The literature shows conflicting outcomes, making it difficult to determine how e-learning affects the performance of students in higher education. The effect of e-learning was studied and data has been gathered with the utilization of a variety of qualitative and quantitative methods, especially in relation to students' academic achievements and perceptions in higher education, according to literature review that has been drawn from articles published in the past two decades (2000-2020). The development of a sense of community in the on-line environment has been identified to be one of the main difficulties in e-learning education across this whole review. In order to create an efficient online learning community, it could be claimed that both instructors and students must work together to engage in extensive collaboration and engagement with both students and one another. Since educational institutions must be ready for the sustainability regarding e-learning adoption, the presented work argues that there is a requirement for better identification and knowledge of this. The results revealed that a university's competency and capacity for meeting elearning demand stemmed from actual requirement for the implementation of e-learning for specific academic environment hinged on sustainability related to implementation of e-learning. In addition to that, each university's local culture influenced and supported the implementation process, where the inhibiting and driving variables had a substantial effect on the continuity and outcome of the process. The range of digital tools that can successfully encourage social interactions as well as the learning community need to be further researched. With regard to higher education, there is an increase in innovative assessments of variables to assess learning results in the settings of digital learning. Researchers should carefully evaluate their study designs and study subjects in digital learning environments for this reason, as well as how to handle measuring learning.
One study whose importance has significantly grown in recent years is lip-reading, particularly with the widespread of using deep learning techniques. Lip reading is essential for speech recognition in noisy environments or for those with hearing impairments. It refers to recognizing spoken sentences using visual information acquired from lip movements. Also, the lip area, especially for males, suffers from several problems, such as the mouth area containing the mustache and beard, which may cover the lip area. This paper proposes an automatic lip-reading system to recognize and classify short English sentences spoken by speakers using deep learning networks. The input video extracts frames and each frame is passed to the Viola-Jone
... Show MoreSelf-driving automobiles are prominent in science and technology, which affect social and economic development. Deep learning (DL) is the most common area of study in artificial intelligence (AI). In recent years, deep learning-based solutions have been presented in the field of self-driving cars and have achieved outstanding results. Different studies investigated a variety of significant technologies for autonomous vehicles, including car navigation systems, path planning, environmental perception, as well as car control. End-to-end learning control directly converts sensory data into control commands in autonomous driving. This research aims to identify the most accurate pre-trained Deep Neural Network (DNN) for predicting the steerin
... Show MoreIn this paper, a new class of non-convex functions called semi strongly (
Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F
... Show MoreThis study aimed at investigating the effect of using computer in
Efficiency of Training Programme of Science Teachers in Ajloun District in
Jordan.
1- What is the effect of using computer in program for the two groups
2- ( the experimental and control group ) .
3- Are there any statistics different in the effect of using computer
program for the two groups ?
4- Are there any statistics (comparison ) or different of the effect of the
effect of using computer program refer to the sex (male or female )?
The community of the study consisted of all the science student in
educational directorate of Ajloun district for the academic year 2009 –
2010, they are (120) ( male and female) . The sample of the study<
This study aims to find out the effect of the mediator on scaffolding fourth yearstudent- teachers' teaching competencies and their self-efficacy. The present study combines scaffolding and self-efficacy by using a mediator on scaffolding students affects teaching competencies and selfefficacy and from the results of which the existence of student-teachers’ selfawareness was ensured as an effect of the same independent variable. The model affects their teaching competencies and led them to be aware of the needs of their pupils and themselves.
During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve
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