Earticle

초록

영어

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.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Data Collection and Preprocessing
  3.1. Feature Analysis
  3.2. Data Preprocessing
 4. Methodology
 5. Experiments and Results
  5.1. Environment
  5.2. Evaluation Measures
  5.3. Evaluation Results
 6. Conclusion
 References

키워드

저자

• Elaf Abu Amrieh [ Computer Information Systems Department, The University of Jordan ]
• Thair Hamtini [ Computer Information Systems Department, The University of Jordan ]
• Ibrahim Aljarah [ Computer Information Systems Department, The University of Jordan ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605