Skip to main navigation menu Skip to main content Skip to site footer
×
Español (España) | English
Editorial
Home
Indexing
Original

An Efficient Hybrid Deep Learning Framework for Predicting Student Academic Performance

By
M. Viveka ,
M. Viveka

Research Scholar (Part Time), Department of Computer Science, Dr. SNS Rajalakshmi College of Arts and Science, Coimbatore – 49

Search this author on:

PubMed | Google Scholar
Dr.N.Shanmuga Priya ,
Dr.N.Shanmuga Priya

Associate Professor & Head, Department of Computer Applications, Dr.SNS Rajalakshmi College of Arts and Science, Coimbatore – 49

Search this author on:

PubMed | Google Scholar

Abstract

Introduction: Educational data analysis with data mining techniques for enhanced learning is increasing. Voluminous data available through institutions, online educational resources and virtual educational courses could be useful in tracking learning patterns of students. Data mining techniques could be helpful for predicting students’ academic performance from raw data. Conventional Machine Learning (ML) techniques have so far been widely used for predicting this.
Methods: However, research available on the Convolutional Neural Networks (CNNs) architecture is very scarce in the context of the academic domain. Therefore, in this work a hybrid CNN model involving 2 different CNN models for forecasting academic performance. The one-dimensional data is converted into two-dimensional equivalent to determine efficiency of the hybrid model which is subsequently compared with many existing.
Result: The experimental results are evaluated using various performance metrics like precision, accuracy, recall and F-Score.
Conclusion: The proposed hybrid model outperforms-Nearest Neighbour (K-NN), Decision Trees (DTs), and Artificial Neural Network (ANN) in terms of precision, accuracy, recall and F-Score.

 

How to Cite

1.
Viveka M, Shanmuga Priya D. An Efficient Hybrid Deep Learning Framework for Predicting Student Academic Performance. Salud, Ciencia y Tecnología - Serie de Conferencias [Internet]. 2024 May 12 [cited 2024 May 21];3:759. Available from: https://conferencias.saludcyt.ar/index.php/sctconf/article/view/759

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

Article metrics

Google scholar: See link

Metrics

Metrics Loading ...

The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.