International Journal of Database Theory and Application Vol.9, No.8 (2016), pp.119-136 http://dx.doi.org/10.14257/ijdta.2016.9.8.13 ISSN: 2005-4270 IJDTA Copyright ⓒ 2016 SERSC Mining Educational Data to Predict Student’s academic Performance using Ensemble Methods *Elaf Abu Amrieh 1 , Thair Hamtini 2 and Ibrahim Aljarah 3 1,2,3 Computer Information Systems Department 1,2,3 The University of Jordan 1 Ilef.kram@hotmail.com, 2 thamtini@ju.edu.jo, 3 i.aljarah@ju.edu .jo Abstract Educational data mining has received considerable attention in the last few years. Many data mining techniques are proposed to extract the hidden knowledge from educational data. The extracted knowledge helps the institutions to improve their teaching methods and learning process. All these improvements lead to enhance the performance of the students and the overall educational outputs. In this paper, we propose a new student’s performance prediction model based on data mining techniques with new data attributes/features, which are called student’s behavioral features. These type of features are related to the learner’s interactivity with the e-learning management system. The performance of student’s predictive model is evaluated by set of classifiers, namely; Artificial Neural Network, Naïve Bayesian and Decision tree. In addition, we applied ensemble methods to improve the performance of these classifiers. We used Bagging, Boosting and Random Forest (RF), which are the common ensemble methods used in the literature. The obtained results reveal that there is a strong relationship between learner’s behaviors and their academic achievement. The accuracy of the proposed model using behavioral features achieved up to 22.1% improvement comparing to the results when removing such features and it achieved up to 25.8% accuracy improvement using ensemble methods. By testing the model using newcomer students, the achieved accuracy is more than 80%. This result proves the reliability of the proposed model. Keywords: Student academic performance, Educational Data Mining, E-learning, Ensemble, knowledge discovery, ANN Model 1. Introduction Recently there is an increasing research interest in educational data mining (EDM). EDM is an emerging field that uses data-mining (DM) techniques to analyze and extract the hidden knowledge from educational data context [1]. EDM includes different groups of users, these users utilize the knowledge discovered by EDM according to their own vision and objectives of using DM [2]. For example, the hidden knowledge can help the educators to improve teaching techniques, to understand learners, to improve learning process and it could be used by learner to improve their learning activities [3]. It also helps the administrator taking the right decisions to produce high quality outcomes [4]. The educational data can be collected from different sources such as web-based education, educational repositories and traditional surveys. EDM can use different DM techniques, each technique can be used for certain educational problem. As Example, to predict an educational model the most popular technique is classification. There are several algorithms under classification such as Decision tree, Neural Networks and Bayesian networks [5]. This paper introduces a students’ performance model with a new category of features, which called behavioral features. The educational dataset is collected from learning management system (LMS) called Kalboard 360 [6]. This model used some data mining Online Version Only. Book made by this file is ILLEGAL.