Details

Autor(en) / Beteiligte

• Amjad, Saman
• Younas, Muhammad
• Anwar, Muhammad
• Shaheen, Qaisar
• Shiraz, Muhammad
• Gani, Abdullah
• Gupta, B. B.
• B B Gupta

Titel

Data Mining Techniques to Analyze the Impact of Social Media on Academic Performance of High School Students

Ist Teil von

• Wireless communications and mobile computing, 2022-03, Vol.2022, p.1-11

Ort / Verlag

Oxford: Hindawi

Erscheinungsjahr

2022

Link zum Volltext

Quelle

Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals

Beschreibungen/Notizen

• The main purpose of educational institutions is to provide quality education to their students. However, it is difficult to analyze large data manually. Educational data mining is more effective as compared to statistical methods used to explore data in educational settings to analyze students’ performance. The objective of the study is to use different data mining techniques and find their performance and impact of different features on students’ academic performance. The dataset was collected from the Kaggle repository. To analyze the dataset, different classification algorithms were applied like decision tree, random forest, SVM classifier, SGD classifier, AdaBoost classifier, and LR classifier. This research revealed that random forest achieved a higher score (98%). The score of decision tree, AdaBoost, logistic regression, SVM, and SGD is 90%, 89%, 88%, 86%, and 84%, respectively. Results show that technology greatly influences student performance. The students who use social media throughout the week showed low performance as compared to the students who use it only at weekends. Furthermore, the impact of other features on the performance of students is also measured.