The spread of COVID-19 poses a threat to humanity, as this pandemic has forced many global activities to close, including educational activities. To reduce the spread of the virus, education institutions have been forced to switch to e-learning using available educational platforms, despite the challenges facing this sudden transformation. In order to further explore the potentials challenges facing learning activities, the focus of this study is on e-learning from students’ and instructor’s perspectives on using and implementing e-learning systems in a public university during the COVID-19 pandemic. The study targets the society that includes students and teaching staff in the Information Technology (IT) faculty at the University of Benghazi. The descriptive-analytical approach was applied and the results were analyzed by statistical methods. Two types of questionnaires were designed and distributed, i.e., the student questionnaire and the instructor questionnaire. Four dimensions have been highlighted to reach the expected results, i.e., the extent of using e-learning during the COVID-19 pandemic, advantages, disadvantages and obstacles of implementing E-learning in the IT faculty. By analyzing the results, we achieved encouraging results that throw light on some of the issues, challenges and advantages of using e-learning systems instead of traditional education in higher education in general and during emergency periods.
The conventional information retrieval (IR) framework consists of four primary phases, namely, pre-processing, indexing, querying and retrieving results. Some phases of the current Arabic IR (AIR) framework have several drawbacks. This research aims to enhance an AIR by improving the processes in a conventional IR framework. We introduce an enhanced stop-word list in the pre-processing level and investigate several Arabic stemmers. In addition, an Arabic WordNet was utilized in the corpus and query expansion levels. We also adopted semantic information for the Pseudo Relevance Feedback. The enhanced Arabic IR framework was built and evaluated using TREC 2001 data. The technique of using the Arabic WordNet to build a semantic relationship between query and corpus in two levels, that is, the corpus and query levels, is a new one. The enhanced AIR framework demonstrated an improvement by 49% in terms of mean average precision, with an increase of 7.3% in recall compared with the baseline framework.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.