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Model for Improving the level of Labourers Productivity through the Use o BIM and Clould technology in UAEBuilding Construction Projects
No full textImportance of the research
This study examined the capabilities of building information modelling (BIM) and cloud technologies and their impact on improving labourers’ productivity in building construction projects in the United Arab Emirates (UAE). Thus, there was a need for this research to identify the key factors affecting labourers’ productivity in construction projects in the UAE and their impact magnitude.
Problem and aim
The research aimed to examine how advanced technologies such as BIM and cloud could be used to improve labourers’ productivity during the delivery of building construction projects in the UAE. The research proposed a model that comprises of Building Information Modelling (BIM) and cloud technology determinants that could be used to improve labourers’ productivity in the UAE building construction sector.
Method
The study adopted a quantitative method approach to achieve the intended research objectives. The population of the study comprised construction contractors and consultants working on building construction projects in the UAE. The key targeted participants who provided the data required for this study were project managers, supervisors, foremen, inspectors, project engineers, and consultant resident engineers. Analysis was performed using IBM-SPSS and Hayes process.
Findings
The research proposed and validated a model that comprises BIM, cloud technology and labourers’ productivity determinants that can be used to improve labourers’ productivity in the UAE building construction sector. As shown in this study, the advancement of BIM and cloud technologies can democratize access to vital information, tools, and techniques, which can be used to create a collaborative team environment. Within the context of the UAE, this research has also shown how these technologies have emerged as a means of improving productivity in the construction sector.
Conclusion
The research identified several factors affecting construction labourers’ productivity. Among the 42 factors identified from the reviewed literature. From the findings of the study, it was found that the UAE construction sector is still facing labourers’ productivity challenges, and the industry has one of the lowest productivity rates. As a result, it was revealed that implementing BIM and cloud technologies facilitates the successful delivery of building construction projects. In addition, the study further established that BIM and cloud technologies can be used to facilitate integrated project delivery.
Future research
The study achieved its main aim of proposing a model that comprises BIM and labourers’ productivity determinants’. Although the findings and the conclusion of the study have added value to the existing knowledge on the use of BIM to improve labourers’ productivity in the UAE, it is important that further research is carried out in the form of a case study to evaluate the impact of the model’s proposed context
Challenges in Effective Curriculum Implementation: A Study of Leaders and Teachers in a Private School in Dubai
No full textThe process of putting an educational curriculum into practice, which includes delivering the planned instructional content, methodologies, and assessment strategies in a classroom or other educational setting, is referred to as curriculum implementation. It includes converting the aims and objectives of the curriculum into practical learning opportunities, making sure that it is in line with academic standards, and making adjustments for the wide range of needs of the students. In order to implement the curriculum effectively, teachers must work together, assess continuously, and be adaptable to meet changing student requirements. The study was conducted in one of the private schools in Dubai. The aim of the study was to identify the challenges faced by school stakeholders (teachers, MLL, and the SLT) regarding effective curriculum implementation and discussed several approaches that facilitate curriculum reform. The researcher included the findings of some studies that have been done related to the topic. In order to achieve the aim of the study, the qualitative method was used. The study included 17 participants. A questionnaire was sent to 8 teachers and 4 department heads. In addition, 5 members of the senior leadership team were interviewed to compare and contrast the views of the teachers. The replies from the interviews and open-ended questionnaires were interpreted using thematic analysis. It was shown by the study that the department heads and teachers have an excessive amount of work related to curriculum implementation. They face unique difficulties in doing their duties. On the other hand, the senior leadership team is in charge of observing and assessing the curriculum in order to ensure that the teachers are implementing it effectively. The study suggests that educational leaders should be responsible for offering direction, encouragement, and a clear vision for the implementation of the curriculum. In addition, policymakers ought to pass policies that promote effective curriculum implementation and deal with underlying issues
PSYCHOLOGICAL EMOTION RECOGNITION OF STUDENTS USING MACHINE LEARNING BASED CHATBOT
No full textAnxiety and depression can have a significant impact on students’ academic performance, however, these
mental health impacts were increased during the Covid-19 pandemic, and accordingly students and
parents need some people to share their feelings together; however, there are different types of social
media apps and platforms such as Facebook, Twitter, Reddit, Instagram, and others. Twitter is one of the
most popular social application that people prefer to share their emotional states. Interestingly, the
psychologist and computer scientists are inspired to study these emotions. In this paper, we propose a
chatbot for detecting the students feeling by using machine-learning algorithms. The authors used a
dataset of tweets from Kaggle’s paltform, and it includes 41157 tweets that are all related to the COVID 19. The tweets are classified into categories based on the feeling: Positive and negative. The authors
applied Machine Learning algorithms, Support Vector Machines (SVM) and the Naïve Bayes (NB) and
accordingly they compared the accuracy between them. In addition to that, the classifiers were evaluated
and compared after changing the test split ratio. The result shows that the accuracy performance of SVM
algorithm is better than Naïve Bayes algorithm, but the speed is extremely slow compared to Naive Bayes
model. In future, other neural network algorithms such as the RNN, LSTM will be implemented, and Arabic
tweets will be included in the future
Employee’s Strategic Alignment Effects on Employee’s Performance, the Mediating Role of Employee’s Engagement
No full textThis research explores employees' strategic alignment Impact on employee engagement and performance. Moreover, the study investigates the mediation role of employee engagement in this set of relationships. Accordingly, the main research question is, "what is the impact of Employee strategic alignment on employee engagement and performance".
Previous studies have approached strategic alignment, shared goals, and their impact on employee performance and engagement. Nonetheless, there is not enough empirical research regarding employees' strategic alignment and their impact on employee performance and engagement in a governmental organisation. Furthermore, this research looks into the function of employee engagement as a mediator; investigating the mediation role helps in understanding the mechanism by which alignment might affect employee performance and whether this impact is direct or indirect. The employee's strategic alignment is addressed depending on Kaplan and Norton's (2001c) work regarding the strategic-focused organisation, whereby principle three, "Make strategy everyone's everyday job", is the centre of this research. Employee strategic alignment is investigated through three dimensions: creating strategic awareness, defining personal objectives and linking compensations to performance. In order to explain the hypothesised relationship in this research, three theories were followed, the Goal setting theory by Edwin Locke, the Job demand-resources (JD-R) model by Baker and Demerouti, and the engagement theory by Kahan. This investigation follows a positivism philosophy with a deduction approach; the research is quantitative that adopts a survey strategy. It is cross-sectional in time and relies on random sampling techniques and a questionnaire as a data-gathering tool.Dubai Governmen
Attitude and Readiness of Teachers to Impart Value Education: Exploring Teachers’ Experiences and Views from a Private School in Sharjah
No full textThis research sought to examine teachers' attitudes and readiness towards imparting value education at a private school in Sharjah. It aimed to explore existing literature on the subject, measure teachers' attitudes using a quantitative questionnaire, and gain deeper understanding through a qualitative questionnaire. The research was guided by four questions, investigating the attitudes and readiness of teachers to impart value education, perceptions of teachers' attitudes in recent research, teachers' own attitudes and readiness, and their experiences and views towards imparting value education. The findings from the quantitative data revealed that the majority of teachers have a positive attitude towards teaching value education and feel comfortable, satisfied, and confident in doing so. Furthermore, a statistically significant moderate positive correlation was found between teachers' attitudes towards and readiness for imparting value education. In addition to the quantitative findings, the qualitative data offered deeper insights into the themes of importance to teachers. These themes included the necessity of collaboration and partnerships, the call for extensive teacher training and professional development, the integration of value education across all subjects, the utilization of technology and resources, the importance of practical examples and real-life contexts, the promotion of moral and ethical standards, and the awareness of mental health. The study's findings provide useful insights for schools and policymakers in UAE and beyond, informing strategies to enhance the effective teaching of value education. The findings underline the importance of comprehensive teacher training, collaborative efforts among educators, and the integration of technology and real-life contexts in teaching. It also highlights the need to consider the moral, ethical, and mental health aspects of education
A Qualitative study in two Dubai Government Sector organisations
No full textThe public sector is under increasing pressure to deliver efficient services, attain high levels of customer satisfaction, and meeting the pressing societal needs. Consequently, there is need for the implementation of technology to ensure efficiency within the public sector organizations. Artificial intelligence (AI) has emerged as one of the useful technologies available for organizations to enhance efficiency and deliver quality and satisfactory services to customers. The purpose of this dissertation was to carry out an assessment of AI technology implementation in the operational workplace processes and its impact at the individual and team level. The study is qualitative and involved in depth exploration of AI technology implementation in two governmental entities, referred to as Organization X and Organization Z. Data was collected through semi-structured interviews with 15 participants from Organization X and 16 from Organization Z. The participants were involved in AI technology implementation within their operational workplace processes and were highly informative about the impact of AI implementation at the individual and team levels. The textual data collected through the interviews was thematically analyzed using traditional thematic analysis.
The findings underlined the impact of AI in enhancing employee autonomy through independent decision-making. AI also enhances competence through learning how to apply complex technologies in delivering services to citizen. In addition, AI enhance employee relatedness through increased connectedness emanating from data sharing across departments. AI implementation also leads to increased team integration through cooperative working. Finally, AI implementation advances creativity and innovativeness through the discovery of new ideas to improve the organization by employees.
The study specifically contributes to the research question, the SDT theory, team effectiveness theory, and AI technology research. The first contribution of the research is that it enhanced the comprehension of how the autonomy of employees in the use of AI could be manipulated to achieve greater levels of innovation through AI. Second, the study contributed to the understanding of how both the SDT theory and the team effectiveness theories could be used in the implementation of AI at the individual and team level by emphasizing how employees could complement each other’s skills, collaborate, and solve problems while using high technological capacities such as AI.
Keywords: AI, autonomy, competence, team integration, creativity, Dubai, relatednes
The impacts of professional development program on school improvement: a study among school teachers at a private school in Abu Dhabi
No full textThis study examines the effectiveness of conducting continuous professional development (CPD) programs for school teachers on students' achievement, encompassing attainment, progress, and study skills. CPD has gained considerable attention in the education sector as a means to enhance teachers' knowledge, skills, and instructional practices. The study employs a mixed-methods approach, incorporating qualitative interviews and quantitative data analysis. The findings demonstrate that CPD initiatives positively impact teachers' pedagogical knowledge, instructional strategies, and overall professional growth. Additionally, the study reveals a significant correlation between teachers' participation in CPD and students' academic achievement, including improved attainment levels, enhanced progress, and strengthened study skills. These results highlight the importance of investing in continuous professional development for teachers, as it directly influences student outcomes and contributes to the overall quality of education
Enhancing Arabic Offensive Tweet Classification: An Ensemble Approach Integrating AraBERT, Neural Networks, and LSTM Models
No full textThis thesis addresses the crucial research problem of accurate detection and moderation of offensive language in Arabic text, considering the intricacies posed by the language's complex morphology, dialectal variations, orthographic ambiguity, orthographic noise, limited linguistic resources, and the necessity for comprehensive coverage of offensive language expressions. The research objectives are delineated through four key research questions. Firstly, the study aims to identify the existing research gaps in Arabic Text Classification (ATC) through an extensive and rigorous systematic literature review. The study adopts a scholarly and formal approach, aiming to identify the specific areas within ATC research that lack comprehensive exploration or exhibit inadequacies in existing knowledge. This endeavor is grounded in the rigorous analysis and synthesis of relevant academic literature, ensuring a meticulous examination of the current state of research in ATC. Secondly, it investigates the effects of employing novel pre-processing methods on the performance of Arabic Text Classification. Thirdly, the research endeavors to determine the most effective model for enhancing the accuracy of Arabic offensive text classification by introducing a novel approach using pre-trained models; AraBERT model in conjunction with fully connected neural networks (NN) and long short-term memory (LSTM) networks. Finally, the study evaluates the proposed model's ability to classify Arabic offensive text effectively. The research methodology consists of two integral parts, comprising dataset description, the proposed framework. The dataset description provides insights into the two datasets utilized, namely OSACT and SEMEval. The framework elucidates the proposed model, which leverages a combination of pretrained models and neural networks, thereby achieving a high level of effectiveness in classifying Arabic offensive text. The model's performance is meticulously assessed using various evaluation metrics, including accuracy and F1-macro score, and is compared against other classifier models. The research findings demonstrate the superiority of the proposed model over the baseline AraBERT model, with the proposed model achieving an accuracy of 0.870 compared to the baseline accuracy of 0.820, along with an F1-score of 0.853 compared to the baseline's 0.800. This emphasizes the model's exceptional capacity to accurately identify offensive content in Arabic text. The implications of this research extend to diverse domains and stakeholders, encompassing decision makers, developers, and policy makers. The insights garnered from the study can be instrumental in making informed decisions pertaining to the integration of Arabic text classification systems in various operational settings. By comprehending the proposed model's performance and efficacy, decision makers can assess its potential impact on optimizing processes such as information retrieval, content filtering, and sentiment analysis in Arabic text. In conclusion, this thesis contributes significantly to the existing literature by addressing the complexities associated with offensive language identification in Arabic text and introducing an innovative approach that integrates pretrained models with deep learning techniques and neural networks. The demonstrated effectiveness and superior performance of the proposed model underscore its potential for practical implementation in real-world scenarios, thereby bolstering the field of Arabic offensive text classification
Evaluation of LEED Interior Design Environment to Improve the Indoor Environmental Quality Through Enhancing Lighting Parameters of UAE Campus Buildings
No full textThis study investigates the integration of interior artificial lighting, daylight, and quality views, all of which fall under Indoor Environmental Quality (IEQ), a key LEED (ID+C) credit in interior design environments. The research focuses on a campus building in the UAE, specifically the British University in Dubai. Effective design of these variables can enhance visual comfort and improve the building's indoor environment, including lighting quality and user performance. The methodology combines online surveys, field measurements, and computer simulations using DIALux EVO 11. It also considers human factors among students and staff through a mixed-method approach to validate findings and correlate data. The study aims to connect objective and subjective assessments of environmental factors in both daylight and artificial lighting, including lux value, uniformity ratio, glare, and daylight factor, according to EN 12464-1 (2021) standards. Field measurements validate outcomes from the base model in the software. The research offers practical insights into enhancing IEQ in campus buildings through various lighting strategies and scenarios. It analyzes different spaces based on LEED classifications, such as auditoriums, classrooms, libraries, and administration areas. The key findings emphasize the need to balance lighting parameters and conditions to achieve optimal results. The results show improved lighting conditions, enhanced lux values, glare reduction, and balanced light distribution across various spaces, benefiting user experience and performance. For instance, in the corridor areas, the lux value met the target value, ensuring the desired lighting quality. In other locations, such as the auditorium and classroom FF-111, lux values significantly exceeded the target levels, with values reaching 527lx and 602lx, respectively, greatly enhancing overall lighting conditions. The study's actionable insights cater to designers, architects, and stakeholders involved in campus building projects in the UAE. By integrating sustainable standards, this research promotes environmentally sustainable indoor environments that prioritize occupant well-being and enhance the quality of educational spaces
Corrosion Monitoring Technologies for Reinforced Concrete Structures: A Review
No full textReinforced concrete (RC) structures are susceptible to many problems which would lead ultimately to the degradation of RC structures or a total loss in the worst-case scenario. Corrosion represents one of the main degradation sources. Due to the vital impact size of corrosion of RC structures in the form of high maintenance demand and an increase in the total life cycle cost, corrosion monitoring, early detection, and timely remediation is considered a necessity and a crucial proactive measure to control corrosion and limit its impact. In this work, the main classifications of corrosion monitoring systems were explored, highlighting their features, advantages, disadvantages, and future recommended works. To achieve that a state-of-the-art literature review was employed. Six main categories were identified from the literature: visual inspection, electrochemical methods, elastic wave methods, electromagnetic methods, fiber optic sensing methods, and mechanical methods. The subcategories of each of these were reviewed highlighting its concepts, pros, cons, and outlook and future works. Irrespective of the adopted processes for corrosion monitoring, it was concluded that none of these methods represent an optimum solution by itself, where employing a combination of multiple systems is one way of optimizing its results. New technology, algorithms, data processing, and AI are new approaches to improving corrosion monitoring processes. However, it needs further development and research