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 |Pages: 27
DOI: 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.
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2. Problem Statement And Objective

The factors which act as a predictor for performance of students in higher education institutes using ANN has not been studied much by researchers and this research gap has been the main focus area of the present study.

Hence the study was done with following objectives.

The purpose of the research is to study the various aspects of quality, namely commitment of Board of Trustees towards quality management, improvement in teaching and learning, mapping of stakeholders expectations, and professional development assistance by affiliating university. The basic purpose of the research is to predict the academic quality of institutions using deep neural network, and to improve the areas using probabilistic based recommendation model.

This research has contributed to the knowledge in area of ANN, but there are several limitations of this study which should be kept in mind when interpreting the findings, The study has been conducted on students of 10 universities and colleges in Oman and thus generalization of the findings should be done with caution. It is also suggested to have further empirical investigation to establish whether the constructs in the proposed model vary across countries and types of higher education institutes.

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