Students' perceptions about electronic assessment and their relationships to learning styles and academic self-efficacy

The present study aimed to identify students' perceptions about electronic assessment and their relationships to learning styles and academic self-efficacy. The sample of the study consisted of 342 male and female students enrolled in the baccalaureate programs at Sultan Qaboos University for the second semester of the academic year 2019/2020. To achieve the goals of the study, three instruments were used after establishing their validity and reliability: Students' Perceptions about Electronic Assessment Questionnaire, Academic Self-Efficacy Beliefs Scale, and Preferred Learning Styles Scale. The results of the study showed that students had a neutral perception about electronic assessment and medium level of academic self-efficacy. Also, the results showed that the most preferred learning styles for students were in order: the participatory, competitive, and independent;whereas the least preferred learning styles for students were in order: the cooperative, dependent, and avoidant. Further, the results showed a mediating effect of academic self-efficacy in the relationship between some of the students' preferred learning styles and perceptions about electronic assessment, as well as a direct positive effect of the cooperative learning style on the perception of electronic assessment. The study came out with a set of recommendations and suggestions to enhance students' perceptions of electronic assessment. © 2022, An-Najah National University. All rights reserved.

The Impact of COVID-19 on Digital Competence A Case Study of Preservice Teacher Education Students in the Sultanate of Oman

The study looked into how COVID-19 affected the digital competence of a group of preservice teacher education students at a higher education institution in the Sultanate of Oman. The paper examined students' digital profile in five areas namely information and data literacy, communication and collaboration, digital content creation, safety and problem solving. Data from 32 undergraduate students was collected by utilizing DigComp, a European Commission digital skills self-assessment tool and findings from a survey. The digital competence framework measures the set of skills, knowledge and attitudes that describes what it means to be digitally competent. These skills are important for students to be effective global citizens in the 21st century. The results of the study revealed that the majority of the students scored Level 3 (Intermediate) in their self-assessment competency test score. The majority of the students perceived that their digital competence improved significantly as the result of online learning which was accelerated by the COVID-19 pandemic. The rationale of this investigation is that it helps educators understand the students' level of digital competence and the students' perspectives on ICT skills. In turn, it informs us the ways to monitor the students' digital progress and the next steps in developing their digital competency © 2023, International Journal of Advanced Computer Science and Applications.All Rights Reserved.

Using Deep Learning Model for Adapting and Managing COVID-19 Pandemic Crisis.

The purpose of current paper is to create a smart and effective tool for telemedicine to early detect and diagnose COVID-19 disease and therefore help to manage Pandemic Crisis (MCPC) in Sultanate of Oman, as a tool for future pandemic containment. In this paper, we used tools to create robust models in real-time to support Telemedicine, it is Machine Learning (ML), Deep Learning (DL), Convolutional Neural Networks using Tensorflow (CNN-TF), and CNN Deployment. These models will assist telemedicine, 1) developing Automated Medical Immediate Diagnosis service (AMID). 2) Analysis of Chest X-rays image (CXRs). 3) Simplifying Classification of confirmed cases according to its severity. 4) Overcoming the lack of experience, by improving the performance of medical diagnostics and providing recommendations to the medical staff. The results show that the best Regression among the five Regression models is Random Forest Regression. while the best classification among the eight classification models and Recurrent Neural Network using Tensorflow (RNNTF) is Random Forest classification, and the best Clustering model among two Clustering models is K-Means++. Furthermore, CNN-TF model was able to discriminate between those with positive cases Covid-19 and those with negative cases.