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The impact of Transforming an Independent School with Conventional Hierarchal Structure into a Professional Learning Community (PLC) on the Overall School Improvement Process
No full textThis research study intends to investigate the impact of shifting school improvement initiatives from being reactive, long delayed, and externally initiated into being more innovative intra-institutionally originated by getting an anonymised independent school established as a professional learning community (PLC). The investigation is focused on reconceptualising school leadership structures to become more collegial and participatory by abandoning the top-down leadership styles, empowering stakeholders’ collaboration and involvement as authentic partners, and thoroughly reviewing the school ethos to redefine its strategic direction in a more inclusive and collaborative approach to become optimally aligned to the stakeholder groups’ aspirations. This transformation is expected to enhance the cultivation of a new sustainable culture that prioritises collaborative learning, institutional efficacy, and continuous improvement. With this PLC model, the school will develop greater agility and capability of coping with the challenges induced by continuous change that is becoming more expedited than ever before. This way, school improvement initiatives become immaculately contextualised and internally initiated, and thus far more sustainably effective than current standardised school improvement models with very limited potential to achieve stakeholders’ aspirations of building a robust system that can internally, sustainably, and proactively revive school improvement dynamics and mechanisms. This investigation is primarily grounded in an anonymised independent school staff’s insights, as represented by their participation in semi-structured interviews and a self-completed questionnaire. The generated data, both qualitative and quantitative, are analysed into three themes which are aligned to three research questions in order to inform discussions and key findings, and inspire data-driven recommendations
Exploring the Impact of Technology-Enabled Pedagogies on the Effectiveness of Students’ Learning
No full textThe rapid evolution of digital technologies is fundamentally transforming educational methodologies, prompting a critical reassessment of conventional teaching approaches. This study explores technology-enabled pedagogies and their impact on student learning particularly in promoting student-centered learning. Anchored in constructivist learning theories, which emphasize active, inquiry-based learning and authentic assessments, the study examines the impact of immersive technologies like virtual and augmented reality, as well as data-driven instruction on enhancing educational outcomes. Based on selected higher education institutions, data was collected from a total of 120 teacher educators. The findings of the study underlined the efficacy of personalized, technology-driven educational strategies in increasing accessibility, educational equity and student engagement, thereby equipping students more effectively for effective learning in the digital age. The paper proposes that future research should focus on the long-term impacts of technology-integrated education and the development of scalable educational models on student learning. Although acknowledging limitations due to varied technological infrastructure across institutions, the study concludes that embracing technology-enabled pedagogies is essential for modern education, offering significant advantages in personalizing learning experiences and preparing students with crucial 21st-century skills
Metaverse Integration in Education: Empowering Diverse Learning Experiences
No full textThis dissertation investigates the integration of the Metaverse into educational settings to enhance diverse learning experiences. The research aims to explore the transformative potential of leveraging immersive technologies in education (Zhang et al., 2022), particularly focusing on fostering inclusivity and accessibility. The methodology involves a comprehensive literature review to understand the technological components of the Metaverse, its evolution (Chen et al., 2023), benefits, challenges (Lin et al., 2022), and ethical considerations (Kaddoura et al., 2023). Qualitative and quantitative data collection methods are employed to gather insights from educators, students, and stakeholders regarding their attitudes, perceptions, and experiences with Metaverse integration. The major findings reveal that Metaverse integration holds promise for creating inclusive and adaptive learning environments, especially for students with special educational needs. However, challenges such as technological proficiency and ethical considerations need to be addressed (Lin et al., 2022). The implications of the findings underscore the importance of informed strategies and resources to support educators and students in leveraging the full potential of the Metaverse (Zhang et al., 2022). The dissertation concludes with recommendations for future research and the imperative for continued commitment to inclusivity, ethical considerations (Kaddoura et al., 2023), and pedagogical innovation in Metaverse integration within education
Sentiment Analysis of Dialectal Speech: Unveiling Emotions through Deep Learning Models
No full textDialect Speech Sentiment Analysis is an evolutional field where machine learning algorithms are utilized to detect emotions in spoken language. However, Arabic, particularly Egyptian Arabic, remains underrepresented, lacking a dedicated speech sentiment database. This thesis introduces a novel dataset specifically created for sentiment and emotion detection in the Egyptian Arabic dialect, generated from publicly available YouTube videos and annotated across seven emotional categories: anger, happiness, sadness, disgust, fear, romantic, and neutrality. The proposed solution involves leveraging a multi-stage machine learning pipeline that first extracts spectral features such as MFCC and mel spectrograms from acoustic speech waves using Fourier transformation. These features are then classified using a range of Deep Learning Models, including convolutional neural networks (CNN), bidirectional long-short-term memory (BI-LSTM), gated recurrent units (GRU), and Artificial Neural Networks (ANNs). A key contribution of this work is the development and evaluation of hybrid Deep Learning Models that combine CNN-BI-LSTM, CNN-GRU, GRU-CNN, GRU-BI-LSTM, and GRU-ANN architectures. The results demonstrate the superiority of the hybrid CNN-BI-LSTM model, achieving an accuracy of 93%, significantly outperforming individual deep-learning models such as CNN (87%) and BI-LSTM (83%). Additionally, the GRU-CNN hybrid model attained a notable accuracy of 90%. These findings establish the robustness and effectiveness of hybrid architectures in enhancing emotion recognition accuracy in Arabic speech data, presenting a novel approach for Arabic dialect sentiment analysis
Examining the Use of Generative Artificial Intelligence in Higher Education and Its Impact on Social Sustainability: An Integrated Model of UTAUT2 and T-EESST
No full textIn this research, the author assesses and evaluates the degree to which the higher education (HE) sector has embraced generative artificial intelligence (GenAI), primarily focusing on the adoption and application of GenAI and its implications for social sustainability. The aim of this research entails the proposing and advancing of a model that enhances the adoption of GenAI in an educational setting that is based on Technology-Environmental, Economic, and Social Sustainability Theory (T-EESST) and the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model. This research employs structural equation modelling (SEM), in conjunction with artificial neural networks (ANNs), to examine the main constructs driving the intention to use GenAI and the nature of its impact on social sustainability (SS). The research’s results indicate four factors that significantly impact the adoption of GenAI in HE—habit (HB), hedonic motivation (HM), performance expectancy (PE), and perceived trust (PT)—essentially highlighting intrinsic motivators. However, effort expectancy (EE), social influence (SI), facilitating conditions (FC), price value (PV), and perceived risk (PR) were not statistically significant. Furthermore, the study demonstrates that adopting GenAI has a significant impact on social sustainability (SS) in education by promoting equitable, inclusive, and lifelong learning, as well as enhancing societal well-being. These outcomes provide essential awareness for policymakers and educational institutions, establishing a basis for developing socially sustainable learning environments that leverage the revolutionary capabilities of GenAI.
Keywords: Generative AI, higher education, technology acceptance, UTAUT2, T-EESST, SEM, ANN, habit, hedonic motivation, perceived trust, social sustainability, perceived risk, performance expectancy, effort expectancy
Neutrosophic bipolar fuzzy decision-based approach for developing sustainable circular business model innovation tools
No full textThe circular economy (CE) has been identified as a possible catalyst for sustainable development by business,
academics, and policymakers. To aid company developers in creating and improving business models that
incorporate circularity, a variety of tools for circular business model innovation (CBMI) have been proposed.
Nevertheless, the existing tools failed to consider sustainability or CE in their advancements. Currently, there is
no research that has presented a complete dataset including all potential tools that may be created based on the
CE’ sustainability performance attributes. Moreover, there has been a dearth of research conducted to assess and
model these tools in order to determine the most efficient ones, which has resulted in a research gap. This paper
constructs a decision matrix of CBMI tools by intersecting 100 CBMI tools with 10 CE’ sustainability performance
attributes. The modeling of CBMI tools falls under Multiple Attribute Decision Making (MADM) due to the
presence of many attributes, varying importance levels of these attributes, and the and variation in data. Thus,
the fuzzy weighted with zero inconsistency (FWZIC) method is reformulated under neutrosophic bipolar fuzzy
sets (NBFS) to determine the weight of CE’s sustainability performance attributes. The matrix that has been
constructed and the resulting weight values are fed into the CODAS method in order to model CBMI tools and
identify the most sustainable tool. The results indicate that the NBFS-FWZIC method gave a weight value of
0.1031 to A7, which is the greatest weight value. On the other hand, A3 had the lowest weight value of 0.0944.
The CODAS method modeled the 100 CBMI tools, with Tool39 being identified as the most sustainable tool and
Tool26 as the least sustainable tool. The robustness and durability of the proposed method are evaluated using a
sensitivity analysis, Spearman’s rank correlation test, and comparison analysis
Estimating the elastic constants of orthotropic composites using guided waves and an inverse problem of property estimation
No full textThe present research focusses on estimating the elastic constants of orthotropic laminates using ultrasonic guided
waves (GWs) excited through Lead Zirconate Titanate (PZT) sensors and sensed using one-dimensional laser
vibrometer. The elastic constants of a material are crucial for understanding its mechanical behaviour and are
typically determined through experimental testing. However, this process can be time-consuming and expensive.
We formulate this problem as an inverse problem of property estimation. Thus, in this work, the simulation
models with PZT transducers have been employed for generating time series (TS) GWs for the orthotropic ma
terial. Then, an inverse machine learning model is trained using a TS dataset pertaining to different elastic
constants generated using the simulations. The inverse model consists of deep neural networks and designing a
loss function for the specific application. Limited number of unique sets of simulations were conducted. Out of
available simulation data, 30% of the sets were used for validation. To further test the model, a blind experi
mental test was conducted, and the corresponding elastic constants were estimated with a mean absolute per
centage error (MAPE) of 12.89% and standard deviation of 5.47%. The results demonstrate that formulation of
property estimation as inverse problem is capable of accurately predicting the elastic constants of a material, by
using a model solely trained on simulation and a very scare amount of data. This approach has the potential to
significantly reduce the computational time for predicting the elastic constants, and thereby could have wide ranging applications in materials science and engineering
The Impact of Leadership Styles on Improving Employee Agility: The Mediating Role of Employee Incentives
No full textThe interrelationship between the leadership style and employee agility has been a frequently discussed topic by industry and academia. Different leadership styles have different qualities that impact the employees differently. The core purpose of the present study was to explore the impact of three key leadership styles, namely, transformational, transactional, and laissez-faire, on employee agility through the mediating role of employee incentives and the moderating role of the years of experience. This study was underpinned by contingency theory, quantitative in nature, and based on primary data collected online from 384 respondents in the United Arab Emirates. Data analyses of this study were conducted through the structural equation modelling technique of SmartPLS. Analyses of the direct relationships suggest that transformational leadership style has a significant impact on employee agility. While analysis of the indirect relationships confirms that employee incentives is a significant mediator for the relationship of transactional and laissez-faire leadership with employee agility however, years of experience does not have a moderating role for any of the above relationships. The outcome of this study has identified many key elements that should be focused on by the organisations of various domains to enhance the agility of their employees and achieve the organisational objectives in the United Arab Emirates
Comparative analysis of environmental, social and governance (ESG) ratings: do sectors and regions differ?
No full textSustainable and holistic investment philosophy such as environmental, social and
governance (ESG) concepts have now emerged as the subtle, comprehensive and
concrete response to the unprecedented surge in environmental, social and financial
market sustainable development problems. The main aim of this paper is to perform
an in-depth study on ESG ratings world-wide and to granularly analyse how and
why they differ within industries and regions as reported by Sustainalytics on 13,589
companies as of December 2022. Perceiving ESG ratings from dual dimensions, we
introduce the ‘push–pull effect’ where we identify the rationale pushing corporates
for providing their ESG engagements to ESG rating providers and the justification
for stakeholders pulling information from these rating providers. Correspondence
analysis, nonparametric independent sample Kruskal–Wallis test and Mann–Whit
ney test are performed as tools of inference. Results reveal that Asia and America
are regions demonstrating high ESG risks with European corporates exhibiting
low ESG risks. In terms of industry, transportation infrastructure and media, both
categorized under low ESG risk, portray a statistically significant difference from
other industries. Finally, the sector wise reports clearly evince an overall statistically
significant difference between financial and non-financial sector in all regions, the
former presenting high risk ESG scores in Asia and North America. Policy implica
tions are set as ESG is a concept which has stepped out of the “awareness creation”
stage to an implementation state, imploring policy makers to embark on stringent
measures of ensuring ESG compliance to reap stakeholder confidence and ensure
sustainable development
Developing a Framework for Designing and Assessing Professional Development in Educational Technology : A Study in a Private School in the UAE
No full textThe integration of technology in education is essential for enhancing learning experiences and preparing students for a technology-driven world. However, many teachers lack the necessary skills and confidence to effectively incorporate technology into their classroom practices. This dissertation investigates the impact of technology-related professional development (PD) on teachers' self-reported technology competencies and their application of technology in classroom practices, addressing the critical need for effective PD programs that can bridge this gap. The study involved the development and administration of a self-reported ICT competencies survey, the observation of classroom practices using a custom-developed observation tool, and the implementation of a comprehensive PD program followed by a subsequent reassessment using the same instruments. The findings demonstrate significant improvements in teachers' self-reported competencies and classroom practices following the PD sessions. The validated self-rating technology skills survey and classroom observation tool proved to be reliable and effective measures for assessing teachers' ICT competencies and the integration of technology in teaching. The study underscores the necessity of well-designed PD programs that incorporate active learning, collaboration, and sustained support to enhance teachers' technology integration skills