Data Mining Techniques to Analyze the Impact of Social Media on Academic Performance of High School Students
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’ perf...
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| Veröffentlicht in: | Wireless communications and mobile computing 2022-03, Vol.2022, p.1-11 |
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| container_title | Wireless communications and mobile computing |
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| creator | Amjad, Saman Younas, Muhammad Anwar, Muhammad Shaheen, Qaisar Shiraz, Muhammad Gani, Abdullah |
| description | 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. |
| doi_str_mv | 10.1155/2022/9299115 |
| format | Article |
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B. ; B B Gupta</contributor><creatorcontrib>Amjad, Saman ; Younas, Muhammad ; Anwar, Muhammad ; Shaheen, Qaisar ; Shiraz, Muhammad ; Gani, Abdullah ; Gupta, B. B. ; B B Gupta</creatorcontrib><description>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. 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| subjects | Academic achievement Algorithms Classifiers College students Data analysis Data mining Datasets Decision analysis Decision making Decision trees Digital media Education Higher education Impact analysis Influence Internet Machine learning Secondary schools Social networks Statistical analysis Statistical methods Students Support vector machines Teaching |
| title | Data Mining Techniques to Analyze the Impact of Social Media on Academic Performance of High School Students |
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