Academic progress monitoring through neural network
To lessen the impact of a low student success rate, it's critical to be able to identify students who are in danger of failing early on, so that more targeted remedial intervention may be implemented. Private colleges use a variety of techniques, including increased tuition, expanded laboratory...
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| Format: | Article |
| Language: | English |
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REA Press
2021-03-01
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| Series: | Big Data and Computing Visions |
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| Online Access: | https://www.bidacv.com/article_142228_8ac0005d65ffb1bf0553bb605b5aeba9.pdf |
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| _version_ | 1832579343700721664 |
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| author | Ramri Shukla Bardia Khalilian Sara Partouvi |
| author_facet | Ramri Shukla Bardia Khalilian Sara Partouvi |
| author_sort | Ramri Shukla |
| collection | DOAJ |
| description | To lessen the impact of a low student success rate, it's critical to be able to identify students who are in danger of failing early on, so that more targeted remedial intervention may be implemented. Private colleges use a variety of techniques, including increased tuition, expanded laboratory access, and the formation of learning communities. The prompt identification of students in danger of failing a given programme is important to both the students and the institutions with which they are registered, as seen by the debate presented below. Students are classified using artificial neural networks and random forests in this article. A private higher education provider provided a dataset of 2000 students. Artificial neural networks were found to provide the best performing model, with an accuracy of 83.24% percent. |
| format | Article |
| id | doaj-art-8e4c52a16d7749c1b143f46b7c63c9e7 |
| institution | Kabale University |
| issn | 2783-4956 2821-014X |
| language | English |
| publishDate | 2021-03-01 |
| publisher | REA Press |
| record_format | Article |
| series | Big Data and Computing Visions |
| spelling | doaj-art-8e4c52a16d7749c1b143f46b7c63c9e72025-01-30T12:21:07ZengREA PressBig Data and Computing Visions2783-49562821-014X2021-03-01111610.22105/bdcv.2021.142228142228Academic progress monitoring through neural networkRamri Shukla0Bardia Khalilian1Sara Partouvi2Department of Computer Science and Engineering, Amity University, Sector 125, Noida, Uttar Pradesh, India.Department of Management and International Business (MIB), University of Auckland, New Zealand.School of Management & Marketing, Taylor’s University, Malaysia.To lessen the impact of a low student success rate, it's critical to be able to identify students who are in danger of failing early on, so that more targeted remedial intervention may be implemented. Private colleges use a variety of techniques, including increased tuition, expanded laboratory access, and the formation of learning communities. The prompt identification of students in danger of failing a given programme is important to both the students and the institutions with which they are registered, as seen by the debate presented below. Students are classified using artificial neural networks and random forests in this article. A private higher education provider provided a dataset of 2000 students. Artificial neural networks were found to provide the best performing model, with an accuracy of 83.24% percent.https://www.bidacv.com/article_142228_8ac0005d65ffb1bf0553bb605b5aeba9.pdfeducationneural networkmonitroing |
| spellingShingle | Ramri Shukla Bardia Khalilian Sara Partouvi Academic progress monitoring through neural network Big Data and Computing Visions education neural network monitroing |
| title | Academic progress monitoring through neural network |
| title_full | Academic progress monitoring through neural network |
| title_fullStr | Academic progress monitoring through neural network |
| title_full_unstemmed | Academic progress monitoring through neural network |
| title_short | Academic progress monitoring through neural network |
| title_sort | academic progress monitoring through neural network |
| topic | education neural network monitroing |
| url | https://www.bidacv.com/article_142228_8ac0005d65ffb1bf0553bb605b5aeba9.pdf |
| work_keys_str_mv | AT ramrishukla academicprogressmonitoringthroughneuralnetwork AT bardiakhalilian academicprogressmonitoringthroughneuralnetwork AT sarapartouvi academicprogressmonitoringthroughneuralnetwork |