Predicting students’ performance using machine learning techniques
المؤلفون المشاركون
Ali, Usamah Abd al-Jalil
al-Tabarwi, Husayn
al-Ajami, Samir Qaysar
المصدر
Journal of Babylon University : Journal of Applied and Pure Sciences
العدد
المجلد 27، العدد 1 (28 فبراير/شباط 2019)، ص ص. 194-205، 12ص.
الناشر
تاريخ النشر
2019-02-28
دولة النشر
العراق
عدد الصفحات
12
التخصصات الرئيسية
الموضوعات
الملخص EN
The ultimate goal of any educational institution is offering the best educational experience and knowledge to the students.
Identifying the students who need extra support and taking the appropriate actions to enhance their performance plays an important role in achieving that goal.
In this research, four machine learning techniques have been used to build a classifier that can predict the performance of the students in a computer science subject that is offered by Al-Muthanna University (MU), College Of Humanities.
The machine learning techniques include Artificial Neural Network, Naïve Bayes, Decision Tree, and Logistic Regression.
This research pays extra attention to the effect of using the internet as a learning resource and the effect of the time spent by students on social networks on the students’ performance.
These effects introduced by using features that measure whether the student uses the internet for learning and the time spent on the social networks by the students.
The models have been compared using the ROC index performance measure and the classification accuracy.
In addition, different measures have been computed such as the classification error, precision, recall, and the F measure.
The dataset used to build the models is collected based on a survey given to the students and the students’ grade book.
The ANN (fully connected feed forward multilayer ANN) model achieved the best performance that is equal to 0.807 and achieved the best classification accuracy that is equal to 77.04%.
In addition, the decision tree model identified five factors as important factors which influence the performance of the students.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
al-Tabarwi, Husayn& Ali, Usamah Abd al-Jalil& al-Ajami, Samir Qaysar. 2019. Predicting students’ performance using machine learning techniques. Journal of Babylon University : Journal of Applied and Pure Sciences،Vol. 27, no. 1, pp.194-205.
https://search.emarefa.net/detail/BIM-1094625
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
al-Tabarwi, Husayn…[et al.]. Predicting students’ performance using machine learning techniques. Journal of Babylon University : Journal of Applied and Pure Sciences Vol. 27, no. 1 (2019), pp.194-205.
https://search.emarefa.net/detail/BIM-1094625
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
al-Tabarwi, Husayn& Ali, Usamah Abd al-Jalil& al-Ajami, Samir Qaysar. Predicting students’ performance using machine learning techniques. Journal of Babylon University : Journal of Applied and Pure Sciences. 2019. Vol. 27, no. 1, pp.194-205.
https://search.emarefa.net/detail/BIM-1094625
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references : p. 204-205
رقم السجل
BIM-1094625
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر