Predicting Student Academic Performance with Ensemble Classification Method on Imbalanced Educational Data
E Deepak Chowdary1, V Lakshmi Prasanna2, V Vamsi Krishna T3, Gokul Yenduri4
1E Deepak Chowdary*, Department of CSE, VLITS, Vadlamudi, India. V Lakshmi Prasanna, Department of CSE, VNITS, Vadlamudi, India.
2V Vamsi Krishna T, Department of CSE, VLITS, Vadlamudi, India. Gokul Yenduri, Department of IT, VFSTR, Vadlamudi, India.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 1543-1551 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7741118419/2019©BEIESP | DOI: 10.35940/ijrte.D7741.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Education benefits a person in sustaining his present and future by assuring the goal of life. At present universities and colleges are mainly focusing to improve the academic performance of the students. Recently, many studies have concentrated on employing several machine learning models in the field of higher education to assist both the teachers and the students to identify their problems and can take remedial measures to improve their performances. Some of the earlier studies have been discussed about class imbalance problem and achieved poor prediction outcomes due to low performance of the classifiers. In this paper, we aim to improve the classification/prediction outcomes with a rule-based ensemble model based on various sampling strategies by addressing the class imbalance problem. The dataset used for this study has been collected from Vignan’s Lara and Vignan’s Nirula institutions based on considering various factors such as Attendance Percentage, No of Backlogs, Adjustable Nature, Concentration, Result History, Discipline in class, Usage of Social Media, Degree of Intelligence, Understanding of Subjects, Event Participation, Time Management, Extra Classes, Alternative Learning Skills, Logical Thinking, Bad Habits, Parents Education, Health Condition, Planning for higher studies, Family Support, Time Management, and Aggregate. To evaluate the efficiency, we also considered and compared our original dataset with different benchmark datasets and the performance measures of the proposed method is also tested with various sampling methods based on a learning rate parameter ranging between 0.1 and 0.8. The original data set with the re-sampling method with the proposed method achieved maximum precision values at a learning rate 0.3 with an accuracy rate of 98.36%. Finally, the obtained results were also compared with several baseline classifiers like Naïve Bayes, SVM, MLP, KNN, and OneR on the collected original datasets.
Keywords: Education, Student Performance, Sampling, Class Imbalance, Classification, Prediction.
Scope of the Article: Classification.