An Attentive Deep Learning Framework for the Prediction of Academic Performance of Students in Virtual Learning Environments.
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| Title: | An Attentive Deep Learning Framework for the Prediction of Academic Performance of Students in Virtual Learning Environments. |
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| Authors: | Mohanarathinam, A.1 (AUTHOR) mohanarathinam@gmail.com, Arunkumar, M.1 (AUTHOR), Subramaniam, Kamalraj1 (AUTHOR) |
| Source: | International Journal of Software Engineering & Knowledge Engineering. Oct2026, Vol. 36 Issue 13, p1821-1844. 24p. |
| Subjects: | Deep learning, Recurrent neural networks, Academic achievement, Online education, Virtual classrooms, Selectivity (Psychology), Optimization algorithms |
| Abstract: | Virtual learning environments (VLEs) and Massive open online courses (MOOCs) are two trends that have emerged due to the usage of new technology in education. However, without adequate monitoring, the students will suffer from online education's inability to assess students' achievement. In this research, the Attentive Pelican Optimized Dense Gated Recurrent Unit (APDGRU) network is introduced. In order to eliminate the information that is required for prediction, one-hot encoding (OHE) is used. Dense Connectivity Network (DenseNet) and Gated Recurrent Unit (GRU) are two deep learning models included in the proposed network design, along with another method for carefully evaluating the relative contributions of different elements. The dataset parameters, which mostly consist of categorical data like the student's personal information, are fed into the suggested model. The primary variables in the dataset that have the largest impacts on student performance are found using the DenseNet model. The proposed model's GRU unit deals with temporal data, such as how long students engage in VLE-based learning and interaction. The attention mechanism is crucial for helping the model recognize the best traits, which it learns throughout training. The attention mechanism's integration enhances the model's interpretability and accuracy by concentrating on the most pertinent characteristics affecting academic success. The proposed model parameters enable the creation of the best predictions considering formal metric maximization and interpretability. By minimizing the logarithmic loss function, the model's internal parameters are familiar with the Pelican Optimization Algorithm (POA). POA modifies model weights to ensure the suggested model performs at its best by reducing errors, accelerating the convergence and avoiding overfitting. Furthermore, POA aids in the selection of pertinent characteristics and the avoidance of unnecessary data, resulting in a more robust and generalized model. The implementations are carried out in the Python platform, and the evaluations are done using the Open University Learning Analytics Dataset (OULAD). The overall accuracy of 97.18% proves the model's benefit over other existing models. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Software Engineering & Knowledge Engineering is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194643016 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An Attentive Deep Learning Framework for the Prediction of Academic Performance of Students in Virtual Learning Environments. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mohanarathinam%2C+A%2E%22">Mohanarathinam, A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mohanarathinam@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Arunkumar%2C+M%2E%22">Arunkumar, M.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Subramaniam%2C+Kamalraj%22">Subramaniam, Kamalraj</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Software+Engineering+%26+Knowledge+Engineering%22">International Journal of Software Engineering & Knowledge Engineering</searchLink>. Oct2026, Vol. 36 Issue 13, p1821-1844. 24p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Recurrent+neural+networks%22">Recurrent neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+achievement%22">Academic achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Online+education%22">Online education</searchLink><br /><searchLink fieldCode="DE" term="%22Virtual+classrooms%22">Virtual classrooms</searchLink><br /><searchLink fieldCode="DE" term="%22Selectivity+%28Psychology%29%22">Selectivity (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Virtual learning environments (VLEs) and Massive open online courses (MOOCs) are two trends that have emerged due to the usage of new technology in education. However, without adequate monitoring, the students will suffer from online education's inability to assess students' achievement. In this research, the Attentive Pelican Optimized Dense Gated Recurrent Unit (APDGRU) network is introduced. In order to eliminate the information that is required for prediction, one-hot encoding (OHE) is used. Dense Connectivity Network (DenseNet) and Gated Recurrent Unit (GRU) are two deep learning models included in the proposed network design, along with another method for carefully evaluating the relative contributions of different elements. The dataset parameters, which mostly consist of categorical data like the student's personal information, are fed into the suggested model. The primary variables in the dataset that have the largest impacts on student performance are found using the DenseNet model. The proposed model's GRU unit deals with temporal data, such as how long students engage in VLE-based learning and interaction. The attention mechanism is crucial for helping the model recognize the best traits, which it learns throughout training. The attention mechanism's integration enhances the model's interpretability and accuracy by concentrating on the most pertinent characteristics affecting academic success. The proposed model parameters enable the creation of the best predictions considering formal metric maximization and interpretability. By minimizing the logarithmic loss function, the model's internal parameters are familiar with the Pelican Optimization Algorithm (POA). POA modifies model weights to ensure the suggested model performs at its best by reducing errors, accelerating the convergence and avoiding overfitting. Furthermore, POA aids in the selection of pertinent characteristics and the avoidance of unnecessary data, resulting in a more robust and generalized model. The implementations are carried out in the Python platform, and the evaluations are done using the Open University Learning Analytics Dataset (OULAD). The overall accuracy of 97.18% proves the model's benefit over other existing models. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Software Engineering & Knowledge Engineering is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1142/S0218194026500026 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 1821 Subjects: – SubjectFull: Deep learning Type: general – SubjectFull: Recurrent neural networks Type: general – SubjectFull: Academic achievement Type: general – SubjectFull: Online education Type: general – SubjectFull: Virtual classrooms Type: general – SubjectFull: Selectivity (Psychology) Type: general – SubjectFull: Optimization algorithms Type: general Titles: – TitleFull: An Attentive Deep Learning Framework for the Prediction of Academic Performance of Students in Virtual Learning Environments. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mohanarathinam, A. – PersonEntity: Name: NameFull: Arunkumar, M. – PersonEntity: Name: NameFull: Subramaniam, Kamalraj IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02181940 Numbering: – Type: volume Value: 36 – Type: issue Value: 13 Titles: – TitleFull: International Journal of Software Engineering & Knowledge Engineering Type: main |
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