Suggested Citation: Jishan, Syed; Rashu, Raisul; Haque, Naheena; Rahman, Rashedur M. (2015) : Improving accuracy of students' final grade prediction model using optimal equal width binning and synthetic minority over-sampling technique, Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. There is a perpetual elevation in demand for higher education in the last decade all over the world; therefore, the need for improving the education system is imminent. Educational data mining is a newly-visible area in the field of data mining and it can be applied to better understanding the educational systems in Bangladesh. In this research, we present how data can be preprocessed using a discretization method called the Optimal Equal Width Binning and an over-sampling technique known as the Synthetic Minority Over-Sampling (SMOTE) to improve the accuracy of the students' final grade prediction model for a particular course. In order to validate our method we have used data from a course offered at North South University, Bangladesh. The result obtained from the experiment gives a clear indication that the accuracy of the prediction model improves significantly when the discretization and over-sampling methods are applied. Educational Data Mining (EDM) is an interdisciplinary research area that fixates on the utilization of data mining in the educational field. Educational data can be from different sources, but generally from academic institutions, but nowadays, online learning systems are also the incipient environment for acquiring educational data which can be habituated to analyze and extract utilizable information (Romero & Ventura 2010). The goal of the research is to predict the students' performance using attributes such as Cumulative Grade Point Average, Quiz, Laboratory, Midterm and Attendance marks. However, in order to improve the prediction model we introduced some preprocessing techniques so that the prediction model provides with more precise results which could be used to alert students before the final examination regarding their final outcome. We received the course data and student information from the North South University. After acquiring the data we preprocessed it and then applied three classification algorithms, e.g., Naïve Bayes, Decision Tree and Neural Network. In order to improve the model we looked into the techniques at the data preprocessing level. At first we
In REF authors conducted experiments and showed that the accuracy of the prediction model improves significantly when the discretization and over-sampling methods are applied.
Improving accuracy of students' final grade prediction model using optimal equal width binning and synthetic minority over-sampling technique
Decision Analytics
Computer Science