Online learning is one of the educational solutions for students during the COVID-19 pandemic. Worldwide, most universities have shifted much of their learning frameworks to an online learning model to limit physical interaction between people and slow the spread of COVID-19. The effectiveness of online learning depends on many factors, including student and instructor self-efficacy, attitudes, and confidence in using the technology involved; the educational strategies employed; the ability to monitor and evaluate educational outcomes; and student motivation, among many others. In this study, we analyzed how these factors were associated and impacted each other. We developed a comprehensive model after an extensive review of the relevant literature. The model was validated by applying partial least square regression to the data obtained by surveying 469 students who were enrolled in online education. The test results indicated that all the variables had a positive effect on the effectiveness of online learning. The effectiveness of online learning had a significant impact on the benefits of online learning. This showed that the more effective online learning was, the more benefits and positive outcomes the student experienced. The result of this research showed that learning objectives could enable universities to increase the effectiveness of students’ online learning by motivating students to join online classes and developing appropriate learning strategies for their individual needs.

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Abstract:The objective of this study was to present a new technique assists in developing a recognition system for handling the Arabic Hand Written text. The proposed system is called Arabic Hand Written Optical Character Recognition (AHOCR). AHOCR was concerned with recognition of hand written Alphanumeric Arabic characters. In the present work, extracted characters are represented by using geometric moment invariant of order three. The advantage of using moment invariant for pattern classification as compared to the other methods was its invariant with respect to its: position , size and rotation .The proposed technique was divided into three major steps : the first step was concerned with digitization and preprocessing documents to create connect components, detect the skew of characters and correct it .The second step deals with how to use geometric moment invariant features of the input Arabic characters to extract features . The third step focused on description of an advanced system of classification using Probabilistic Neural networks structure which yields significant speed improvement. Our final results indicate and clarify that the proposed AHOCR technique achieves an excellent test accuracy of recognition rated up to 97% for isolated Arabic characters and 96% for Arabic text.

As the number of aged people is rapidly growing, the need for health and living care of aged people living alone becomes imperative. The telecare systems are able to provide flexible services for older people suffering from chronic diseases, but are largely user group oriented. However, it is common in elderly to show symptoms of a combination of (chronic) diseases. Moreover, elderly are totally dependent on a third person as they are unable to perform a number of basic functions at home. They also feel cut off from the social fabric. Old people living in remote places typically use telephone that dials a social alarm control center or mobile social alarm systems and monitoring systems.This study examines the existing solutions related to elderly assistance and proposes an advanced solution based on web technology for the self-management of health and independent living of elderly.