Purpose of study: The study aimed at establishing the implications of the COVID-19 pandemic on project delivery in the UAE with a significant focus on the construction industry. The study also sought to determine some of the negative implications of COVID-19 for the construction industry. Methodology: The within-subjects research design was employed in the study. Purposive and simple random sampling was used in the selection of the respondents. A total of 116 project managers in the construction industry were sampled for the study. Data was collecting using self-administered online questionnaires. The SPSS software was applied to analyze the collected data using the paired samples t-test analysis method to compare the means of the projected number of days taken to complete a project before and during the COVID-19 period. Main Findings: The study findings revealed that there is a statistically significant effect of the COVID-19 pandemic on the number of days taken to complete projects in the construction industry in the UAE. The findings of the study revealed that the pandemic resulted in a lack of access to raw materials and labor, thereby leading to a delay in the completion of projects. Application of the study: The revelation of the implications would inform the policy and decision-makers in the country to devise effective ways of addressing the challenges for the stability of the sector. The researcher recommends the same study to be replicated in other areas to identify the effects the pandemic has had on other industries. Another study should also be conducted on the effective strategies that should be adopted to address the effects caused by the COVID-19 pandemic on the construction industry in the UAE. Novelty/originality of the study: This is contemporary studies that deal with a current issue. The study concluded that the construction industry became a victim of COVID-19 to the extent that it has brought its projects to a halt and significantly eroded the market of its beneficiaries. Unlike other industries, construction projects cannot accommodate distance working but facing challenges making on-time delivery impossible and therefore construction industry is at risk.

Today, it is almost impossible to implement teaching processes without using information and communication technologies (ICT), especially in higher education. Education institutions often use learning management systems (LMS), such as Moodle, Edmodo, Canvas, Schoology, Blackboard Learn, and others. When accessing these systems with their personal account, each student’s activity is recorded in a log file. Moodle system allows not only information sav-ing. The plugins of this LMS provide a fast and accurate analysis of training sta-tistics. Within the study, the capabilities of several Moodle plugins providing the assessment of students' activity and success are reviewed. The research is aimed at discovering possibilities to improve the learning process and reduce the num-ber of underperforming students. The activity logs of 124 participants are ana-lyzed to identify the relations between the number of logs during the e-course and the final grades. In the study, a correlation analysis is performed to determine the impact of students' educational activity in the Moodle system on the final assess-ment. The results reveal that gender affiliation correlates with the overall perfor-mance but does not affect the selection of training materials. Furthermore, it is shown that students who got the highest grades performed at least 210 logs dur-ing the course. It is noted that the prevailing part of students prefers to complete the tasks before the deadline. The study concludes that LMSs can be used to pre-dict students' success and stimulate better results during the study. The findings are proposed to be used in higher education institutions for early detection of stu-dents experiencing difficulties in a course.

OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible.

Data generated on Twitter has become a rich source for various data mining tasks. Those data analysis tasks that are dependent on the tweet semantics, such as sentiment analysis, emotion mining, and rumor detection among others, suffer considerably if the tweet is not credible, not real, or spam. In this paper, we perform an extensive analysis on credibility of Arabic content on Twitter. We also build a classification model (CAT) to automatically predict the credibility of a given Arabic tweet. Of particular originality is the inclusion of features extracted directly or indirectly from the author's profile and timeline. To train and test CAT, we annotated for credibility a data set of 9, 000 Arabic tweets that are topic independent. CAT achieved consistent improvements in predicting the credibility of the tweets when compared to several baselines and when compared to the state-of-the-art approach with an improvement of 21% in weighted average Fmeasure. We also conducted experiments to highlight the importance of the userbased features as opposed to the contentbased features. We conclude our work with a feature reduction experiment that highlights the best indicative features of credibility.

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