Improving students performance prediction using machine learning and synthetic minority oversampling technique

المؤلفون المشاركون

Salih, Nibras Z.
Khalaf, Wala Muhammad Hasan

المصدر

Journal of Engineering and Sustainable Development

العدد

المجلد 25، العدد 6 (30 يونيو/حزيران 2021)، ص ص. 56-64، 9ص.

الناشر

الجامعة المستنصرية كلية الهندسة

تاريخ النشر

2021-06-30

دولة النشر

العراق

عدد الصفحات

9

التخصصات الرئيسية

تكنولوجيا المعلومات وعلم الحاسوب

الموضوعات

الملخص EN

Classification under supervision is the most common job that performed by machine learning.

However, most Educators were worried about the rising evidence of student academic failures in university education.

So, this study presents a supervised classification strategy of machine learning algorithm using an actual dataset contains 44 students, fourteen attributes for three previous academic years.

We have proposed features that show the relationship among three main subjects which are, calculus, mathematical analysis, and control system in the education course.

The objective of this study is to identify the student's failure in the control system subject and to enhance his performance by Multilayer Perceptron (MLP) algorithm.

The dataset is unbalanced, which causes overfitting of the results.

Synthetic Minority Oversampling Technique has applied to a dataset for obtaining balance dataset using Weka tool.

Several standard metrics used to evaluate the classifier results.

Therefore, the suitable results occurred after applying SMOTE with an accuracy of Classification under supervision is the most common job that performed by machine learning.

However, most Educators were worried about the rising evidence of student academic failures in university education.

So, this study presents a supervised classification strategy of machine learning algorithm using an actual dataset contains 44 students, fourteen attributes for three previous academic years.

We have proposed features that show the relationship among three main subjects which are, calculus, mathematical analysis, and control system in the education course.

The objective of this study is to identify the student's failure in the control system subject and to enhance his performance by Multilayer Perceptron (MLP) algorithm.

The dataset is unbalanced, which causes overfitting of the results.

Synthetic Minority Oversampling Technique has applied to a dataset for obtaining balance dataset using Weka tool.

Several standard metrics used to evaluate the classifier results.

Therefore, the suitable results occurred after applying SMOTE with an accuracy of 76.9% .

نمط استشهاد جمعية علماء النفس الأمريكية (APA)

Salih, Nibras Z.& Khalaf, Wala Muhammad Hasan. 2021. Improving students performance prediction using machine learning and synthetic minority oversampling technique. Journal of Engineering and Sustainable Development،Vol. 25, no. 6, pp.56-64.
https://search.emarefa.net/detail/BIM-1368943

نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)

Salih, Nibras Z.& Khalaf, Wala Muhammad Hasan. Improving students performance prediction using machine learning and synthetic minority oversampling technique. Journal of Engineering and Sustainable Development Vol. 25, no. 6 (2021), pp.56-64.
https://search.emarefa.net/detail/BIM-1368943

نمط استشهاد الجمعية الطبية الأمريكية (AMA)

Salih, Nibras Z.& Khalaf, Wala Muhammad Hasan. Improving students performance prediction using machine learning and synthetic minority oversampling technique. Journal of Engineering and Sustainable Development. 2021. Vol. 25, no. 6, pp.56-64.
https://search.emarefa.net/detail/BIM-1368943

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

-

رقم السجل

BIM-1368943