Ahmad Zabidi Abdul Razak

Students with physical disabilities often encounter significant mobility and accessibility challenges in hybrid learning environments, particularly in dynamic classroom settings where conventional wheelchairs lack adaptability and compatibility with hybrid classroom layouts and digital learning activities. This research developed and evaluated a smart wheelchair equipped with sensor-based automated navigation to enhance mobility, independence, and accessibility in hybrid classrooms. The research employed a Research and Development approach using the ADDIE model, integrating prototype development, functional testing, mechanical simulation, and expert validation. The system incorporated a joystick and smartphone-based control, ultrasonic and line sensors for obstacle detection, GPS-assisted positioning, and an auto-parking feature. Mechanical simulation results indicated structural safety with a high safety factor (7.47) and minimal deformation (0.0429 mm). Performance testing demonstrated stable navigation, responsive control with an average latency of 120 ms, and successful auto-parking in 90% of trials. Expert validation involving five specialists showed that the system achieved "highly feasible" ratings across efficiency, durability, aesthetics, precision, and safety indicators. The findings indicate that the proposed smart wheelchair effectively improves independent mobility and supports accessibility in hybrid learning environments. However, further refinement is required to enhance precision and safety performance for broader implementation.

Purpose: This study aims to test large language models as a tool for multi-theory simulation that could potentially augment the doctoral student’s (DS) research efficiency. Design/methodology/approach: The study proceeded in three phases, commencing with theory simulations where identical prompts were applied to GPT-5, DeepSeek-V3.2 and Mistral Small 3.1. Phase Two involved a quantitative evaluation by two independent raters using the percentage of maximum possible score and the average deviation index. Finally, Phase Three entailed a thematic analysis of the simulated outputs. Findings: Large language models can augment research efficiency by simulating varied theoretical perspectives and assisting the student in assessing the suitability of theory before commencing a real-world study. Quantitative analysis showed that GPT-5 achieved the highest percentage maximum possible score, followed by DeepSeek-V3.2, and finally Mistral Small 3.1. Additionally, the average deviation index was 0.53, which is less than the critical value of 0.67. Therefore, the inter-rater reliability was established. The qualitative analysis revealed that two of the three models generated the same output, while the third generated a slightly different output. Originality/value: This research integrates large language model simulation into educational research methodology before conducting empirical studies. This method could potentially augment doctoral research efficiency and present the DS with novel perspectives on their research areas.

Combat sports are defined by strict weight classification systems, rendering weight management a pivotal factor influencing athletes’ competitive performance and long-term health outcomes. College student-athletes in combat sports confront unique predicaments stemming from their dual roles as students and athletes, navigating the competing demands of academic workloads, intensive athletic training, and ongoing physical development. While environmental support systems and socio-psychological factors are widely acknowledged as key determinants of health-related behaviors, their integrated impacts on weight management within this specific population remain underexplored. This study aimed to evaluate the current state of environmental support systems for weight management among college combat sports student-athletes and unpack the socio-psychological mechanisms through which these systems shape weight management behaviors and effectiveness. A mixed-methods research design was employed, combining quantitative questionnaires and qualitative in-depth interviews. Quantitative data were collected from 278 college combat sports student-athletes across 10 higher education institutions and analyzed using SPSS and AMOS for descriptive statistics, correlation analysis, and structural equation modeling. Qualitative data were gathered through in-depth interviews with 18 student-athletes and 6 coaches, processed via thematic analysis with Nvivo. The findings identified three core dimensions of environmental support systems (interpersonal, resource, and institutional support), with an overall moderate level of support (M=3.21, SD=0.58); interpersonal support was the strongest dimension (M=3.54, SD=0.62), while resource support was the weakest (M=2.87, SD=0.71), particularly in terms of access to specialized nutrition counseling and weight management-specific facilities. Correlation analysis confirmed positive relationships between environmental support systems, socio-psychological factors, and weight management effectiveness (r=0.38–0.57, p<0.001). Structural equation modeling revealed that environmental support systems had a direct positive effect on weight management effectiveness (β=0.28, p<0.001) and that self-efficacy (indirect effect=0.17, 95% CI=[0.12, 0.23]) and group norms (indirect effect=0.10, 95% CI=[0.05, 0.15]) served as significant mediators, while behavioral motivation did not exhibit a significant mediating role. Qualitative results complemented the quantitative findings, highlighting an uneven distribution of environmental support (strong coach and peer support alongside limited resource access), the role of self-efficacy as a key enabler of weight management behaviors, and the influence of team norms in either discouraging or normalizing risky rapid weight loss practices. This study enriches interdisciplinary research at the intersection of environmental psychology, sports psychology, and public health, while providing actionable implications for colleges and coaches to optimize weight management support strategies for student-athletes.

This study integrates bibliometric analysis and a systematic literature review (SLR) to examine the role of digital literacy in enhancing the effectiveness of online learning and its influence on educational outcomes. Although research on digital literacy in online learning has increased, many studies remain limited to isolated analyses without presenting global publication trends or addressing contextual challenges across different regions. Through bibliometric analysis of 266 Scopus-indexed documents published between 2015 and 2025, this study revealed a significant upward trend in research focusing on the integration of digital skills and technological tools to support quality education. Furthermore, the SLR conducted using the PRISMA framework showed that digital literacy plays a consistent role in improving student engagement, motivation, and academic performance in online learning settings. Despite these benefits, key challenges persist, including unequal access to technology, insufficient digital competence among educators, and limited curriculum integration. The novelty of this study lies in combining bibliometric analysis with SLR to provide a more comprehensive and holistic mapping of research developments. This study has contributed to the understanding of the importance of digital literacy as a foundation for developing inclusive, adaptive, and sustainable online learning ecosystems and supports evidence-based policy formulation.

This study provides a systematic review of innovation in educational leadership, a pivotal domain in the dynamic field of educational practices. Despite increasing interest, a comprehensive understanding of how innovation shapes educational leadership and its outcomes remains limited. To address this gap, this review examines studies published up to 2024, retrieved from reputable databases such as Scopus and Web of Science. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework, 33 primary studies were selected for in-depth analysis. The findings are categorized into three key themes: i) digital and technological transformation in education, ii) leadership styles and professional development, and iii) innovative strategies and change management. The review highlights significant advancements in these areas, yet underscores the need for further exploration of their long-term impacts, particularly in diverse educational settings. This work contributes by synthesizing current trends and practices in educational leadership innovation, identifying existing challenges, and proposing pathways for future research. These insights aim to support the development of effective and scalable leadership innovations, ensuring adaptability and impact across various educational environments. This review ultimately serves as a foundation for advancing educational leadership to meet the evolving demands of the 21st-century education landscape.

HRMARS is well established journal publisher based in Pakistan. It is publishing prominent journals in social sciences since 2011.

Personality traits are pivotal in shaping human behavior and outcomes across diverse contexts, including education, health, work performance, and interpersonal relationships. As interest in this area continues to grow, a substantial body of literature has emerged. However, a structured analysis that captures global publication patterns, research trajectories, and scholarly collaborations remains limited. Thus, this study aims to conduct a comprehensive bibliometric analysis of personality traits research published between 2015 and 2025, to identify publication trends, influential authors, geographical distribution, thematic evolution, and international collaboration patterns. This study addresses the gap in understanding how the field has developed over the past decade by analysing 915 documents retrieved from the Scopus database. Data cleaning and normalisation were performed using OpenRefine, while Scopus Analyzer and VOSviewer software were employed to extract and visualise bibliometric indicators. The analysis revealed a consistent increase in publication output, particularly from 2020 onward. Notably, the United States, United Kingdom, and China were the most productive countries, and network visualisations demonstrated strong co-authorship ties across institutions and continents. Citation analysis identified the most influential authors and sources. At the same time, keyword co-occurrence mapping illustrated a thematic shift from trait measurement to applied domains such as well-being, academic achievement, and organisational behaviour. The findings of this study provide a comprehensive overview of the intellectual landscape and evolution of personality traits research, offering valuable insights for scholars, practitioners, and policymakers. By uncovering key trends and research gaps, this bibliometric review serves as a strategic guide for future investigations and fosters more informed, interdisciplinary, and globally collaborative efforts in the field.

Much research has been done on continuous professional development (CPD) madrasa (Islamic school) teachers, but incorporating motivation of engagement in future professions into them needs to be addressed. This study aims to determine CPD madrasa teachers based on the motivation to engage future professionals in teaching. This research used a phenomenological design involving 16 madrasa Aliyah teachers as informants. Data was collected by interviews with madrasa teachers and analyzed using a systematic design. This research shows that CPD madrasa teachers are related to organizing teaching and learning activities to improve teachers' abilities and competencies in carrying out professional duties and performing as educators. CPD should include skills using digital technology, ethics, and professional values, applying sharia principles, makarim sharia, and integrity in educating students. Madrasa teachers’ view of motivation for engagement in a future profession in CPD refers to the drive that teachers professionals have to continue to be committed to their profession and develop with the changes and demands of the times. The motivation for madrasa teachers’ future professional involvement and sustainable professional development is mutually reinforcing. Motivation becomes a driving force for madrasa teachers to engage in CPD, while CPD renews and strengthens madrasa teachers’ motivation by providing the skills and knowledge necessary to adapt to future challenges. In this way, motivation and CPD together support better quality education, increase job satisfaction for madrasa teachers, and ensure that teachers remain relevant and competent in facing developments in the world of education.

High-effort teachers tend to experience a higher level of exhaustion and emotional instability. Recent research suggests talent development training is needed to cultivate teachers’ personalities. This research aims to investigate how talent development is correlated with teachers' conscientiousness and emotional stability among teachers in MICSS and examine whether conscientiousness among MICSS teachers in Malaysia serves as a mediator for talent development and is associated with emotional stability. The researcher implemented quantitative research methodologies to collect numerical data from 357 Malaysian teachers serving at MICSS. It was found that talent development, conscientiousness, and emotional stability showed a weak-to-moderate correlation. Teachers' conscientiousness plays a full mediator role in the correlation between talent development and emotional stability among MICSS teachers in Malaysia. To the best of our knowledge, this is the initial endeavour to examine the relationship between the emotional stability, conscientiousness, and talent development of MICSS teachers in Malaysia.

The use of Artificial Intelligence (AI) in educational evaluation optimizes learning outcomes. This research seeks to address the advantages and difficulties of implementing AI within academic evaluation frameworks with particular emphasis on the algorithmic bias problem and its implications for fairness in education. The absence of a thorough grasp of algorithmic bias, particularly how it can be utilized as a weapon against equitable education, reveals an important gap. We conduct a Systematic Literature Review (SLR) and bibliometric analysis on 121 articles sourced from Scopus published between 2021 to 2025 to trace the trends and examine the impacts and biases of AI on grading systems. The data demonstrates a significant increase in publications beginning 2018, concentrating on topics such as educational applications of AI, automated grading systems, and machine learning. The findings further indicate that though AI improves efficiency and consistency of the evaluations, it heightens the chances of biased outcomes because of non-diverse training data, prejudiced developers, and socio-cultural frameworks that could worsen the situation for already marginalized learners. In summary, this study highlights the critical gaps in bias mitigation strategies arising from the lack of ethical design frameworks, antecedent-free algorithms, and educator prep courses aimed at combating bias. These outcomes serve as benchmarks for the creation of more reliable and comprehensive AI systems for assessments and shift subsequent investigations to focus validation on different cultures and the incorporation of just AI design paradigms