Building an effective recommendation model for students' academic pathways using machine learning
Other Title(s)
بناء نموذج توصية فعال للمسارات الأكاديمية للطلاب باستخدام التعلم الآلي
Joint Authors
Muhammad, Ali Rahim
Abd al-Maqsud, Ala Mahmud
Jamal al-Din, Shihab Ahmad
Source
Journal of Al-Azhar University Engineering Sector
Issue
Vol. 18, Issue 69 (31 Oct. 2023), pp.939-950, 12 p.
Publisher
al-Azhar University Faculty of Engineering
Publication Date
2023-10-31
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Information Technology and Computer Science
Topics
Abstract AR
يعد اختيار المسار الأكاديمي الأنسب أمرا بالغ الأهمية للطلاب في التعليم العالي، حيث يمكن أن يؤثر بشكل كبير على فرصهم الوظيفية المستقبلية ونجاحهم.
اقترحت هذه الورقة نظام توصية لطلاب القسم المدني بكلية الهندسة بجامعة الأزهر.
تم إنشاء النظام باستخدام درجات الطلاب الذين تخرجوا من القسم في الفترة من 2018 إلى 2022.
خوارزميات مختلفة للتعلم الآلي مثل Decision Tree (DT) وRandom Forest (RF) وK-Nearest Neighbor (KNN) وNaive Bayes يتم تطبيق (NB) وSupport Vector Machine (SVM) لإنشاء نماذج لكل تخصص في الإدارة المدنية.
يتم تقييم النماذج باستخدام طريقة 5-fold cross-validation، حيث يعمل F1-measure كمعيار للأداء.
تشير النتائج إلى أن نموذج KNN يحقق أفضل أداء لتخصصي الهندسة الإنشائية والأشغال العامة، حيث بلغت دقة F1-measure 84٪ و87٪ على التوالي.
من ناحية أخرى، يحقق نموذج NB أفضل أداء للري والمكونات الهيدروليكية، مع F1-measure-بنسبة 69٪.
يمكن أن يساعد نظام التوصية المقترح الطلاب في اتخاذ قرارات مستنيرة حول حياتهم الأكاديمية، من خلال التوصية بالتخصص الأكثر ملاءمة بناء على أدائهم الأكاديمي.
Abstract EN
Selecting the most suitable academic pathway is critical for students in higher education, as it can significantly impact their future career opportunities and success.
this paper proposed a recommendation system for students in the civil department at al-Azhar university's faculty of engineering.
the system was created using the grades of students who graduated from the department in the period from 2018 to 2022.
different machine learning algorithms such as decision tree (DT), random forest (RF), k-nearest neighbor (KNN), naive bayes (NB), and support vector machine (SVM), are applied to create models for each major in the civil department.
the models are evaluated using 5-fold cross-validation method, with the F1-measure serving as the performance criterion.
the results indicate that the KNN model achieves the best performance for the structural engineering and public works majors, with an F1-measure of 84% and 87% accuracy, respectively.
on the other hand, the nb model achieves the best performance for irrigation and hydraulics, with an F1-measure of 69%.
the proposed recommendation system can potentially assist students in making informed decisions about their academic careers, by recommending the most appropriate major based on their academic performance.
American Psychological Association (APA)
Abd al-Maqsud, Ala Mahmud& Jamal al-Din, Shihab Ahmad& Muhammad, Ali Rahim. 2023. Building an effective recommendation model for students' academic pathways using machine learning. Journal of Al-Azhar University Engineering Sector،Vol. 18, no. 69, pp.939-950.
https://search.emarefa.net/detail/BIM-1519783
Modern Language Association (MLA)
Abd al-Maqsud, Ala Mahmud…[et al.]. Building an effective recommendation model for students' academic pathways using machine learning. Journal of Al-Azhar University Engineering Sector Vol. 18, no. 69 (Oct. 2023), pp.939-950.
https://search.emarefa.net/detail/BIM-1519783
American Medical Association (AMA)
Abd al-Maqsud, Ala Mahmud& Jamal al-Din, Shihab Ahmad& Muhammad, Ali Rahim. Building an effective recommendation model for students' academic pathways using machine learning. Journal of Al-Azhar University Engineering Sector. 2023. Vol. 18, no. 69, pp.939-950.
https://search.emarefa.net/detail/BIM-1519783
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references: p. 950
Record ID
BIM-1519783