Artificial Intelligence and Machine Learning to Predict Student Performance during the COVID-19

Abstract

Artificial intelligence is based on algorithms that enable machines to make decisions instead of humans. This technology improves user experiences in a variety of areas. In this paper we discuss an intelligent solution to predict the performance of Moroccan students in the region of Guelmim Oued Noun through a recommendation system using artificial intelligence techniques during the COVID-19.

Predictive modelling and analytics of students’ grades using machine learning algorithms

“…However, no comprehensive documentation was mentioned in terms of hyperparameters and values. (Tarik et al, 2021 ) opted to remove all missing data from its initial 142,110 students. With the remaining 72,010, accuracy of up to 70% were recorded with Random Forest.…”

Prospects and Challenges of Using Machine Learning for Academic Forecasting

“…Musso et al [10] e study successfully forecasted students' academic success one year ahead using the ANN based on cognitive and demographic traits Hudson and Cristiano [7] e results suggest that ML can generate dependable results in prediction Elhaj et al [13] e study was empirical, and it showed the ability of KNN in prediction of learning patterns of students Ahajjam et al [24] e paper provided AI-based solutions to track students' performance and was able to recommend diagnosis for the Moroccan students Pranav et al [25] e paper provided evidence on the significance of AI in management of education data and decision-making Lidia et al [26] e paper concluded that ML will be required more in the future because of the need to assist students to overcome learning difficulties and also enhance their productivity in learning Phauk and Takeo [27] e study recommended the use of the hybrid machine learning algorithm approach to solve misclassification issues and improve academic prediction accuracy Onan and Korukoglu [28] e research proposed an ensemble method to feature selection that combines the results of numerous independent feature lists generated by various features that may be used in education Onan [29] e study provided a better approach for managing students' information system via ML Hassen et al [30] e study showed that the student's success with the aid of machine learning can be monitored using their previous performance data before they engaged in the current program Ibtehal [31] e study affirmed the applicability of ML in education technology development and deployment Feders and Anders [32] ey developed a smart algorithm that assessed the teaching methods of teachers and how it affects the understanding of their lessons by students in the class taking into consideration the former knowledge of students Popenici and Kerr [33] ey examined the various implications of ML and other relevant AI-driven systems in higher education…”

Multi-classification deep CNN model for diagnosing COVID-19 using iterative neighborhood component analysis and iterative ReliefF feature selection techniques with X-ray images

“…It is very important the application of deep learning and artificial intelligence based approaches for the efficient detection of disease from X-ray images. During the current COVID-19 pandemic, using such deep learning based approaches in real time, especially for rapid testing, successful implementation, and detection of disease, could potentially provide enormous benefits [ [4] , [5] , [6] , [7] , [8] ].…”