A significant number of educational stakeholders are concerned about the issue of digitalization in higher educational institutions (HEIs). Digital skills are becoming more pertinent throughout every context, particularly in the workplace. As a result, one of the key purposes for universities has shifted to preparing future managers to address issues and look for solutions, including information literacy as a vital set of skills. The research of educational technology advances in higher education is now being discussed and debated, with various laws, projects, and tactics being offered. Digital technology has been a part of the lives of today’s children from the moment they are born. There are still many different types of digital divisions that exist in our society, and they affect the younger generation and their digital futures. Today’s students do not have the same level of preparation for the technology-rich society they will have. Universities and teaching should go through a significant digital transformation to fulfill the demands of today’s generation and the fully digitized world they will be living in. The COVID-19 pandemic has quickly and unexpectedly compelled HEIs and the educational system to engage in such a shift. In this study, we investigate the digital transformation brought about by COVID-19 in the fundamental education of the younger generation. Additionally, the study investigates the various digital divides that have emerged and been reinforced, as well as the potential roadblocks that have been reported along the way. In this paper, the study suggests that research into information management must better address students, their increasingly digitalized everyday lives, and basic education as key focus areas.
<p>Educational Data Mining (EDM) research has taking an important place as it helps in exposing useful knowledge from educational data sets to be employed and serve several purposes such as predicting students’ achievements. Predicting student’s achievements might be useful for building and adopting several changes in the educational environments as a re-action in the current educational systems. Most of the existing research have used machine learning to predict students’ achievements by using diverse attributes such as family income, students gender, students absence and level etc. In this paper, the effort is made to explore the effectiveness of using the deep learning algorithm more precisely CNN to predict students’ achievements which could hlp in predicting if student will be able to finish their degree or not. The experimental results reveal how the proposed model outperformed the existing approaches in terms of prediction accuracy.</p>
Problem statement: Extensive research efforts in the area of Natural Language Processing (NLP) were focused on developing reading comprehension Question Answering systems (QA) for Latin based languages such as, English, French and German. Approach: However, little effort was directed towards the development of such systems for bidirectional languages such as Arabic, Urdu and Farsi. In general, QA systems are more sophisticated and more complex than Search Engines (SE) because they seek a specific and somewhat exact answer to the query. Results: Existing Arabic QA system including the most recent described excluded one or both types of questions (How and Why) from their work because of the difficulty of handling these questions. In this study, we present a new approach and a new questionanswering system (QArabPro) for reading comprehension texts in Arabic. The overall accuracy of our system is 84%. Conclusion/Recommendations: These results are promising compared to existing systems. Our system handles all types of questions including (How and why).
Abstract:Companies all over the world try to get the benefits from accessing to information that is available in social media to improve their performance and increase their revenue, processing heterogeneous type of data to extract the valuable data is a problem that many organizations try to solve. One of the most important trends is in general known as "Big Data", technology for Storing, Processing and analyzing data, companies are Managing data in order to use it in new levels and direct decision makers to make agile decisions in real time, Big Data trend have the capability to guide a revolutionary transformation in research, invention, and business marketing. In this research we highlight some aspects of Big Data and its importance on organizations' business performance and how companies can use the famous open source platform Hadoop to process data to gain the competitive advantage.
Summary Software fault prediction is a process of developing modules that are used by developers in order to help them to detect faulty classes or faulty modules in early phases of the development life cycle and to determine the modules that need more refactoring in the maintenance phase. Software reliability means the probability of failure has occurred during a period of time, so when we describe a system as not reliable, it means that it contains many errors, and these errors can be accepted in some systems, but it may lead to crucial problems in critical systems like aircraft, space shuttle, and medical systems. Therefore, locating faulty software modules is an essential step because it helps defining the modules that need more refactoring or more testing. In this article, an approach is developed by integrating genetics algorithm (GA) with support vector machine (SVM) classifier and particle swarm algorithm for software fault prediction as a stand though for better software fault prediction technique. The developed approach is applied into 24 datasets (12‐NASA MDP and 12‐Java open‐source projects), where NASA MDP is considered as a large‐scale dataset and Java open‐source projects are considered as a small‐scale dataset. Results indicate that integrating GA with SVM and particle swarm algorithm improves the performance of the software fault prediction process when it is applied into large‐scale and small‐scale datasets and overcome the limitations in the previous studies.
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