Big Data courses in which students are asked to carry out Big Data projects are becoming more frequent as a part of University Engineering curriculum. In these courses, instructors and students must face a series of special characteristics, difficulties and challenges that it is important to know about beforehand, so the lecturer can better plan the subject and manage the teaching methods in order to prevent students' academic dropout and low performance. The goal of this research is to approach this problem by sharing the lessons learned in the process of teaching e-learning courses where students are required to develop a Big Data project as a part of a final degree/master course. In order to do so, a survey was carried out among a group of students enrolled in those kinds of courses during the last years. The quantitative and qualitative analysis of the obtained data led us to present a series of lessons learned that may help other participants (both students and lecturers) to better study, design and teach similar courses. In addition, the results shed light on possible existing open problems in the area of Big Data project development. Both the methodology used and the survey designed in this research were validated by a group of experts in the area using a formal statistical approach at a significance level of p<0.008, which support the validity of the lessons learned.
E-Government is defined as a system utilizing the Internet and the world-wide-web for delivering government informa-tion and services to citizens. This system reduces the processing costs, improves service delivery, and increases trans-parency and communication between a government and its citizens. The aim of this paper is to propose a new model to measure the readiness of e-Government according to cultural factors. By assessing to which degree these cultural factors are present/absent in a country and which of them have a significant impact on government readiness, the government will be able to identify their weakness and strength points, then build a preparing plan that can help them to achieve the readiness required toward a successful implementation of the e-Government systems
The e-learning system has gained a phenomenal significance than ever before in the present COVID-19 crisis. The E-learning delivery mechanisms have evolved to enhanced levels facilitating the education delivery with greater penetration and access to mass student population worldwide. Nevertheless, there is still scope to conduct further research in order to innovate and improve higher quality delivery mechanism using the state-of-the-art information and communication technologies (ICT) available today. In the present pandemic crisis all the stakeholders in the higher education system, i.e., the governments, institutions, and the students expect seamless and efficient content delivery via e-learning platforms. This study proposes the adoption of the e-learning system by the integration of the model proposed by Delon and Mcclean “Information System Success Model” in Jazan University, Kingdom of Saudi Arabia (KSA) and further attempts to identify the factors affecting E-learning applications' success among the students.
The data were gathered from 568 respondents. The Statistical Package for the Social Sciences version 26 (SPSS v.26.0) was used for the data analysis and one-way ANOVA is applied to test the hypothesis.
The overall results of this study allude to the fact that there is a significant relationship between Information system Success Model factors and the adoption of e-learning systems. The research results indicated that the information system success model has a strong associating cost-benefit value towards the adoption of e-learning systems across the Jazan University that may be further expanded to the other Saudi universities.
In this paper, an experimental study was conducted on three techniques for Arabic text classification. These techniques are Support Vector Machine (SVM) with Sequential Minimal Optimization (SMO), Naïve Bayesian (NB), and J48. The paper assesses the accuracy for each classifier and determines which classifier is more accurate for Arabic text classification based on stop words elimination. The accuracy for each classifier is measured by Percentage split method (holdout), and K-fold cross validation methods, along with the time needed to classify Arabic text. The results show that the SMO classifier achieves the highest accuracy and the lowest error rate, and shows that the time needed to build the SMO model is much lower compared to other classification techniques.
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