Cognitive Learning Style Detection in e-Learning Environments using Artificial Neural Network

COVID-19 pandemic has impacted all aspects of our lives including learning. With the particular growth of e-learning, teaching approaches are being implemented at a distance on online platforms due to this pandemic. In this context, to make student involved throughout the online course, it is recommended to create an efficient platform similar to the traditional learning mode. In this study, we aims to improve learning style detection process by exploring additional such as cognitive traits. In fact, we have proposed novel approach based on Artificial neural network that classify students according to their level of cognitive learning styles in real-time. The proposed automated approach will certainly provide tutors with exhaustive information that helps them in achieving an improved and innovative online learning method. The results obtained are quite interesting and demonstrate the relevance of our solution. © 2022, International Journal of Emerging Technologies in Learning. All Rights Reserved.

Evaluating the Performance of Online Courses During Covid19 in a Moroccan Context

E-learning, one of the newest forms of learning, derives its appeal from being able to learn at one's own pace on a variety of knowledge. However, e-learning requires learning materials, a teacher or instructor to conduct the course online and a good internet connection. All these requirements must be taken into account in an appropriate way in order to allow quality learning for the different learners. Several researchers approach e-learning as a powerful learning tool, others approach it as a complement to face-to-face learning. Recently, the world has undergone a great transformation, especially after the spread of Coronavirus in all countries of the world. The whole world has been forced to stop face-to-face courses and close all universities. The solution was to use e-learning to replace face-to-face courses and allow students to take their courses without difficulty. In this article, we will analyze the feedback of some students in Morocco with the aim of evaluating the performance of distance learning and to know the learners' opinion on the quality of the online courses, since it was the first experience for many learners. The analysis of feedback was carried out through an online questionnaire addressed directly to the various learners.

Incidental diagnosis of Covid-19 pneumonia on chest computed tomography.

PURPOSE: The purpose of this study was to determine the prevalence and imaging characteristics of incidentally diagnosed COVID-19 pneumonia on computed tomography (CT). MATERIALS AND METHODS: This retrospective study was conducted between March 20th and March 31st, 2020 at Cochin hospital, Paris France. Thoracic CT examinations of all patients referred for another reason than a suspicion of SARS-CoV-2 infection were reviewed. CT images were analyzed by a chest radiologist to confirm the presence of findings consistent with COVID-19 pneumonia and quantify disease extent. Clinical and biological data (C-reactive protein serum level [CRP] and white blood cell count) of patients with CT findings suggestive for COVID-19 pneumonia were retrieved from the electronic medical chart. RESULTS: During the study period, among 205 diagnostic CT examinations, six examinations (6/205, 3%) in 6 different patients (4 men, 2 women; median age, 57 years) revealed images highly suggestive of COVID-19 pneumonia. The final diagnosis was confirmed by RT-PCR. Three inpatients were suspected of extra thoracic infection whereas three outpatients were either fully asymptomatic or presented with fatigue only. All had increased CRP serum level and lymphopenia. Disease extent on CT was mild to moderate in 5/6 patients (83%) and severe in 1/6 patient (17%). CONCLUSION: Cumulative incidence of fortuitous diagnosis if COVID-19 pneumonia did not exceed 3% during the highest pandemic phase and was predominantly associated with limited lung involvement.

COVID-19 pneumonia: A review of typical CT findings and differential diagnosis.

The standard of reference for confirming COVID-19 relies on microbiological tests such as real-time polymerase chain reaction (RT-PCR) or sequencing. However, these tests might not be available in an emergency setting. Computed tomography (CT) can be used as an important complement for the diagnosis of COVID-19 pneumonia in the current epidemic context. In this review, we present the typical CT features of COVID-19 pneumonia and discuss the main differential diagnosis.