Predicting Students’ Failure Risk Education Through Machine Learning
In the evolving landscape of education, the paradigm shift towards online learning has become even more pronounced in response to the prevailing global conditions. This transition, while offering unprecedented accessibility and flexibility, brings forth unique challenges, particularly in the realm o...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
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| Series: | MATEC Web of Conferences |
| Online Access: | https://www.matec-conferences.org/articles/matecconf/pdf/2024/04/matecconf_icmed2024_01113.pdf |
| _version_ | 1797248892900737024 |
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| author | Subbarayudu Yerragudipadu Vijendar Reddy Gurram Raj Kumar Masuram Aravind Nai Mudavath Prashanthi G. Bhalla Lalit |
| author_facet | Subbarayudu Yerragudipadu Vijendar Reddy Gurram Raj Kumar Masuram Aravind Nai Mudavath Prashanthi G. Bhalla Lalit |
| author_sort | Subbarayudu Yerragudipadu |
| collection | DOAJ |
| description | In the evolving landscape of education, the paradigm shift towards online learning has become even more pronounced in response to the prevailing global conditions. This transition, while offering unprecedented accessibility and flexibility, brings forth unique challenges, particularly in the realm of assessing and monitoring student performance. The absence of traditional face-to-face interactions and direct observations necessitates innovative solutions to gauge and predict student success effectively. Recognizing the imperative need for proactive intervention, this project endeavours to harness the power of machine learning algorithms to develop a robust predictive model. By leveraging comprehensive datasets encompassing student activities, grades, interactions with educators and peers, and other pertinent information derived from learning management systems, the aim is to construct a reliable framework capable of forecasting the likelihood of a student encountering academic challenges or failure. The predictive model proposed in this study is poised to revolutionize the educational landscape by enabling early identification of students at risk. Through the analysis of diverse parameters, the model seeks to provide educators with actionable insights, empowering them to take timely and targeted measures to support struggling students. The overarching goal is not only to predict potential academic setbacks but to also equip tutors with the tools necessary to implement tailored strategies that mitigate these risks, ultimately contributing to a substantial reduction in the overall failure rate within educational institutions. In essence, this project embodies a forward-looking approach to education, where data-driven insights pave the way for personalized interventions, fostering a supportive and inclusive learning environment. As we navigate the digital era of education, the development of such predictive models becomes indispensable in ensuring the success and well-being of students, ushering in a new era of educational excellence and adaptability. |
| first_indexed | 2024-04-24T20:21:49Z |
| format | Article |
| id | doaj.art-84d84ddc57894131ac0e1ee665bae7ef |
| institution | Directory Open Access Journal |
| issn | 2261-236X |
| language | English |
| last_indexed | 2024-04-24T20:21:49Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj.art-84d84ddc57894131ac0e1ee665bae7ef2024-03-22T08:05:18ZengEDP SciencesMATEC Web of Conferences2261-236X2024-01-013920111310.1051/matecconf/202439201113matecconf_icmed2024_01113Predicting Students’ Failure Risk Education Through Machine LearningSubbarayudu Yerragudipadu0Vijendar Reddy Gurram1Raj Kumar Masuram2Aravind Nai Mudavath3Prashanthi G.4Bhalla Lalit5Department of AI&ML, KG Reddy College of Engineering and Technology, MoinabadDepartment of Information Technology, GRIETDepartment of Information Technology, GRIETDepartment of Information Technology, GRIETDepartment of IT, GRIETLovely Professional UniversityIn the evolving landscape of education, the paradigm shift towards online learning has become even more pronounced in response to the prevailing global conditions. This transition, while offering unprecedented accessibility and flexibility, brings forth unique challenges, particularly in the realm of assessing and monitoring student performance. The absence of traditional face-to-face interactions and direct observations necessitates innovative solutions to gauge and predict student success effectively. Recognizing the imperative need for proactive intervention, this project endeavours to harness the power of machine learning algorithms to develop a robust predictive model. By leveraging comprehensive datasets encompassing student activities, grades, interactions with educators and peers, and other pertinent information derived from learning management systems, the aim is to construct a reliable framework capable of forecasting the likelihood of a student encountering academic challenges or failure. The predictive model proposed in this study is poised to revolutionize the educational landscape by enabling early identification of students at risk. Through the analysis of diverse parameters, the model seeks to provide educators with actionable insights, empowering them to take timely and targeted measures to support struggling students. The overarching goal is not only to predict potential academic setbacks but to also equip tutors with the tools necessary to implement tailored strategies that mitigate these risks, ultimately contributing to a substantial reduction in the overall failure rate within educational institutions. In essence, this project embodies a forward-looking approach to education, where data-driven insights pave the way for personalized interventions, fostering a supportive and inclusive learning environment. As we navigate the digital era of education, the development of such predictive models becomes indispensable in ensuring the success and well-being of students, ushering in a new era of educational excellence and adaptability.https://www.matec-conferences.org/articles/matecconf/pdf/2024/04/matecconf_icmed2024_01113.pdf |
| spellingShingle | Subbarayudu Yerragudipadu Vijendar Reddy Gurram Raj Kumar Masuram Aravind Nai Mudavath Prashanthi G. Bhalla Lalit Predicting Students’ Failure Risk Education Through Machine Learning MATEC Web of Conferences |
| title | Predicting Students’ Failure Risk Education Through Machine Learning |
| title_full | Predicting Students’ Failure Risk Education Through Machine Learning |
| title_fullStr | Predicting Students’ Failure Risk Education Through Machine Learning |
| title_full_unstemmed | Predicting Students’ Failure Risk Education Through Machine Learning |
| title_short | Predicting Students’ Failure Risk Education Through Machine Learning |
| title_sort | predicting students failure risk education through machine learning |
| url | https://www.matec-conferences.org/articles/matecconf/pdf/2024/04/matecconf_icmed2024_01113.pdf |
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