Exploring Media and Communication Students’ Perception of Egyptian Universities’ Use of Augmented Reality in Learning

The aim of this study was to investigate the perceptions of media students in Egypt universities about using augmented reality (AR) technology in learning. To achieve this, the study adopted Technology Acceptance Model (TAM) and utilized a survey questionnaire to collect data from students in seven universities across Egypt. The findings revealed that (i) the students had a positive perception about using AR in media and communication learning; (ii) many media students in Egypt were not fully aware of the various AR technology applications in media and communication education; (iii) the students identified several negative factors that may hinder their acceptance of AR technology as an instructional tool, such as poor connectivity, lack of free AR programs, and lack of training programs. Addressing these barriers could help promote the adoption of AR technology in media and communication learning among students in Egypt. The significance of the study lies in that it sheds light on the need for increased awareness and education of the potential benefits of using AR technology in media and communication learning

Optimizing student engagement in edge-based online learning with advanced analytics

Edge-Based Online Learning (EBOL), a technique that combines the practical, hands-on approach of EBOL with the convenience of Online Learning (OL), is growing in popularity. But accurately monitoring student engagement to enhance teaching methodologies and learning outcomes is one of the difficulties of OL. To determine this challenge, this paper has put forth an Edge-Based Student Attentiveness Analysis System (EBSAAS) method, which uses a Face Detection (FD) algorithm and a Deep Learning (DL) model known as DLIP to extract eye and mouth landmark features. Images of the eye and mouth are used to extract landmarks using DLIP or Deep Learning Image Processing. Landmark Localization pre-trained models for Facial Landmark Localization (FLL) are one well-liked DL model for facial landmark recognition. The Visual Geometry Group-19 (VGG-19) learning model then uses these features to classify the student's level of attentiveness as fatigued or focused. Compared to a server-based model, the proposed model is developed to execute on an Edge Device (ED), enabling a swift and more effective analysis. The EBOL achieves 95.29% accuracy and attains 2.11% higher than existing model 1 and 4.41% higher than existing model 2. The study's findings have shown how successful the proposed method is at assisting teachers in changing their teaching methodologies to engage students better and enhance learning outcomes