Volume 14 | Issue 7 |

 Abstract

Background: The classroom management and teacher self-efficacy are important factors in determining the quality of instruction and student engagement in the classroom, but the relationship between the two has not been well studied at the higher secondary level in Ranipet district. Purpose: This study was conducted to find out the level of classroom management behavior and self-efficacy of higher secondary teachers in Ranipet district and to find out the differences in classroom management behavior and self-efficacy among the teachers based on the different demographic variables and to find out the relationship between classroom management behavior and self-efficacy among the higher secondary teachers in Ranipet district. Methodology: A descriptive survey design was used with 200 higher secondary teachers (100 male and 100 female) selected randomly from government, aided and private schools of Ranipet District; the instruments used were 31 item Teacher Classroom Management Behaviour Scale and 10 item Self Efficacy Scale (both adapted and validated with satisfactory reported test-retest reliability). Results: Most teachers exhibited moderate levels of classroom management behavior (49.5%) and moderate self-efficacy (48.5%); with the exception of the few comparisons, no significant differences were observed between the classroom management behavior and self-efficacy, across gender, medium of instruction, locality or qualifications; a small but positive correlation was obtained between classroom management behavior and self-efficacy (r ? 0.057).

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 Keywords

Classroom management, teacher self-efficacy, higher secondary teachers, Ranipet District, teaching behavior, teacher effectiveness.

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Creative Commons Attribution 4.0 and The Open Definition

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Ayurveda-based wellness tourism has positioned Kerala as a leading global destination for holistic health and well-being. This study examines tourist perceptions and satisfaction toward wellness tourism in Kerala, with specific emphasis on identifying the key determinants influencing wellness tourists' experiences. Using a descriptive and analytical research design, primary data were collected from 100 domestic and international wellness tourists through a structured questionnaire. Tourist satisfaction was assessed across multiple dimensions, including service quality, therapist competence, treatment authenticity, physical environment and ambience, and perceived wellness outcomes. Statistical analysis using SPSS reveals that overall tourist satisfaction with wellness tourism in Kerala is high and largely consistent across age groups and type of tourists. Gender emerges as a partially significant factor, influencing perceptions of service quality, treatment authenticity, price fairness, and accessibility. Authentic Ayurvedic treatments, professional competence of therapists, hygienic and serene environments, and positive wellness outcomes are identified as the most influential drivers of satisfaction. The findings indicate that demographic factors exert a limited impact on wellness tourism satisfaction, reinforcing the robustness of Kerala's wellness offerings. The study underscores the need for transparent pricing, improved accessibility, continuous quality assurance, and stronger regulation to preserve authenticity and enhance tourist satisfaction. The results provide valuable insights for policymakers and wellness tourism stakeholders seeking to sustain Kerala's competitiveness and long-term leadership in Ayurveda-based wellness tourism

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 Keywords

Ayurveda-based wellness tourism, Tourist satisfaction, Perceptions, Service quality, Wellness tourism.

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Background: First-year MBBS students are introduced to foundational biomedical sciences through predominantly teacher-centered, passive instructional methods. With the implementation of Competency-Based Medical Education (CBME) in India, there is an increasing need to understand whether current teaching practices align with student learning preferences and expectations. Objectives: To evaluate first-year MBBS students' satisfaction with existing teaching-learning methods and to identify their preferences for transitioning toward more active, interactive, and technology-integrated pedagogical approaches. Methods: A cross-sectional, questionnaire-based survey was conducted among first-year MBBS students in June 2026 using a structured Google Form. Voluntary informed consent was obtained prior to participation. Complete anonymity was maintained throughout. Data analyzed using descriptive statistics and thematic analysis of qualitative responses. No external funding was received, and no conflict of interest exists. Many students who consented"No" in form were excluded from the study. Results: Of 100 responses collected, 75 valid consented responses were analyzed. Audio-visual aids (Mean = 3.84) and Practical/Dissection sessions (Mean = 3.78) received the highest satisfaction scores among current methods. Students expressed strong agreement for clinical correlation (Mean = 4.12), recorded lectures (Mean = 4.08), and integrated teaching (Mean = 4.01) as desired future changes. Case-Based Learning (69.3%) and Problem-Based Learning (62.7%) were the most preferred future methods. Qualitatively, students expressed dissatisfaction with passive slide-reading lectures and desired story-based, AI-assisted, and clinically contextualized learning. Conclusion: First-year MBBS students demonstrate a clear shift in learning preferences - from passive reception of information toward active, clinically relevant, and technology-enhanced engagement. Institutions must urgently incorporate these insights into curriculum planning to improve learning outcomes and student satisfaction.

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Active learning, Medical education, First-year MBBS, Teaching-learning methods, Clinical correlation, Case-based learning, CBME, Student satisfaction, Pre-clinical education, Digital learning

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The Arab world, a vast geographical expanse often characterized by its arid deserts, holds a subsurface wealth of archaeological and historical treasures that span the entirety of human civilization. This review article synthesizes current archaeological understanding and recent discoveries to trace the arc of human history across this vital region. It begins with the monumental legacy of the Nile, exploring the engineering evolution of Egypt's pyramid fields at Dahshur and Saqqara, before examining the less-traveled Kushite pyramids of Sudan, which challenge the narrative of Egyptian exclusivity in monumental tomb construction. The article then shifts focus to the Arabian Peninsula, where recent excavations are radically reshaping historical narratives. Sites like the AlUla oasis in Saudi Arabia, with its Nabataean capital of Hegra and the open-air library of Jabal Ikmah, reveal the peninsula as a vibrant crossroads of ancient trade and culture rather than a peripheral hinterland. Complementary discoveries in Oman and the UAE provide evidence of prehistoric coastal settlements and Iron Age industrial hubs, further illustrating the region's deep and complex history. Finally, the article reviews contemporary preservation efforts, including new UNESCO designations and the launch of the Arab Architectural and Urban Heritage Register by ALECSO. By connecting the Pyramids to the desert, this review demonstrates that the treasures of the Arab world are not static relics but dynamic sites of ongoing discovery that are essential to understanding our shared global heritage.

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Arab world archaeology; cultural heritage; pyramids; Nabataean; UNESCO

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Edge Artificial Intelligence (Edge AI) and the Internet of Things (IoT) are revolutionizing the smart food manufacturing landscape, offering enhanced adaptive process control, real-time quality monitoring, and more sustainable production processes. Food manufacturers must make quick and accurate decisions to ensure product consistency, safety, and operational efficiency in their food manufacturing processes. Smart use of IoT-based infrastructure, with its sensors and edge-based analytics, can process data near its source, thereby reducing latency and enabling instant responses to process changes. This paper reviews the system architecture for smart food manufacturing enabled by Edge AI, focusing on physical sensing, local data collection, Edge AI intelligence, and cloud connectivity, and highlighting communication protocols and deployment considerations. It also focuses on the use of artificial intelligence (AI) for developing adaptive control strategies, including predictive modeling, anomaly detection, and closed-loop feedback for process optimization and quality assurance. Furthermore, the article discusses the benefits of minimizing waste, conserving energy, and predicting maintenance needs. Lastly, the following key challenges are addressed: security, interoperability, scalability, and integration complexity, as well as future opportunities such as 5G, blockchain, and digital twins for smart and resilient food systems.

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AI for the Edge, Internet of Things (IoT), smart food manufacturing, adaptive process control, real-time quality monitoring, sustainable production, food safety.

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The rapid adoption of artificial intelligence (AI) tools has transformed the way students and working professionals perform academic and workplace tasks. However, empirical evidence on the relationship between screen time, AI engagement, and perceived performance remains limited, particularly in exploratory settings. This study investigates the influence of screen time and AI tool usage on perceived academic and professional performance. Data were collected from 45 respondents, comprising college students and working professionals, through a structured online questionnaire. The proposed model examined the relationships among screen time, device usage breadth, AI engagement, AI usage purposes, perceived performance improvement, and screen time growth. The findings reveal that AI has become an integral part of respondents' daily routines, with ChatGPT emerging as the most frequently used AI application. Working professionals reported significantly higher AI usage than full-time students. Most respondents perceived AI tools as enhancing their productivity and academic or professional performance. Furthermore, respondents reported a noticeable increase in overall screen time compared with the previous year, reflecting the growing integration of AI into everyday digital activities. Although several hypothesized relationships were not statistically significant because of the limited sample size, the study offers valuable exploratory evidence on emerging AI usage patterns. The findings contribute to the growing literature on AI adoption by providing preliminary empirical insights while emphasizing the need for larger, more representative samples to validate the proposed relationships.

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Academic Performance, Artificial Intelligence, ChatGPT, Digital Behaviour, Professional Performance, Screen Time and Technology Adoption

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Cricket, especially in shorter formats like T20 and One-Day matches, requires batsmen to perform repeated high-speed sprints while running between the wickets. Sprint performance plays a crucial role in improving scoring rate and reducing the risk of run-outs. Core muscles are believed to contribute significantly to athletic performance by providing stability and efficient transfer of force between the upper and lower body. However, there is limited research exploring how core endurance and core strength relate to sprint speed in amateur cricket players. The present study aims to investigate the correlation between core endurance, core strength, and sprint speed in amateur cricket batsmen. A total of 80 male participants aged between 18-25 years will be selected using convenient sampling from cricket clubs. Core endurance will be assessed using the McGill torso endurance tests, core strength will be measured using the plank test, and sprint performance will be evaluated using the Repeated Sprint Ability (RSA) test. Data will be analyzed using statistical software to determine the relationship between these variables. It is expected that core strength will show a stronger correlation with sprint performance compared to core endurance. The findings of this study may help in designing better training programs focusing on core strengthening to enhance sprint ability and overall performance in amateur cricketers.

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Amateur cricket batsmen, Core strength, Core endurance, Sprint speed, Repeated Sprint Ability (RSA), McGill torso endurance test, Plank test, Athletic performance, Running between the wickets, Sprint performance

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Purpose The Indian IT sector is a global leader in digital transformation, yet empirical evidence on how an integrated HR tech ecosystem affects employee outcomes remains limited. This study examines the impact of Cloud-based Human Resource Information Systems (HRIS), Robotic Process Automation (RPA), and AI-powered HR Chatbots on HR service delivery, employee experience, engagement, and retention in Indian IT firms. Design/Methodology/Approach Grounded in the UTAUT2 and AMO frameworks, the study uses a cross-sectional survey of 428 HR professionals and IT employees across 9 leading Indian IT companies. Data were analyzed using Confirmatory Factor Analysis and Structural Equation Modeling in AMOS 26. Findings Cloud HRIS and RPA significantly enhance HR service delivery efficiency. AI Chatbots and HR service efficiency positively influence employee experience and engagement. Organizational change management and employee digital literacy significantly moderate these relationships. Employee experience mediates the path from HR tech to engagement. Originality/Value This is among the first studies in India to test the HR tech ecosystem as an integrated construct and link it to both organizational and employee-level outcomes. The findings provide actionable insights for CHROs on designing tech that delivers both efficiency and human value. Practical Implications HR leaders must invest not only in platforms but also in change management and digital literacy. Technology should be designed for inclusion, with multilingual chatbots and self-service for distributed teams.

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HR Tech Ecosystem, Cloud HRIS, RPA, HR Chatbot, Employee Experience, Employee Engagement, Indian IT Sector, UTAUT2, AMO

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Creative Commons Attribution 4.0 and The Open Definition

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Social media has quietly but substantially changed how people discover and decide to buy things. What was once driven by television commercials and newspaper ads is now increasingly shaped by a person with a ring light talking directly to their audience about a product they may or may not genuinely use. This shift from broadcast advertising to influencer-driven recommendation is the starting point of this research. This study explores how social media influencers shape consumer purchase intention, with particular interest in the human side of this process: the trust consumers place in influencers, the emotional connections they form, and the mental calculations they make before buying something. A qualitative approach was used, drawing on semi-structured interviews with six purposively selected consumers. Three theoretical frameworks guide the analysis: the Elaboration Likelihood Model, Parasocial Interaction Theory, and the Source Credibility Model. Five themes emerged from the data: how and why consumers follow influencers, what builds or breaks trust in a recommendation, how authenticity and emotional connection factor into purchase decisions, how the decision-making process actually unfolds across multiple stages, and how platform and content format shape the whole process. The findings suggest that effective influencer marketing is less about reach and more about trust built over time through credible, experience-based content.

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influencer marketing, purchase intention, social media, consumer behavior, qualitative research

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Creative Commons Attribution 4.0 and The Open Definition