Fuzzy descriptive evaluation system: real, complete and fair evaluation of students

In recent years, descriptive evaluation has been introduced as a new model for educational evaluation of Iranian students. The current descriptive evaluation method is based on four-valued logic. Assessing all students with only four values is led to a lack of relative justice and creation of unrealistic equality. Also, the complexity of the evaluation process in the current method increases teacher error’s likelihood. As a suitable solution, in this paper, a fuzzy descriptive evaluation system has been proposed. The proposed method is based on fuzzy logic, which is an infinite-valued logic, and it can perform approximate reasoning on natural language propositions. By the proposed fuzzy system, student assessment is performed over the school year with infinite values instead of four values. In order to eliminate the diversity of assigned values to students, at the end of the school year, the calculated values for each student will be rounded to the nearest value of the four standard values of the current descriptive evaluation method. It can be implemented in an appropriate smartphone application, which makes it much easier for teachers to assess the educational process of students. In this paper, the evaluation process of the elementary third-grade mathematics course in Iran during the period from the beginning of the MEHR (the seventh month of Iran) to the end of BAHMAN (the eleventh month of Iran) is examined by the proposed system. To evaluate the validity of this system, the proposed method has been simulated in MATLAB software.

[1]  M. Newman,et al.  Hierarchical structure and the prediction of missing links in networks , 2008, Nature.

[2]  J. W. Bakal,et al.  Students' performance evaluation using fuzzy logic , 2012, 2012 Nirma University International Conference on Engineering (NUiCONE).

[3]  Hans-Jürgen Zimmermann,et al.  Fuzzy Set Theory - and Its Applications , 1985 .

[4]  A. Fevzi Baba,et al.  Evaluation of student performance in laboratory applications using fuzzy decision support system model , 2014, 2014 IEEE Global Engineering Education Conference (EDUCON).

[5]  Jian Ma,et al.  Fuzzy set approach to the assessment of student-centered learning , 2000, IEEE Trans. Educ..

[6]  Piet Hut,et al.  A hierarchical O(N log N) force-calculation algorithm , 1986, Nature.

[7]  Li-Xin Wang,et al.  A Course In Fuzzy Systems and Control , 1996 .

[8]  Adel Hatami-Marbini,et al.  An extension of fuzzy TOPSIS for a group decision making with an application to tehran stock exchange , 2017, Appl. Soft Comput..

[9]  Henrique O'Neill,et al.  Team Performance Evaluation Using Fuzzy Logic , 2011, WILF.

[10]  Siu Kai Choy,et al.  Fuzzy model-based clustering and its application in image segmentation , 2017, Pattern Recognit..

[11]  Zhiwei Gao,et al.  Takagi–Sugeno Fuzzy Model Based Fault Estimation and Signal Compensation With Application to Wind Turbines , 2017, IEEE Transactions on Industrial Electronics.

[12]  G. Jyothi,et al.  Fuzzy Expert Model for Evaluation of Faculty Performance in Technical Educational Institutions , 2014 .

[13]  Ibrahim A. Hameed,et al.  An Interval Type-2 Fuzzy Logic System for Assessment of Students' Answer Scripts under High Levels of Uncertainty , 2016, CSEDU.

[14]  Nadia Naghavi,et al.  Nonlinear identification of IPMC actuators based on ANFIS–NARX paradigm , 2014 .

[15]  Nadia Naghavi,et al.  From modeling to implementation of a method for restraining back relaxation in ionic polymer–metal composite soft actuators , 2018, Journal of Intelligent Material Systems and Structures.

[16]  A. Dale,et al.  Hierarchical Genetic Organization of Human Cortical Surface Area , 2012, Science.

[17]  Vijendra Pratap Singh,et al.  Modeling Academic Performance Evaluation Using Soft Computing Techniques: A Fuzzy Logic Approach , 2011 .

[18]  Saurabh Pal,et al.  A study of academic performance evaluation using Fuzzy Logic techniques , 2014, 2014 International Conference on Computing for Sustainable Global Development (INDIACom).

[19]  T. Vicsek,et al.  Hierarchical group dynamics in pigeon flocks , 2010, Nature.

[20]  Khairul A. Rasmani,et al.  Data-driven fuzzy rule generation and its application for student academic performance evaluation , 2006, Applied Intelligence.

[21]  Prasun Das,et al.  Designing a fuzzy approach for modelling the performance evaluation of education service providers , 2017 .

[22]  L. Pangaro Investing in descriptive evaluation: a vision for the future of assessment , 2000, Medical teacher.

[23]  D. J. Felleman,et al.  Distributed hierarchical processing in the primate cerebral cortex. , 1991, Cerebral cortex.

[24]  N. Naghavi,et al.  Non-uniform deformation and curvature identification of ionic polymer metal composite actuators , 2015 .

[25]  G. Vachtsevanos,et al.  Fuzzy Grading System , 1995 .

[26]  Nadia Naghavi,et al.  Restraining IPMC Back Relaxation in Large Bending Displacements: Applying Non-Feedback Local Gaussian Disturbance by Patterned Electrodes , 2016, IEEE Transactions on Electron Devices.

[27]  Mahdi Saadatmand-Tarzjan,et al.  A New Threshold Selection Method Based on Fuzzy Expert Systems for Separating Text from the Background of Document Images , 2018, Iranian Journal of Science and Technology, Transactions of Electrical Engineering.