fuzzy conclusons (fuzzy outputs) by frng the fuzzy rules. However, n the real world, crsp values are requred as a soluton. A defuzzfcaton procedure s used to obtan crsp outputs from the fuzzy conclusons. Below, FES s brefly descrbed to gve readers the necessary background. Membershp Functons and Fuzzfcaton A trangular membershp functon s the smplest used n fuzzy logc. A trangular membershp functon s defned by three parameters: a, b, and c, as follows [10]: Trangular ( x) = 0 ( x a)/( b a) ( c x)/ c b 0 x < a a x < b b x < c x c (1) Fgure 1: Example of membershp functon and fuzzfcaton. Another popular membershp functon s the trapezodal functon, usually specfed by four parameters [10]. Consder a washng machne wth a fuzzy varable of washng cycle. Fgure 1 shows a trangular membershp functon to descrbe the fuzzy expresson of washng cycle s medum, where a = 10 (mnutes), b = 14 (mnutes) and c = 18 (mnutes). From Fgure 1, the matchng degree (μ) of the nput 12 mnutes s 0.5. Fgure 1 demonstrates the fuzzy expresson of washng cycle s medum, but n most cases, a washng cycle mght have several fuzzy expressons, such as washng cycle s long, washng cycle s short. It s convenent to defne dfferent fuzzy expressons wth fuzzy levels, e.g. washng cycle = {long, medum, long} where washng cycle s a fuzzy dmenson and {long, medum, long} s the set of the fuzzy levels assocated wth washng cycle. Fuzzy Rules A fuzzy rule conssts of two parts: an f-part (antecedent) and a then-part (consequent) as follows: IF <antecedent> THEN <consequent>s Mathematcally, a fuzzy rule s defned by: IF X 1 s p X 1 AND X 2 s q X 2 AND X n s u X n THEN Y s Y v where X s the th j fuzzy nput dmenson and X s the j th fuzzy level n the fuzzy level set assocated wth X. Y s the fuzzy output dmenson and Y v s the v th fuzzy level n the fuzzy level set assocated wth Y. Fuzzy Inference (Mn-Mn-Max Method) A fuzzy nference has the followng three steps: Step 1: Determne the mnmum of the matchng degrees: Consder a fuzzy rule wth n nput varables X 1, X 2,, X n. Suppose the nput data for these varables arex 1 = x 1, X 2 = x 2,, X n = x n. The overall matchng degree µ of the fuzzy rule s obtaned by the followng formula [10]: X 284

3 µ X = mn { µ X ( x ), µ X ( x2 ),, µ X ( x )} (2) n n where µ X ( x ) s the matchng degree of x assocated wth X. Step 2: Performng α-cut: Manly, two nference methods are used n fuzzy expert systems: the clppng method and the scalng method. The clppng method s used n ths study. Fgure 2 demonstrates a conceptual dagram of the clppng method. Fgure 2: Example of an α-cut (the clppng method). Step 3: Determne the overall fuzzy concluson by takng the unon of ndvdual fuzzy conclusons: If a fuzzy rule s fred by the nput data, we get a sngle fuzzy concluson for ths fred fuzzy rule. For convenence, a fuzzy concluson conducted from a sngle fuzzy rule s called a rule-level fuzzy concluson. Normally, n a fuzzy expert system, there are multple fuzzy rules fred by the nput data. A system-level fuzzy concluson s obtaned by takng the unon operatons on all of the rule-level fuzzy conclusons conducted from all of the fred fuzzy rules. As mentoned, n the real world crsp values mght be needed to solve a problem. Therefore, the fnal step for a FES s called defuzzfcaton, whch generates a crsp value from a system-level fuzzy concluson. Defuzzfcaton The most popular defuzzfcaton method s the Centre of Area (COA) method. It fnds the centre of a system-level fuzzy concluson and uses the centre as the defuzzfed value. The COA method computes the weghted average over an entre system-level fuzzy concluson set. The COA defuzzfcaton method s gven by the followng formula [10]: def COA µ v ( y) y Y = µ ( y ) v Y (3) def s the defuzzfed value, and ) where µ ( COA v y Y s the matchng degree of dscrete nput y assocated wth Y v. Another defuzzfcatn method s the Mean of Maxmum (MOM) method. The computatonal detals of ths method can be found n the publcaton by Yen and Langar [10]. Ths study uses the COA method as the defuzzfcaton method. Fgure 3 shows the conceptual dagrams of the entre procedure of FES. Fgure 3: Procedure of an entre fuzzy expert system procedure [10]. 285

4 COURSE DESIGN There are two parts n the FES course versus learnng about FES and developng FES applcatons usng the smulaton lab. The course desgn, from several aspects, s descrbed n ths artcle, ncludng learnng topcs and objectves, learnng materals, smulaton lab, learnng sequences and suggested learnng schedule. Learnng topcs and objectves of the FES course nclude: 1. Membershp functon: How to use a membershp functon to descrbe a problem wth uncertanty. 2. Fuzzfcaton: how to get a matchng degree (a fuzzfed value) from a crsp value usng a membershp functon. 3. Fuzzy rule: Generate fuzzy rules based on well-defned membershp functons. 4. Fuzzy nference: Understand the fuzzy nference prncple and be famlar wth the fuzzy nference processes, ncludng fndng the mnmum matchng degree, α-cut clppng and unon of fuzzy conclusons. 5. Defuzzfcaton: Understand the defuzzfcaton prncple and be famlar wth the defuzzfcaton process. 6. FES applcaton: How to buld FES applcatons wth the smulaton lab to solve real-world problems. The learnng materals nclude: 1. HTML pages wth mages. 2. Anmatons: usng Flash. 3. Interactve learnng materals: usng Java applets. 4. Vdeo: recorded by nstructor. 5. Smulaton lab: mplemented by the Java programmng language. The smulaton lab allows users to: 1. Generate and edt fuzzy varables wth approprate membershp functons. 2. Generate and edt fuzzy rules wth well-defned fuzzy varables. 3. Enter crsp nput values, 4. Derve fnal defuzzfed crsp outputs. Suggested sequences of learnng actvtes s: 1. Read the html pages and Flash. 2. Watch the Flash-based anmatons (usng vdeo, f necessary). 3. Conduct nteractve actvtes wth nteractve learnng materals mplemented by Java applets. 4. Use the smulaton lab to create fuzzy expert system applcaton. Suggested learnng schedule s: Week 1: Introducton (hstory of fuzzy logc, basc concept of fuzzy logc, examples of FES applcatons), membershp functons and fuzzfcaton. Week 2: Fuzzy nference method and the defuzzfcaton procedure. Week 3: Implement fuzzy expert applcaton project. Week 4: Project demonstraton and concludng revew on the project. Fgure 4: An overall dagram of the FES course desgn. Fgure 4 shows an overall dagram of the FES course desgn. Fgure 5 shows selected examples of nteractve learnng materals mplemented by Java applets. In Fgure 5(a), a learner can defne a trangle membershp functon (or a 286

5 trapezod membershp case) by enterng the three parameters a, b and c (Eq. (1) on nput boxes a, b and c. The membershp functon s then vsually generated on the screen. If the learner enters a crsp nput value on the nput box x, the applet calculates the matchng degree μ assocated wth the crsp value and dsplays t on the screen. In addton, the learner can also use a horzontal scroll bar to locate the crsp nput value by pressng-and-shftng the bar. Fgure 5 (b) demonstrates a fuzzy nference process wth an α-cut clppng. Fgure 5: Examples of nteractve learnng materals mplemented by Java applets. In Fgure 5(b), a learner can use two horzontal scroll bars to locate two crsp nputs and the applet wll generate two matchng degrees (μ 1 and μ 2 ) assocated wth the two nputs. The two matchng degree wll be shown on the screen. The mnmum of μ 1 andμ 2 s then calculated by the applet, whch s the value of the α-cut to clp the fuzzy output varable. The fuzzy concluson s produced by the fuzzy nference (the α-cut clppng). Fgure 5(c) shows the unon of two fuzzy conclusons. In Fgure 5(c), a learner can use two vertcal scroll bars to manpulate two α-cuts to smulate two fuzzy rules fred by the α-cuts. The applet wll then react to the learner wth two fuzzy conclusons assocated wth the two α-cuts. If the learner clcks on the Unon button, the applet takes a unon operaton on the two fuzzy conclusons, and shows the result (the unon of the fuzzy conclusons) on the screen. Wth the nteractve learnng materals done by Java applets, learners mght have more understandng of the ndvdual step-by-step operatons of fuzzy expert systems. CONCLUSIONS An e-learnng course desgn archtecture was proposed n ths artcle, combnng mult-meda learnng materals and a smulaton lab for a course on fuzzy expert systems. Introduced frst was the background of fuzzy expert systems and then the desgn of the course was descrbed from dfferent aspects. Ths ncluded learnng topcs and objectves, learnng materals, smulaton lab, learnng sequence, and learnng schedule. The course desgn proposed n ths study can be used as part of artfcal ntellgence courses. The learnng materals of the course nclude html pages, mages, Flash fles, Java applets and nstructonal vdeos. In addton, a smulaton lab s 287

6 also used for students to set up ther fuzzy expert systems based on the scenaros they create. Sx core concepts of the fuzzy expert systems are ncluded n ths study: 1) membershp functon; 2) fuzzfcaton; 3) fuzzy rule; 4) fuzzy nference; 5) defuzzfcaton; and 6) fuzzy expert applcaton. The course can be used as a 4-week lecture course for fuzzy expert systems. The smulaton lab can be used n courses related to FES for dfferent majorng students, such as engneerng, busness admnstraton, fnance, and educaton. Instructors of FES can encourage the students to buld ther FES applcatons based on the students majors. Through qualtatve research, varous usages of FES applcatons among dfferent majorng students could be explored. Ths mght be an nterestng topc for future studes. REFERENCES 1. Moreno, R. and Mayer, R.E., Cogntve prncple of multmeda learnng: the role of modalty and contguty. J. of Educatonal Psychology, 91, 2, (1999). 2. Mousav, S.Y., Low, R. and Sweller, J., Reducng cogntve load by mxng audtory and vsual presentaton modes. J. of Educatonal Psychology, 87, 2, (1995). 3. Gerjets, P. and Scheter, K., Goal confguratons and processng strateges as moderators between nstructonal desgn and cogntve load: evdence from hypertext-based nstructon. Educatonal Psychologst, 38, 1, (2003). 4. Brunken, R., Plass, J.L. and Leutner, D., Drect measurement of cogntve load n multmeda learnng. Educatonal Psychology, 38, 1, (2003). 5. Mayer, R.E. and Moreno, R., Nne ways to reduce cogntve load n multmeda learnng. Educatonal Psychologst, 38, 1, (2003). 6. Paas, F., Renkl, A. and Sweller, J., Cogntve load theory and nstructonal desgn: recent developments. Educatonal Psychology, 38, 1, 1-4 (2003). 7. Mayer, R.E. and Moreno, R., Anmaton as an ad to multmeda learnng. Educatonal Psychology Revew, 14, 1, (2002). 8. Kalyuga, S., Chandler, P. and Sweller, J., Incorporatng learner experence nto the desgn of multmeda nstructon. J. of Educatonal Psychology, 92, 1, (2000). 9. Leahy, W., Chandler, P. and Sweller, J., When audtory presentatons should and should not be a component of multmeda nstructon. Appled Cogntve Psychology, 17, (2003). 10. Yen, J. and Langar, R., Fuzzy Logc: Intellgence, Control, and Informaton, New Jersey, USA: Prentce-Hall, Inc., Chapters 2 and 3 (1999). 288