I.J.Modern Education and Computer Science, 2012, 7, 42-49 Published Online July 2012 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijmecs.2012.07.06 Copyright © 2012 MECS I.J. Modern Education and Computer Science, 2012, 7, 42-49 Students Classification With Adaptive Neuro Fuzzy Mohammad Saber Iraji Department of Computer science, Young Researchers Club sari Branch, Islamic Azad University, sari, Iran E-mail: iraji.ms@Gmail.com Majid Aboutalebi Department of Computer Engineering , Islamic Azad University, Sari Branch, Sari, Iran E-mail: Aboutalebi@iausari.ac.ir Naghi. R. Seyedaghaee Department of Computer Engineering, Aliabad Katoul Branch, Islamic Azad University,Aliabad Katoul, Iran E-mail: Sn_seyedaghaee@yahoo.com Azam Tosinia Department of Computer Engineering, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran E-mail: azam.tosi@Gmail.com Abstract— Identifying exceptional students for scholarships is an essential part of the admissions process in undergraduate and postgraduate institutions, and identifying weak students who are likely to fail is also important for allocating limited tutoring resources. In this article, we have tried to design an intelligent system which can separate and classify student according to learning factor and performance. a system is proposed through Lvq networks methods, anfis method to separate these student on learning factor . In our proposed system, adaptive fuzzy neural network(anfis) has less error and can be used as an effective alternative system for classifying students. Index Terms — Adaptive neuro fuzzy, Neural network, Students classification, Lvq I. I NTRODUCTION Predicting students‟ academic performance is critical for educational institutions because strategic programs can be planned in improving or maintaining students‟ performance during their period of studies in the institutions[1]. Arithmetical and statistical methods are unable to offer an effective inference procedure to perform the evaluation of the academic performances of students in a more natural way, using linguistic variables. This method might help students, their parents, decision makers, and evaluators in obtaining more reliable and understandable results for a student‟s achievement, or for a group of students and their comparative evaluations. It is important to point out that the aim of proposed method is not to replace the traditional method of evaluation; instead, it is to strengthen the present system by providing additional information for decision making [2]. Assessment of the student‟s academic performance (SAP) is one of the most important practices used for three main reasons: to decide on pass and failure in courses, to obtain an indication of the student‟s level of learning, and to provide information on the effectiveness of teaching. In traditional (statistical) methods, the student‟s academic performance (SAP) is evaluated based on the marks collected by a student. It can be classified into numerous categories such as single numerical scores usually referring to 100 percent, single letter grades (e.g. A, B, C, D, or F), nominal scores (e.g. 1, 2, 3. . .10), linguistic terms such as „„Fail”, or „„Pass” or single grade- points from 0.00 to 4.00. As a part of this study, a weighted sum of assessment tools is used to calculate the numerical score of each student as follows: Quiz (Q) is 10%, Major (M) is 15%, Midterm (MD) is 20%, Final (F) is 40%, Performance Appraisals (P) is 10%, and Survey (S) is 5%. The total out of 100 indicates the student‟s academic performance(SAP)[2]. A number of socio-economic, biological, environmental, academic, and other related factors that are considered to have influence on the performance of a university student were identified. These factors were carefully studied and harmonized into a manageable number suitable for computer coding within the context of the ANN modeling[3]. The paper is organized in five sections. After the introduction in Section І, Section ІІ, which also introduces the existing methods of the performance evaluation. Section ІІ continues with explanations of Lvq neural network and adaptive neuro-fuzzy systems (ANFIS) in section ІІІ. Section ІV discusses the factors affecting on classification student base learning . It continues with discussions on the architecture of hybrid learning and fuzzy model validation and lvq neural network, the error of observations for training data sets. Section V presents