Using the Combined Model of PROMETHEE and Fuzzy Analytic Network Process for Determining Question Weights in Scientific Exams through Data Mining Approach
Need for an appropriate system of evaluating students-
educational developments is a key problem to achieve the predefined
educational goals. Intensity of the related papers in the last years; that
tries to proof or disproof the necessity and adequacy of the students
assessment; is the corroborator of this matter. Some of these studies
tried to increase the precision of determining question weights in
scientific examinations. But in all of them there has been an attempt
to adjust the initial question weights while the accuracy and precision
of those initial question weights are still under question. Thus In
order to increase the precision of the assessment process of students-
educational development, the present study tries to propose a new
method for determining the initial question weights by considering
the factors of questions like: difficulty, importance and complexity;
and implementing a combined method of PROMETHEE and fuzzy
analytic network process using a data mining approach to improve
the model-s inputs. The result of the implemented case study proves
the development of performance and precision of the proposed
model.
[1] Ibrahim saleh, Seong - in Kim (2008). A fuzzy system for evaluating
students- learning achievement. Expert Systems with Applications, 36
(2009) 6236 - 6243
[2] Bai, S.-M., & Chen, S.-M. (2008a). automatically constructing grade
membership functions of fuzzy rules for students- evaluation. Expert
Systems with Applications, 35(3), 1408-1414
[3] Bai, S.-M., & Chen, S.-M. (2008b). Evaluating students- learning
achievement using fuzzy membership functions and fuzzy rules. Expert
Systems with Applications, 34, 399-410.
[4] Wang, H. Y., & Chen, S. M. (2008). Evaluating students- answer scripts
using fuzzy numbers associated with degrees of confidence. IEEE
Transactions on Fuzzy Systems, 16(2), 403-415.
[5] Biswas, R. (1995). An application of fuzzy sets in students- evaluation.
Fuzzy Sets and Systems, 74(2), 187-194.
[6] Chen, S. M., & Lee, C. H. (1999). New methods for students- evaluating
using fuzzy sets. Fuzzy Sets and Systems, 104(2), 209-218.
[7] Echauz, J. R., & Vachtsevanos, G. J. (1995). Fuzzy grading system.
IEEE Transactions on Education, 38(2), 158-165.
[8] Law, C. K. (1996). Using fuzzy numbers in education grading system.
Fuzzy Sets and Systems, 83(3), 311-323.
[9] Wilson, E., Karr, C. L., & Freeman, L. M. (1998). Flexible, adaptive,
automatic fuzzy-based grade assigning system. In Proceedings of the
North American fuzzy information processing society conference (pp.
334-338).
[10] Ma, J., & Zhou, D. (2000). Fuzzy set approach to the assessment of
student-centered learning. IEEE Transactions on Education, 43(2), 237-
241.
[11] Weon, S., & Kim, J. (2001). Learning achievement evaluation strategy
using fuzzy membership function. In Proceedings of the 31st
ASEE/IEEE frontiers in education conference, Reno, NV (Vol. 1, pp.
19-24).
[12] Weon, S., & Kim, J. (2001). Learning achievement evaluation strategy
using fuzzy membership function. In Proceedings of the 31st
ASEE/IEEE frontiers in education conference, Reno, NV (Vol. 1, pp.
19-24).
[13] Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338-353.
[1] Ibrahim saleh, Seong - in Kim (2008). A fuzzy system for evaluating
students- learning achievement. Expert Systems with Applications, 36
(2009) 6236 - 6243
[2] Bai, S.-M., & Chen, S.-M. (2008a). automatically constructing grade
membership functions of fuzzy rules for students- evaluation. Expert
Systems with Applications, 35(3), 1408-1414
[3] Bai, S.-M., & Chen, S.-M. (2008b). Evaluating students- learning
achievement using fuzzy membership functions and fuzzy rules. Expert
Systems with Applications, 34, 399-410.
[4] Wang, H. Y., & Chen, S. M. (2008). Evaluating students- answer scripts
using fuzzy numbers associated with degrees of confidence. IEEE
Transactions on Fuzzy Systems, 16(2), 403-415.
[5] Biswas, R. (1995). An application of fuzzy sets in students- evaluation.
Fuzzy Sets and Systems, 74(2), 187-194.
[6] Chen, S. M., & Lee, C. H. (1999). New methods for students- evaluating
using fuzzy sets. Fuzzy Sets and Systems, 104(2), 209-218.
[7] Echauz, J. R., & Vachtsevanos, G. J. (1995). Fuzzy grading system.
IEEE Transactions on Education, 38(2), 158-165.
[8] Law, C. K. (1996). Using fuzzy numbers in education grading system.
Fuzzy Sets and Systems, 83(3), 311-323.
[9] Wilson, E., Karr, C. L., & Freeman, L. M. (1998). Flexible, adaptive,
automatic fuzzy-based grade assigning system. In Proceedings of the
North American fuzzy information processing society conference (pp.
334-338).
[10] Ma, J., & Zhou, D. (2000). Fuzzy set approach to the assessment of
student-centered learning. IEEE Transactions on Education, 43(2), 237-
241.
[11] Weon, S., & Kim, J. (2001). Learning achievement evaluation strategy
using fuzzy membership function. In Proceedings of the 31st
ASEE/IEEE frontiers in education conference, Reno, NV (Vol. 1, pp.
19-24).
[12] Weon, S., & Kim, J. (2001). Learning achievement evaluation strategy
using fuzzy membership function. In Proceedings of the 31st
ASEE/IEEE frontiers in education conference, Reno, NV (Vol. 1, pp.
19-24).
[13] Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338-353.
@article{"International Journal of Business, Human and Social Sciences:63049", author = "Hassan Haleh and Amin Ghaffari and Parisa Farahpour", title = "Using the Combined Model of PROMETHEE and Fuzzy Analytic Network Process for Determining Question Weights in Scientific Exams through Data Mining Approach", abstract = "Need for an appropriate system of evaluating students-
educational developments is a key problem to achieve the predefined
educational goals. Intensity of the related papers in the last years; that
tries to proof or disproof the necessity and adequacy of the students
assessment; is the corroborator of this matter. Some of these studies
tried to increase the precision of determining question weights in
scientific examinations. But in all of them there has been an attempt
to adjust the initial question weights while the accuracy and precision
of those initial question weights are still under question. Thus In
order to increase the precision of the assessment process of students-
educational development, the present study tries to propose a new
method for determining the initial question weights by considering
the factors of questions like: difficulty, importance and complexity;
and implementing a combined method of PROMETHEE and fuzzy
analytic network process using a data mining approach to improve
the model-s inputs. The result of the implemented case study proves
the development of performance and precision of the proposed
model.", keywords = "Assessing students, Analytic network process, Clustering, Data mining, Fuzzy sets, Multi-criteria decision making, and Preference function.", volume = "5", number = "11", pages = "1703-5", }