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Artificial Intelligence and Deep Learning-Based Information Retrieval Framework for Assessing Student Performance

Artificial Intelligence and Deep Learning-Based Information Retrieval Framework for Assessing Student Performance

S. L. Gupta, Niraj Mishra
Copyright: © 2022 |Volume: 12 |Issue: 1 |Pages: 27
ISSN: 2155-6377|EISSN: 2155-6385|EISBN13: 9781683182085|DOI: 10.4018/IJIRR.2022010101
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MLA

Gupta, S. L., and Niraj Mishra. "Artificial Intelligence and Deep Learning-Based Information Retrieval Framework for Assessing Student Performance." IJIRR vol.12, no.1 2022: pp.1-27. http://doi.org/10.4018/IJIRR.2022010101

APA

Gupta, S. L. & Mishra, N. (2022). Artificial Intelligence and Deep Learning-Based Information Retrieval Framework for Assessing Student Performance. International Journal of Information Retrieval Research (IJIRR), 12(1), 1-27. http://doi.org/10.4018/IJIRR.2022010101

Chicago

Gupta, S. L., and Niraj Mishra. "Artificial Intelligence and Deep Learning-Based Information Retrieval Framework for Assessing Student Performance," International Journal of Information Retrieval Research (IJIRR) 12, no.1: 1-27. http://doi.org/10.4018/IJIRR.2022010101

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Abstract

Improving the quality of education is a challenging activity in every educational institution. Through this research paper, a model has been proposed representing the challenges in order to manage the trade-off to maintain the philosophy of continuous quality improvement and strict control based on Higher Education Institutions (HEIs). Several standards criteria, performance parameters, and Key Performance Indicators are studied and suggested for a quality self-assessment approach. After the data is collected, the significant features are selected for analysis of data using dedicated gain, which are designed by integrating the information gain and the dedicated weight constants. After that, deep learning methodologies like regression analysis, the artificial neural network, and the Matlab model are used for evaluating the academic quality of institutions. Finally, areas of development have been recommended using the probabilistic model to the administrators of the institutions based on the prediction made using a deep neural network.