An Augmented Intelligence Framework for Performance Prognosis Using Hybrid Deep Learning
Kannan M
Department of Computer Science, Faculty of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India.
Department of Computer Applications, Faculty of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India.
There are many chances where improvement in the learning process is made through incorporation of technology-enhanced learning, which is made possible by virtual learning environments (VLEs). This research suggests a novel deep hybrid framework for predicting performance by using data from academic records along with other sources. Through the joint study of five behavior vectors—academic registry, VLE clickstream, midterm continuous evaluation, MOOC engagement, and behavioral features—the suggested framework, which integrates several data sources, makes it possible to predict the learning capability. This system could improve learner’s Augmented Intelligence by offering precise forecasts of their performance, guiding data-driven decision-making. The suggested approach models learner behavior and forecasts academic achievement using a multi-component design. This system builds a comprehensive prediction model by integrating various learning tools. It examines the fused data using deep learning algorithms to find intricate linkages and patterns that allow for precise student performance forecasts. This platform provides educators looking to improve student success with a game-changing way to integrate deep learning and fused data sources.
Keywords
Deep Learning, Performance Prognosis, Behavioural Features, Virtual Learning Environments.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Kannan M, Albert Antony Raj S and Ananthapadmanaban K R;
Methodology: Kannan M;
Software: Albert Antony Raj S and Ananthapadmanaban K R;
Data Curation: Kannan M;
Writing- Original Draft Preparation: Kannan M, Albert Antony Raj S and Ananthapadmanaban K R;
Visualization: Albert Antony Raj S and Ananthapadmanaban K R;
Investigation: Kannan M;
Supervision: Albert Antony Raj S and Ananthapadmanaban K R;
Validation: Kannan M;
Writing- Reviewing and Editing: Kannan M, Albert Antony Raj S and Ananthapadmanaban K R; All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
The authors would like to thank to the reviewers for nice comments on the manuscript.
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Kannan M
Department of Computer Science, Faculty of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India.
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Cite this article
Kannan M, Albert Antony Raj S and Ananthapadmanaban K R, “An Augmented Intelligence Framework for Performance Prognosis Using Hybrid Deep Learning”, Journal of Machine and Computing, vol.5, no.3, pp. 1905-1914, July 2025, doi: 10.53759/7669/jmc202505149.
