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(IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 8, November 2010 Fuzzy expert system for evaluation of students and online exams 1 2 Mohammed E. Abd-Alazeem Sherief I. Barakat, Computer science department, Information system department, Faculty of computers and information, Faculty of computers and information, Mansoura Egypt, Mansoura ,Egypt, mmandouh_work@yahoo.com sherifiib@yahoo.com Abstract- In this paper we will introduce an expert describes the results of the assessment students and system for evaluation of online exam. We use fuzzy online exams. Section 5 shows the feedback of system for classifying students based on their usage evaluating students and online exams. Section 6 concludes the work and indicates future research data and the final marks obtained in their respective directions. courses. We have used real data from nine Moodle II. Knowledgebase expert system courses with Mansoura University Pharmacy students All needed data is acquired from a teacher and and apply techniques on two hundred students. This stored in a “Knowledge Based” which should be expert system will be able to facilitate education and able to face student training and their up growing problem. [2] We use knowledge base expert system play the role to play the role of virtual intelligent as follow to model knowledge domain “fig.1” teacher referring to student capabilities by following the feedback mechanisms and will evaluate the online exams and questions to measure the difficulty level of exams. The main components of this expert system are Inference Engine, Knowledge Acquisition Facility and Knowledge-base that construct back-end of the system. We realize the model by a fuzzy rule-based expert system with its inference engine that uses various inference methods for education. Figure1. Knowledge base expert system Keywords: Fuzzy rule base, Knowledge base, Inference engine I. INTRODUCTION Global Data Base consists of student state variables, teaching state variables and exam state variables Modern information management systems enable the recording and the management of data • Student state variable (xi): using sophisticated data models and a rich set of x1-Exercise grade for every student management tools. In the context of educational x2-Exam grade for every student systems, the information typically includes details x3- Interesting course about learning material, the tasks and the objectives, x4- Student level the course information, the contact information, the teacher and the student profiles, and the information • Teaching state variable (yi): related to student assignments, the tests, the grades, y1- Difficulty level exam and other records[1]. In this paper, we seek means to model the y2-Teaching content imprecision of information and simplify the access y3-Teaching method to information systems, in terms of fuzzy modeling. The paper is organized as follows. Section 2 y4-Teaching schedule presents the knowledge base of expert system for y5- Degree of course usages student grades assessment Section 3 discuss the components of fuzzy rule based controller. Section 4 y6- Degree of creating motivation. 125 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 8, November 2010 • Exam state variable (zi) A. Fuzzy Inference Process z1- Exam average grade A fuzzy system works similar to a conventional system: it accepts an input value, z2- Exam level performs some calculations, and generates an output Fuzzy logic was primarily designed to represent and value. This process is called the Fuzzy Inference reason with some particular form of knowledge. Process and works in three steps illustrated in Fuzzy logic is powerful problem solving “Fig.3” [5]: methodology with a myriad of applications in • Fuzzification where a crisp input is translated embedded control and information processing. into a fuzzy value. Fuzzy systems are mathematically based systems • Rule Evaluation, where the fuzzy output truth that enable computers to deal with values are computed, and imprecise, ambiguous, or uncertain information and • Defuzzification where the fuzzy output is situations. translated to a crisp value. Fuzzy set theory was proposed in 1965 by Zadeh to help computers reason with uncertain Crisp Input and ambiguous information. Zadeh proposed fuzzy technology as a means to model the uncertainty of natural language [3]. He Fuzzification reasoned that many difficult problems can be expressed much more easily in terms of linguistic variables. Linguistic variables are words and attributes which are used to describe certain aspects of the real world. One Fuzzy Input important feature of linguistic variables is the notion of their utility as an expression of data compression. Zadeh describes this as Rule Evaluation compression granulation. He argues that this is important because it is more general than use of discrete values. This point means that an agent using linguistic variables may be able to Fuzzy Output deal with more continuous and robust descriptions of reality and problem spaces. Our approach is to design a fuzzy rule base system to control training process. Defuzzification III. FUZZY RULE BASED This system is designed for evaluating and teaching the students so that the resulting control Crisp Output system will reliably and safely achieve high Figure3. Fuzzy System Process performance operation. A block diagram of fuzzy system is shown in “Fig.2” Basically in fuzzy control system, there are B. Fuzzification four major stages to accomplish the control process: Fuzzification where a crisp input is translated [4] into a fuzzy value. • Fuzzy input and output variables & their The membership functions defined on the input fuzzy value variables are applied to their actual values to • Fuzzy rule base determining the degree of truth. • Fuzzy inference engine For example for the fuzzification crisp inputs, x1 • Fuzzification and defuzzification modules and y1 and determine the degree to which these inputs belong to each of the appropriate fuzzy sets (Figure 3). At first it gets inputs and then fuzzifies them. After fuzzification, make decision through fuzzy inference engine according to fuzzy rule based system. C. The Fuzzy Inference engine fuzzy rule based: This is an interface for fuzzifying the user- requested parameters of the test items. The fuzzified parameters, along with a set of fuzzy rules, are then sent to an expert system to perform the inference Figure2. Fuzzy System process. . 126 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 ournal of Compu Science and Information Sec (IJCSIS) International Jo uter d curity, No. er Vol. 8, N 8, Novembe 2010 D. Defuz zzification nd hen ult R9: If z1 is low an y1 is low Th z2 is difficu Defu onverting outpu uzzification is a process of co ut ariable into a un fuzzy va nique number. BLE1. Fuzzy R TAB m Rules for exam evaluation Defuzzi ss ification proces has the capa ce ability to reduc L Low dium Med High s to a fuzzy set into a crisp single-valued quantity or int set; t rix a crisp s to convert a fuzzy matr into a cris sp Low L Diff fficult Diff ficult Mod derate ber matrix; or to convert a fuzzy numb into a cris sp number. [6] edium Me Diff fficult derate Mod asy Ea IV. RIMENTS AN RESULTS EXPR ND A. Evaluating an online exam high h Mod derate Mod derate Ea asy f We consider two fuzzy input va m ariables as exam average grade z1 (Figg4.a) and diff of ficulty level o exam y1 (Fig4.b). And the output will be the exam 1 d w m logy" course: For Exam "Cyptol 2). ip f level (z2 Membershi function of z1, y1 and z z2 1- Exam averag grade for all students ( ge (exam be 0 should b as follows (0 ≤ µ≤ 1).[7] ted grade) is calculat as: Fig 4-a (Sum of student grades)/no of students ts o xam rade = 66% Ex average gr ulty 2- Exam difficu level ollowing form Fo culating the Exam mula for calc Diifficulty: [8] Wheere ed fficulty, denote the item dif enoted de xaminees that answered the item the number of ex e corr rectly, and denoted th total numb of he ber Fig 4-b minees. Exam difficulty level = 62% exam l y n p Firs we can apply fuzzification where a crisp input st is translated into a fuzzy value, angle Membe By applying Tria on ership Functio for “Fig 4.a” g (z1) = 60% µA( 0% µA(y1) = 20 µB(z1) = 20% 0% µB(y1) = 80 a ence mechanism By applying infere m e4. ip Figure Membershi function of e exam average A(x1) = 60% a µA(y1) = 2 if µA and 20% then d vel grade (z1) and difficulty lev (y1) (z2) = 20% µA( ule valuating exam is designed a Fuzzy ru base for ev m as A(z1) = 60% a µB(y1) = 8 if µA and 80% then follows: (z2) = 60% µA( 1 R1: If z1 is high and y1 is high Then z2 is easy B(x1) = 40% a µA(y1) = 2 if µB and 20% then z1 d um R2: If z is high and y1 is mediu Then z2 i is (z2) = 20% µA( moderate e 1 z2 R3: If z1 is high and y1 is low Then z is moderate B(z1) = 40% a µB(y1) = 80% then if µB and 1 nd hen R4: If z1 is medium an y1 is high Th z2 is easy (z2) = 40% µA( and R5: If z1 is medium a y1 is med dium Then z2 iis moderate e valuation, wher the fuzzy o ond: Rule Ev Seco re output z m ow R6: If z1 is medium and y1 is lo Then z2 i is h omputed. truth values are co difficult R7: If z1 is low and y1 is high Then z is moderate 1 z2 cording to fuzzy based rule, we find Acc y w z d um R8: If z1 is low and y1 is mediu Then z2 i is 0% ∆z2 = µA(z2) = 20 "Easy" Rule 1 difficult 127 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 8, November 2010 ∆z2 = µB(z2) = 60% "Moderate" Rule 2 Fuzzy rule base for evaluating student is designed as follows: Third: We will apply defuzzification where the R1: If x1 is low and y1 is low Then x4 is fail fuzzy output is translated to a crisp value[9] shown R2: If x1 is low and y1 is medium Then x4 is fail in (figure 5). R3: If x1 is low and y1 is high Then x4 is pass The center of gravity is calculated as follow: R4: If x1 is low and y1 is very high Then x4 is pass µA . , COG = = = 62.5 µA . . R5: If x1 is medium and y1 is low Then x4 is fail R6: If x1 is medium and y1 is medium Then x4 is pass R7: If x1 is medium and y1 is high Then x4 is good R8: If x1 is medium and y1 is very high Then x4 is good R9: If x1 is high and y1 is low Then x4 is pass R10: If x1 is high and y1 is medium Then x4 is pass R11 If x1 is high and y1 is high Then x4 is good R12: If x1 is high and y1 is very high Then x4 is excellent R13: If x1 is very high and y1 is low Then x4 is pass R14: If x1 is very high and y1 is medium Then x4 is Figure5. Defuzzification Result for Exam good Evaluation R15: If x1 is very high and y1 is high Then x4 is excellent Then the exam level is "Moderate". B. Evaluating students R16: If x1 is very high and y1 is very high Then x4 is excellent We consider two fuzzy input variables as exam TABLE 2. Fuzzy Rules for student evaluation grade x3 (figure 6.a) and difficulty level of exam y1 Low Medium High Very High (figure 6.b) and the output will be the student level (x4). Membership function of x3, y1 and x4 should Low Fail Fail Pass Pass be as follows (0 ≤ µ≤ 1). Mediu Fail Pass Good Good Fig 6-a m Low Moderate High Very High 1 high Pass Pass Good Excellent Very Pass Good Excellent Excellent High 0 20 40 60 80 100 For student "student ID 1008": Fig 6-b 1- Exam grade for this student is : Low Moderate High Very High Student grade = 77% 1 2-Exam difficulty level Exam difficulty level = 62% First we can apply fuzzification where a crisp input is translated into a fuzzy value, By applying Triangle Membership Function for Fig 6-a 0 20 40 60 80 100 µA(x1) = 10% µA (y1) = 85% Figure 6. Membership function of student grade µB(x1) = 90% µB (y1) = 15% (x1) and difficulty level (y1) By applying inference mechanism if µA(x1) = 10% and µA(y1) = 85% then µA(x4) = 10% . 128 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 8, November 2010 if µA(x1) = 10% and µB(y1) = 15% then TABLE 4. Results for student evaluation µA(x4) = 10% if µB(x1) = 90% and µA(y1) = 85% then Student ID Grade Level µA(x4) = 85% 1006 63% Good if µB(x1) = 90% and µB(y1) = 15% then µA(z2) = 15% 1007 87% Excellent 1008 77% Excellent Second: Rule Evaluation, where the fuzzy output truth values are computed. 1009 52% Pass According to fuzzy based rule, we can use 1011 38% Fail ∆x4 = µA(x4) = 10% "Excellent" Rule 16 ∆x4 = µB(x4) = 85% "Excellent" Rule 12 1014 83% Excellent 1052 60% Good Third: We will apply defuzzification where the fuzzy output is translated to a crisp value. 1062 30% Fail COG = 78 Then the student level is "Excellent". CONCOLUSION V. FEEDBACK FOR EVALUATING Fuzzy expert system and fuzzy rule based is a great SRUDENTS AND ONLINE EXAM step forward for the adaptation of the accessible knowledge for the student according to the feedback We can classify exams according to our obtain from the evaluating system. expert system in to 3 levels: Easy, Moderate and It's also considered a good reference for instructor to Difficult.[10] Then we have exam store for evaluate the exam level and the quality assurance Pharmacy students for Mansoura University so we organization is benefit from this evaluation. can evaluate this exams and give the feedback to the instructor to be a good reference for exam evaluation, so the results is as follow in Table[3] : ACKNOWLEDGMENT First of all, I thank Allah for achieving this paper and giving me the ability to finish it. Second, I TABLE3. Results for exam evaluation would like to express my appreciation to my Course Level supervisor Dr. Sherief Barakat for his continuous support and encouragement during the research Cartilage and Bone Online Exam Difficult study in this thesis. He really influenced my way of Cytology for Clinical Pharmacy Exam Difficult thinking and developing the research ideas adopted in this thesis. I am very grateful for his effort and Group I Online Exam of Immune Easy his highly useful advice throughout the development System of this work. REFRENCES CVS Online Exam Moderate [1] Henry Nasution, "Design methodology of fuzzy Urinary, Male, and Female Online Moderate logic control", Journal Teknos-2k, Universitas Bung Hatta, Vol.2, No.2, December (2002). Exam [2] Ishiburchi, H., Nozaki, K., and Tanaka, H. Online Exam of Muscular Tissue Easy “Distributed Representation of Fuzzy Rules and Its Application to Pattern Classification. Fuzzy Sets Modifications, Glands & CT Exam Difficult and Systems”, Vol. 52,pp. 21-32. 1992. Med-Term Exam for Clinical Moderate [3] Zadeh, L. A. “Fuzzy sets. Information and Control”, Vol. 8, pp. 338-353. 1965. Pharmacy [4] Takagi,T. and Sugeon, "Fuzzy identification of Second Med-Term Exam for CP Difficult System and Its Applications to Modeling and Control", vol. 15, no. 1, 116-132, 1985. [5] GAO Xinbo (1) XIE Weixin(2)," Advances in theory and applications of fuzzy clustering", The student assessment is very important because a Institute of Electronic Engineering, China, 2000. good assessment let the instructor to have a correct [6] H.Bevrani, "Defuzzification", University of decision for student follow up. Kurdistan Department of Electrical & Computer Eng, Spring Semester, 2009. We classify student in to 4 levels: fail, pass, good [7] J. Harris ,"Fuzzy Logic Applications in and excellent. [11] So according to "Cyptology" Engineering Science", vol 29,2003. Course the students level are as shown in Table [8] S. J. Osterlind, "Constructing Test Items: Multiple-choice, Constructed-response, . 129 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 8, November 2010 Performance, and Other Formats", London, United Kingdom: Springer, 1998. [9] Sudarshan, Pavankiran, Swetha Krishnan and G Raghurama, "Fuzzy Logic Approach for Replacement Policy in Web Caching", Indian, December 2005, ISBN: 0-9727412-1-6, pp 2308- 2319. [10] Arriaga, F. de, Alami, M. El., & Arriaga, A, "Evaluation of Fuzzy Intelligent Learning Systems". Spain, November 2005. [11] Nykänen, "Inducing Fuzzy Models for or Student Classification". Educational Technology & Society, vol 2, pp 223-234, 2006. . 130 http://sites.google.com/site/ijcsis/ ISSN 1947-5500

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