Malvezzi, W.R., Mourao, A.M. and Bressan, G. (2010) Learning Evaluation in Classroom Mediated by Technology Model Using Fuzzy Logic at the University of Amazonas State. Proceeding of 40th ASEE/IEEE Frontiers in Education Conference, Washington DC, 27-30 October 2010, S2C-1-S2C-6.
http://dx.doi.org/10.1109/fie.2010.5673494

has been cited by the following article:

• TITLE: Applications of Clustering Algorithms in Academic Performance Evaluation

  AUTHORS: Jyotirmay Patel, Ramjeet Singh Yadav

  KEYWORDS: Student Allocation Problem, Academic Performance, Fuzzy C-Means, K-Means, Clustering Algorithms

  JOURNAL NAME: Open Access Library Journal, Vol.2 No.8, August 21, 2015

  ABSTRACT: In this paper, we explore the applicability of K-means and Fuzzy C-Means clustering algorithms to student allocation problem that allocates new students to homogenous groups of specified maximum capacity, and analyze effects of such allocations on the academic performance of students. The paper also presents a Fuzzy set and Regression analysis based Dynamic Fuzzy Expert System model which is capable of dealing with imprecision and missing data that is commonly inherited in the student academic performance evaluation. This model automatically converts crisp sets into fuzzy sets by using C-Means clustering algorithm method. The comparative performance analysis indicates that the student group formed by Fuzzy C-Means clustering algorithm performed better than groups formed by K-Means, classical fuzzy logic clustering algorithms and Bayesian classifications.