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Leveraging Smart Home Training Kits as an Innovative Educational Tool to Foster Higher-Order Thinking Skills
Full text linkThis study aims to establish the impact of employing Smart Home Training Kits as a new approach to developing Higher-Order Thinking Skills (HOTS) in vocational education. Using a quasi-experimental design, the study involved two groups of vocational students: supporting them by an experimental group applying Smart Home Training Kits and a control group using conventional methods of instruction. Standard pre-tests and post-tests were administered among the students to evaluate the enhancement in the level of higher-order thinking skills, for aspects of critical thinking, problem-solving solving, and creativity. The findings also showed a work improvement in the experimental group compared to the group control group. The experimental group of students who were trained using the Smart Home Training Kits performed better when it came to the analysis, evaluation, and Synthesis of possible solutions regarding smart homes. Also, a number of the activity kits characterized the technical thing being taught in a detailed way that allowed the students to gain a more realistic understanding of the principles at work. The findings of this paper suggest that Smart Home Training Kits are one of the ways through which Higher-Order Thinking Skills can be effectively taught within the technical education training regime while closing the gap between theory and practical. This indicates that the assimilation of these kits in curricula could help the effective development of critical thinking and innovation at the expense of students to face the current world workplace challenges
Application of computer simulation technology in traditional building protection
Full text linkBackground: Computer simulation technology, especially virtual reality (VR) technology, offers an innovative solution for participating in architectural design by providing an immersive and interactive experiences. Aim: This research aims to provide the VR application for the protection of traditional buildings, focusing on how this technology can enhance stakeholder participation in the protection and preservation of historical structures. The aim is to evaluate the effectiveness of VR in facilitating a bottom-up approach to decision-making, thereby preserving cultural heritage. Method: To gather data, a random sample of 136 participants, including both local residents and architectural professionals, were engaged in VR simulations of renovation for traditional buildings. The VR environment presented two design schemes: one reflecting a traditional architectural style and the other featuring a modern approach. Participants interacted with both schemes using VR, and their feedback was collected through structured surveys. Statistical methods were employed to evaluate the quality of VR experiences and their impact on participant preferences and decision-making. Result: It indicate that VR technology significantly improves stakeholder engagement, with a majority of participants expressing a strong preference for traditional designs in terms of cultural protection. The immersive nature of VR was found to effectively replace traditional review methods, offering clearer insights into design intentions and facilitating informed decisions. Conclusion: VR technology proves to be a valuable tool in the protection of traditional buildings by enhancing participant engagement and supporting informed decision-making processe
Hybrid weighted sequential learnong technique for structural health monitoring using learning approaches
Full text linkAbstract- Structural Health Monitoring (SHM) plays a vital role in damage detection, offering significant maintenance and failure prevention benefits. Establishing effective SHM systems for damage identification (DI) traditionally requires extensive experimental datasets collected under varied operating and environmental conditions, which can be resource-intensive. This study introduces a novel approach to SHM by leveraging a Hybrid Weighted Sequential Learning Technique (HWSLT) classifier, which uses Finite Element (FE) computed responses to simulate structural behaviors under both healthy and damaged states. Initially, an optimal FE model representing a healthy, benchmark linear beam structure is developed and updated using experimental validation data. The HWSLT classifier is trained on SHM vibration data generated from this model under simulated load cases with uncertainty. This allows for minimal real-world experimental intervention while ensuring robust damage detection. Results demonstrate that the HWSLT classifier, trained with optimal FE model data, achieves high accuracy in predicting damage states in the benchmark structure, even when mixed with random disturbances. Conversely, data from non-ideal FE models produced unreliable classifications, underscoring the importance of model accuracy. These findings suggest that the integration of ideal FE models and deep learning offers a promising pathway for future SHM applications, with potential for reduced experimental costs and enhanced damage localization capabilities
A new hybrid approach based on machine learning for more efficient time series forecasting
Full text linkIntroduction: Forecasting new student enrollment in bachelor\u27s degree programs has emerged as a critical need for higher education institutions. Accurate enrollment predictions are essential for improving the student-teacher ratio and optimizing resource allocation.Methods: A hybrid approach combining statistical and machine learning techniques was proposed to develop accurate forecasting models. The study utilized the historical enrollment database of Ibn Zohr University, which included data from over twenty institutions dating back to 2003. This dataset was used to train and validate the proposed models.Results: The hybrid approach demonstrated superior accuracy compared to standalone statistical and machine learning models. The results indicated that the proposed method effectively captured enrollment trends and provided reliable forecasts.Conclusions: The study concluded that the hybrid approach offers a robust solution for enrollment forecasting in higher education. It highlighted the potential of combining statistical and machine learning techniques to improve prediction accuracy, thereby aiding institutions in better planning and resource management.
Design of a virtual platform for the promotion and trade of utilitarian ceramics from indigenous communities
Full text linkDigital commerce offers opportunities for the promotion of handicraft products, especially those with a strong cultural component such as utilitarian ceramics made by indigenous communities. This study aimed to design a virtual platform for the promotion and commercialization of ceramics inspired by the iconography of the Shawi indigenous communities, preserving their cultural traditions and improving their economic opportunities. The methodology employed included the application of Kanban as an agile approach for the design of the platform. Technological tools such as PHP, MySQL, HTML and CSS were used to ensure the functionality and scalability of the system. The platform included functionalities such as an interactive catalog, a shopping cart and an educational section on the history of the pieces. In addition, we worked in collaboration with the artisans to integrate cultural elements into the design. The results showed that the platform not only facilitates access to digital markets, but also strengthens the cultural valorization of Shawi ceramics. In conclusion, this model represents an effective solution for linking technology, commerce and culture, with practical implications for economic development and cultural preservation of indigenous communities
Enhancing Metadata Management And Data-Driven Decision-Making In Sustainable Food Supply Chains Using Blockchain And AI Technologies
Full text linkSustainability in food supply chains is a critical global challenge, particularly in resource-constrained regions like Jordan, where operational inefficiencies and environmental concerns are prevalent. This study explores the integration of blockchain and artificial intelligence (AI) technologies to enhance metadata management, forecast sustainability metrics, and support decision-making in Jordan’s food supply chains. Blockchain\u27s ability to improve metadata accuracy, standardization, and traceability, combined with AI’s predictive capabilities, offers a powerful solution for addressing sustainability challenges.MethodsThe research employed a mixed-methods approach, combining real-time data from blockchain transaction logs, AI-generated forecasts, and stakeholder surveys. Blockchain data from platforms like Hyperledger Fabric and Ethereum provided insights into metadata accuracy and traceability. AI models were developed using machine learning techniques, such as linear regression, to forecast food waste reduction, carbon footprint reduction, and energy efficiency. Multi-Criteria Decision Analysis (MCDA), using AHP and TOPSIS, was applied to evaluate trade-offs among sustainability goals.ResultsThe results revealed significant improvements in metadata accuracy (from 83% to 96.66%) and reductions in traceability time (from 4.0 to 2.35 hours) following blockchain implementation. AI models demonstrated high predictive accuracy, explaining 88%, 81%, and 76% of the variance in food waste reduction, carbon footprint reduction, and energy efficiency, respectively. ConclusionThis study underscores the transformative potential of blockchain and AI technologies in achieving sustainability goals. By fostering transparency, predictive insights, and data-driven decision-making, these innovations can address key challenges in Jordan’s food supply chains, offering actionable strategies for stakeholders
Exploring the Influence of Green Human Resource Management on Risk Management: The Mediating Effect of Agile Leadership
Full text linkThis study explores the relationship between Green Human Resource Management (GHRM) practices, agile leadership, and organizational risk management. The study collected data from 501 managers in 130 businesses registered on the Amman Stock Exchange in Jordan. It used a questionnaire to gather information on their organization\u27s GHRM practices, agile leadership style, and risk management strategies. SPSS and Amos were used to analyze the data. The results show that GHRM practices positively impact risk management, supporting previous research on the influence of GHRM on fostering sustainable practices in organizations. Furthermore, the study finds that the agile leadership style moderates the relationship between GHRM practices and risk management, highlighting the importance of leadership in increasing the efficiency of sustainable practices in organizations. The findings have implications for managers and policymakers, emphasizing the need for organizations to prioritize GHRM practices and cultivate agile leadership to improve their risk management strategies, expand their innovation skills, and encourage sustainable practices. Policymakers can also use the results to support sustainability efforts and urge businesses to follow good governance and risk management practices. The findings show the significance of agile leadership as a mediating variable and emphasize the relevance of organizations prioritizing GHRM practices to achieve sustainable results
The Impact of Digital System Tools on Project Management Efficiency in Educational Institutions: The Mediating Role of Communication Quality within the Team (Language)
Full text linkIntroduction: This research explores the impact of digital system tools on project management efficiency in educational institutions in Jordan and Saudi Arabia, with a particular focus on the role of team communication quality. In the context of educational project management, the study examines how digital tools influence project outcomes and communication practices within teams.Methods: The study is based on an experimental design with three hypotheses, which were tested using questionnaire data collected from 176 respondents. The first hypothesis evaluates the positive effects of digital system tools on project management efficiency. The second hypothesis investigates the effects of digital tools on the quality of team communication. The third hypothesis examines the intervening role that communication quality plays in the relationship between digital tool usage and project efficiency.Results: The research confirms all three hypotheses. It demonstrates that the use of digital systems significantly enhances both project management efficiency and team communication quality. Furthermore, communication quality is found to act as a mediator in the relationship between digital system tools and project efficiency, amplifying the impact of digital tools on project outcomes.Conclusions: The findings suggest that educational institutions in Jordan and Saudi Arabia should prioritize the adoption of digital tools and focus on improving team communication methods. By doing so, they can significantly enhance project management efficiency and achieve better project results. The study underscores the importance of integrating digital tools and fostering strong communication practices to improve educational project management
The Impact of Data-Driven Decision-Making, Real-Time Analytics, and Ethical Data Practices on HR Performance and Employee Satisfaction
Full text linkIntroduction: The importance of this study is to investigate decision-making by making decisions about data and real-time analytics and practicing ethical data on human resource performance and employee satisfaction.Objective: The study was conducted at Zain Telecommunications Company Jordan through designing a questionnaire for a segmentation research in the telecommunications and exhibitions company and 220 suitable samples were removed to analyze the structural equation modeling program method SEM.Method: The diversity of independent studies was indicated through the contracts indicating it, so multiple choices were used as evidence, workforce turnover forecasts, performance measures were available to indicate the correct decision-making to the data. Its employees were used in real time, tracking the productivity of dynamic workforce workers, and instant questionnaire mechanisms to indicate real-time analytics.Result: Transparency in its data use policies, implementation of data privacy standards, and algorithmic fairness were used in innovative processes to indicate ethical data practices. Through the questionnaire that was distributed, these parties\u27 studies were conducted on improving the performance of human resources and employee satisfaction.Conclusion: His studies have concluded by integrating his three main areas of accurate decision making of his research, real-time analysis and practice of creative data and performance significantly improves the HR outcomes he chooses from employee satisfaction by choosing his specialty on data keeping pace with organizational goals by choosing his evidence
Development of a Hybrid CNN-BiLSTM Architecture to Enhance Text Classification Accuracy
Full text linkIntroduction: Natural Language Processing (NLP) has experienced significant advancements to address the growing demand for efficient and accurate text classification. Despite numerous methodologies, achieving a balance between high accuracy and model stability remains a critical challenge. This research aims to explore the implementation of a hybrid architecture integrating Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) with FastText embeddings, targeting effective text classification.Methods: The proposed hybrid architecture combines the CNN\u27s ability to capture local patterns and BiLSTM\u27s temporal feature extraction capabilities, enhanced by FastText embeddings for richer word representation. Regulatory mechanisms such as Dropout and Early Stopping were employed to mitigate overfitting. Comparative experiments were conducted to evaluate the performance of the model with and without Early Stopping.Results: The experimental findings reveal that the model without Early Stopping achieved a remarkable accuracy of 99%, albeit with a higher susceptibility to overfitting. Conversely, the implementation of Early Stopping resulted in a stable accuracy of 73%, demonstrating enhanced generalization capabilities while preventing overfitting. The inclusion of Dropout further contributed to model regularization and stability.Conclusions: This study underscores the significance of balancing accuracy and stability in deep learning models for text classification. The proposed hybrid architecture effectively combines the strengths of CNN, BiLSTM, and FastText embeddings, providing valuable insights into the trade-offs between achieving high accuracy and ensuring robust generalization. Future work could further explore optimization techniques and datasets for broader applicability