E-ISSN 1658-7073 | ISSN 1658-6638
 

Original Research 


Classifying Studentís Academic Performance using SVM

Dr. Jabeen Sultana, Dr. Kishwar Sadaf, Dr. Abdul Khader Jilani.

Abstract
Learning management systems are mainly concerned to educational sectors play a dominant role in leading the nation across the globe. These days as everything goes online in terms of data storage across the globe. Lots of data emerges from learning systems and makes very promising to predict and classify learnerís performances. In order to classify studentsí performance, various techniques are available. One of the most popular techniques to classify studentsí performance is Machine Learning and is widely used in learning systems to process Informative facts about learners. Processing Educational data involves usage of several data processing methods like forecasting, clustering and finding out associations in order to extract the valuable information of the learners, their mood changes in shifting of subjects and accordingly their performances by extracting the hidden knowledge. Subsequently the obtained useful information and patterns can be used in predicting studentís performance. This research work suggests the effective technique in order to process and classify learnerís performance. Data is gathered from a middle east university concerning to graduate course. Machine Learning techniques like Support Vector Machine-SVM, Multi-Layer Perceptron-MLP, Random Forest-RF, Decision Tree-DT, NaÔve Bayes-NB and K-Nearest Neighbor-KNN are applied after preprocessing the data. The outcomes attained are assessed on few metrics like Accuracy, Sensitivity, Specificity, ROC Curve Area and Kappa statistics. SVM outperforms in classifying learnersí part linked to other methods by yielding optimal classification results like high accuracy and Sensitivity followed by MLP, RF, DT, NB and KNN.

Key words: Educational Data; Support vector Machine (SVM); Multi-Layer Perceptron (MLP); Random Forest (RF); Decision Tree (DT); NaÔve Bayes (NB); K-Nearest Neighbor (KNN).


 
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How to Cite this Article
Pubmed Style

Sultana DJ, Sadaf DK, Jilani DAK. Classifying Studentís Academic Performance using SVM. JEAS. 2021; 8(2): 61-68. doi:10.5455/jeas.2021110107


Web Style

Sultana DJ, Sadaf DK, Jilani DAK. Classifying Studentís Academic Performance using SVM. http://jecasmu.org/?mno=134005 [Access: December 31, 2021]. doi:10.5455/jeas.2021110107


AMA (American Medical Association) Style

Sultana DJ, Sadaf DK, Jilani DAK. Classifying Studentís Academic Performance using SVM. JEAS. 2021; 8(2): 61-68. doi:10.5455/jeas.2021110107



Vancouver/ICMJE Style

Sultana DJ, Sadaf DK, Jilani DAK. Classifying Studentís Academic Performance using SVM. JEAS. (2021), [cited December 31, 2021]; 8(2): 61-68. doi:10.5455/jeas.2021110107



Harvard Style

Sultana, D. J., Sadaf, . D. K. & Jilani, . D. A. K. (2021) Classifying Studentís Academic Performance using SVM. JEAS, 8 (2), 61-68. doi:10.5455/jeas.2021110107



Turabian Style

Sultana, Dr. Jabeen, Dr. Kishwar Sadaf, and Dr. Abdul Khader Jilani. 2021. Classifying Studentís Academic Performance using SVM. Journal of Engineering and Applied Sciences, 8 (2), 61-68. doi:10.5455/jeas.2021110107



Chicago Style

Sultana, Dr. Jabeen, Dr. Kishwar Sadaf, and Dr. Abdul Khader Jilani. "Classifying Studentís Academic Performance using SVM." Journal of Engineering and Applied Sciences 8 (2021), 61-68. doi:10.5455/jeas.2021110107



MLA (The Modern Language Association) Style

Sultana, Dr. Jabeen, Dr. Kishwar Sadaf, and Dr. Abdul Khader Jilani. "Classifying Studentís Academic Performance using SVM." Journal of Engineering and Applied Sciences 8.2 (2021), 61-68. Print. doi:10.5455/jeas.2021110107



APA (American Psychological Association) Style

Sultana, D. J., Sadaf, . D. K. & Jilani, . D. A. K. (2021) Classifying Studentís Academic Performance using SVM. Journal of Engineering and Applied Sciences, 8 (2), 61-68. doi:10.5455/jeas.2021110107