Analyzing Digital Addiction through Feature Selection and Machine Learning Techniques
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| Title: | Analyzing Digital Addiction through Feature Selection and Machine Learning Techniques |
|---|---|
| Language: | English |
| Authors: | Mizanur Rahman, Hasan Sarwar, Md Kamrul Hasan, Ting Tin Tin |
| Source: | Educational Process: International Journal. Article e2026067 2026 23. |
| Availability: | UNIVERSITEPARK Limited. iTOWER Plaza (No61, 9th floor) Merkez Mh Akar Cd No3, Sisli, Istanbul, Turkey 34382. e-mail: editor@edupij.com; Web site: http://www.edupij.com/ |
| Peer Reviewed: | Y |
| Page Count: | 24 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Addictive Behavior, Prediction, Artificial Intelligence, Statistical Analysis, Social Media, Students |
| ISSN: | 2147-0901 2564-8020 |
| Abstract: | Background/purpose: Digital addiction has become an increasing concern, particularly among students, as excessive engagement with digital platforms negatively impacts academic performance, social interactions, and mental health. This study primarily aims to evaluate and compare the effectiveness of various machine learning and deep learning models in predicting digital addiction. While we examine key features that contribute to classification performance, our focus is not on discovering new predictors but on selecting features that enhance model accuracy. However, few datasets are publicly available, and, moreover, these datasets do not employ similar feature types. As a result, it is not readily clear which set of features a researcher should consider to obtain the most accurate prediction. Materials/methods: We utilized two publicly available datasets and conducted feature selection using ANOVA scores and Random Forest. We then performed prediction of digital addiction using four machine learning (ML) models--K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB)--as well as one deep learning model, Artificial Neural Network (ANN). These models were evaluated using standard performance metrics, including accuracy, precision, recall, and F1 Score. Results: Our results show that Random Forest, when used to identify the most important feature, performs better overall, especially for models such as KNN. The Random Forest and Gradient Boosting models achieved perfect scores (100% accuracy, precision, recall, and F1-score) on one dataset, and the ANN model reached up to 99.5% accuracy. Conclusion: Random Forest-based feature importance is particularly effective, and the overall results suggest that ensemble models such as RF and GB are highly reliable. This research will guide future researchers, as they create their own dataset, on the importance of selecting appropriate features in combination with machine learning algorithms to achieve better predictive accuracy. These insights may support early interventions in educational settings to reduce the academic risks associated with digital addiction. |
| Abstractor: | As Provided |
| Entry Date: | 20260101 |
| Accession Number: | EJ1514572 |
| Database: | ERIC |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=EJ1514572 Name: ERIC Full Text Category: fullText Text: Full Text from ERIC |
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| Items | – Name: Title Label: Title Group: Ti Data: Analyzing Digital Addiction through Feature Selection and Machine Learning Techniques – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mizanur+Rahman%22">Mizanur Rahman</searchLink><br /><searchLink fieldCode="AR" term="%22Hasan+Sarwar%22">Hasan Sarwar</searchLink><br /><searchLink fieldCode="AR" term="%22Md+Kamrul+Hasan%22">Md Kamrul Hasan</searchLink><br /><searchLink fieldCode="AR" term="%22Ting+Tin+Tin%22">Ting Tin Tin</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22Educational+Process%3A+International+Journal%22"><i>Educational Process: International Journal</i></searchLink>. Article e2026067 2026 23. – Name: Avail Label: Availability Group: Avail Data: UNIVERSITEPARK Limited. iTOWER Plaza (No61, 9th floor) Merkez Mh Akar Cd No3, Sisli, Istanbul, Turkey 34382. e-mail: editor@edupij.com; Web site: http://www.edupij.com/ – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 24 – Name: DatePubCY Label: Publication Date Group: Date Data: 2026 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Journal Articles<br />Reports - Research – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Addictive+Behavior%22">Addictive Behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction%22">Prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+Analysis%22">Statistical Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Social+Media%22">Social Media</searchLink><br /><searchLink fieldCode="DE" term="%22Students%22">Students</searchLink> – Name: ISSN Label: ISSN Group: ISSN Data: 2147-0901<br />2564-8020 – Name: Abstract Label: Abstract Group: Ab Data: Background/purpose: Digital addiction has become an increasing concern, particularly among students, as excessive engagement with digital platforms negatively impacts academic performance, social interactions, and mental health. This study primarily aims to evaluate and compare the effectiveness of various machine learning and deep learning models in predicting digital addiction. While we examine key features that contribute to classification performance, our focus is not on discovering new predictors but on selecting features that enhance model accuracy. However, few datasets are publicly available, and, moreover, these datasets do not employ similar feature types. As a result, it is not readily clear which set of features a researcher should consider to obtain the most accurate prediction. Materials/methods: We utilized two publicly available datasets and conducted feature selection using ANOVA scores and Random Forest. We then performed prediction of digital addiction using four machine learning (ML) models--K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB)--as well as one deep learning model, Artificial Neural Network (ANN). These models were evaluated using standard performance metrics, including accuracy, precision, recall, and F1 Score. Results: Our results show that Random Forest, when used to identify the most important feature, performs better overall, especially for models such as KNN. The Random Forest and Gradient Boosting models achieved perfect scores (100% accuracy, precision, recall, and F1-score) on one dataset, and the ANN model reached up to 99.5% accuracy. Conclusion: Random Forest-based feature importance is particularly effective, and the overall results suggest that ensemble models such as RF and GB are highly reliable. This research will guide future researchers, as they create their own dataset, on the importance of selecting appropriate features in combination with machine learning algorithms to achieve better predictive accuracy. These insights may support early interventions in educational settings to reduce the academic risks associated with digital addiction. – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 20260101 – Name: AN Label: Accession Number Group: ID Data: EJ1514572 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1514572 |
| RecordInfo | BibRecord: BibEntity: Languages: – Text: English PhysicalDescription: Pagination: PageCount: 24 Subjects: – SubjectFull: Addictive Behavior Type: general – SubjectFull: Prediction Type: general – SubjectFull: Artificial Intelligence Type: general – SubjectFull: Statistical Analysis Type: general – SubjectFull: Social Media Type: general – SubjectFull: Students Type: general Titles: – TitleFull: Analyzing Digital Addiction through Feature Selection and Machine Learning Techniques Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mizanur Rahman – PersonEntity: Name: NameFull: Hasan Sarwar – PersonEntity: Name: NameFull: Md Kamrul Hasan – PersonEntity: Name: NameFull: Ting Tin Tin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 2147-0901 – Type: issn-electronic Value: 2564-8020 Numbering: – Type: volume Value: 23 Titles: – TitleFull: Educational Process: International Journal Type: main |
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