Publication Search

This study aims to conduct a systematic literature review and bibliometric analysis on Transformational Leadership (TL), based on empirical research. Despite extensive studies on TL in recent decades, comprehensive research remains limited. The methodology used is a Systematic Literature Review (SLR) of articles containing “Transformational Leadership” in the title, abstract, or keywords, sourced from the Scopus database, which yielded 1,297 publications from 1977 to 2025. The data were analyzed using VOSviewer software. The findings suggest that TL is an innovative model in vocational education, bridging industrial systems with academic environments for authentic, practice-based learning. However, challenges include limited resources, insufficient instructor training, and inadequate institutional support. Recent innovations, such as advanced ICT, holographic technology for real-time interaction, and digital twins for process optimization, have enhanced the model's effectiveness. The study also discusses the conceptual framework of TL. This research uses the Scopus database, and future studies could broaden its scope by incorporating other databases like Web of Science. Practically, the study’s findings suggest integrating modern pedagogical strategies, including gamification, simulation, and project-based learning, to enhance student engagement and understanding of manufacturing processes. Socially, it encourages educational institutions to invest in infrastructure, strengthen industry partnerships, and provide continuous instructor training. This study highlights the growing global research on TL and calls for deeper exploration of the topic in existing literature.

This study was motivated by the fact that teachers' learning strategies are very important for improving student motivation, understanding, and learning effectiveness. The purpose of this study was to identify the learning approaches used by teachers, the challenges faced, and how these approaches were applied to students in the 10th grade Geography class at SMAN 6 Malang. This study uses a descriptive qualitative methodology that collects data through interviews, observations, and documentation. Furthermore, the data were analyzed through the steps of reduction, presentation, and conclusion drawing. The research information consisted of geography teachers, 10th grade students, and other related parties. The results showed that expository and inquiry strategies were the most effective learning strategies used by teachers to help students learn. Unresponsive students, negative peer influence, and the habit of sleeping in class are some of the obstacles in the learning process. Students' ability to systematically and coherently organize and convey conclusions demonstrates the impact of applying learning strategies. According to this study, teachers must continue to improve their skills through learning training. Students are also expected to be more focused and active during the teaching and learning process.

The flipped classroom model emerges as an innovative pedagogical approach that fosters active student participation and cultivates personal responsibility for learning, particularly within the context of distance education. This method presents a valuable alternative for delivering practical-oriented courses such as Applied Ethnomusicology. In practice, the instructor’s role shifts from a traditional knowledge transmitter to a learning facilitator whose responsibilities include guiding, inspiring, and supporting students in achieving optimal learning outcomes. This study aims to analyze the effectiveness of implementing the flipped classroom model in the online learning of the rapa’i musical instrument at the Department of Performing Arts, Syiah Kuala University. Additionally, it seeks to describe the subjective perceptions and responses of both students and lecturers toward this instructional approach. Online learning was conducted using Zoom Meeting as the primary platform for synchronous interaction, supported by WhatsApp for content distribution and communication. A formative research methodology was employed, involving 20 students enrolled in the Applied Ethnomusicology course. Qualitative data were collected through observations of each flipped classroom phase and in-depth interviews with participants. Quantitative analysis was conducted to assess effectiveness via N-gain calculations. Results indicate that all stages of the flipped classroom model were successfully implemented, and the online learning process proceeded adequately despite challenges such as limited internet access and the scarcity of personal rapa’i instruments. Quantitatively, the model demonstrated moderate effectiveness, with an N-gain value of 0.3. Overall, students displayed high levels of enthusiasm and consistent engagement during each learning session, reflecting a significant increase in learning involvement.

Background: Maritime engineering has traditionally relied on reactive and preventive maintenance strategies, often leading to operational inefficiencies, unplanned downtime, and excessive costs. With the rise of smart ship technologies, predictive maintenance (PdM) has emerged as a data-driven solution, leveraging sensor-based monitoring and real-time diagnostics to optimize ship maintenance. However, its integration into maritime education remains underexplored, particularly in training vessels used for vocational learning. Original Value: This research contributes new insights into the feasibility, effectiveness, and educational relevance of predictive maintenance in maritime vocational training. Unlike previous studies that focus on commercial ship applications, this study examines PdM within the context of training vessels at Poltekpel SULUT, bridging the gap between academic training and industry expectations. Objectives: The study seeks to answer: How does predictive maintenance improve the efficiency, cost-effectiveness, and reliability of naval auxiliary systems in training vessels? Methodology: A qualitative approach was employed, integrating sensor-based performance analysis, structured interviews, and questionnaire surveys involving cadets, instructors, and industry professionals. Data were analyzed through thematic categorization, cross-group comparisons, and narrative synthesis. Results: PdM demonstrated high effectiveness in reducing downtime (92/100), optimizing maintenance efficiency (91/100), and aligning with industry practices (89/100). However, challenges in sensor accuracy (85/100) and training integration were identified. Conclusions: The findings highlight the necessity of incorporating predictive maintenance into maritime training curricula to equip future engineers with the skills required for Industry 4.0 maintenance solutions, ensuring better operational efficiency and sustainability in the maritime sector.

This study investigated the implementation and effectiveness of Task-Based Language Teaching (TBLT) in enhancing the English reading skills of tenth-grade students at an Indonesian senior high school. The research was motivated by the persistent gap left by traditional language teaching methods, which frequently emphasize grammatical accuracy and vocabulary memorization while often neglecting the development of practical reading abilities. To address this issue, the study systematically examined the application of TBLT through three structured phases: pre-reading, main reading, and post-reading tasks. Each phase was designed to promote active learning, critical thinking, and student engagement in the reading process. The methodology involved classroom observations, task-based instructional activities, and an analysis of student exam scores as the primary indicators of progress. During the pre-reading phase, students were encouraged to activate prior knowledge and predict content, which helped build contextual understanding. In the main reading phase, tasks focused on developing essential reading strategies such as skimming for general ideas and scanning for specific information. 

This study was motivated by the low interest and skills of seventh-grade students at Darul Ulum Budi Agung Private Islamic Junior High School, which was caused by the use of uninteresting teaching methods and the lack of innovative learning media during classroom activities. This study aimed to develop animated video learning media for seventh-grade students' descriptive text material. The research methodology uses research and development with the ADDIE model, which consists of five stages: analysis, design, development, implementation, and evaluation. The population in this study was seventh-grade students at MTs Swasta Darul Ulum Budi Agung, with a sample size of 30 students from class VII-1 and 30 students from class VII-5. Data collection was conducted through observation, interviews, validation tests, questionnaires, and documentation, then analyzed using SPSS V.26 software with normality tests, homogeneity tests, and t-tests to obtain valid and accurate results. The research results indicate that the developed animated video was deemed highly suitable by media experts (90%), language experts (84%), and subject matter experts (86%). The practicality of the animated video also received highly practical results, with 94% of teachers, 58% of control class students, and 92% of experimental class students responding positively. Additionally, the effectiveness of the animated video showed an improvement in students' writing skills, with an average of 67.66 for the control class and 84.83 for the experimental class in the effective category. This study demonstrates that animated videos can serve as an alternative teaching material that supports the learning process and leads to significant improvements in learning outcomes.

This study explores the effectiveness of role play as a teaching method to enhance students' speaking skills in professional secretarial contexts, specifically in business telephone conversations, presentations, and negotiations. The research was conducted within the Diploma III Secretarial Program over one academic semester. Employing a qualitative methodology, the study involved classroom observations and student reflections gathered through feedback sessions. The objective was to assess how role play could contribute to the development of fluency, confidence, and contextual language use in English for specific professional purposes. The findings revealed that role play significantly improved students' speaking performance. Students showed greater confidence in expressing ideas, improved fluency, and a better understanding of appropriate vocabulary and expressions suited to professional environments. Furthermore, students expressed that the interactive and practical nature of role play made learning more enjoyable and less intimidating, allowing them to immerse themselves in real-life communication scenarios. These positive experiences encouraged active participation and increased motivation to learn English for professional use. The study also highlighted the value of reflective learning, as students were able to identify their own progress and areas for improvement. Through continuous engagement in simulated professional tasks, learners developed better adaptability in communication and exhibited more natural conversational flow during tasks that mirrored real workplace situations. In conclusion, the role play method proves to be a highly effective pedagogical tool for enhancing speaking skills in secretarial English. It not only helps build linguistic competence but also cultivates soft skills necessary for professional communication. Therefore, it is recommended that educators incorporate role play regularly in English for Specific Purposes (ESP) curricula to maximize its benefits in developing productive speaking abilities among students.

Digital-based evaluation is increasingly utilized across various fields of education and training. This study focuses on the development and analysis of a web-based quiz management system that facilitates interaction between users and administrators. The research involves implementing a user dashboard for quiz participation and an admin dashboard for managing questions and quiz results. The methodology includes a case study on system testing, user needs analysis, and evaluation of system effectiveness using both quantitative and qualitative approaches. The findings indicate that the system enhances evaluation efficiency and simplifies quiz data management. The study concludes that web-based systems play a crucial role in supporting evaluation and learning management.

Transformational leadership has been identified as an effective approach to mitigate these challenges by fostering supportive, innovative, and empowering school environments. This study aims to investigate the multifaceted influence of transformational leadership on teacher performance enhancement, burnout reduction, and professional development through effective management practices. Employing a library research methodology, the study synthesizes recent empirical evidence and theoretical frameworks from peer-reviewed literature published within the past five years. Data were collected and analyzed through systematic review and thematic synthesis of scholarly articles focusing on transformational leadership in educational settings. The findings demonstrate that transformational leadership substantially enhances teacher motivation and job satisfaction by providing individualized support and cultivating a shared sense of purpose. Instructional support through mentoring, guidance, and feedback strengthens teaching effectiveness, while a positive school climate mediated by transformational leadership fosters trust and collaboration. Furthermore, transformational leadership promotes continuous professional learning and pedagogical innovation by creating psychologically safe environments conducive to reflection and experimentation.

This study explores Turkey’s emerging role as a pivotal educational bridge in the context of international higher education, analyzed through the combined lenses of management and international relations. Turkey’s strategic use of education as a form of soft power, alongside its institutional efforts to internationalize higher education, underpins its ambition to foster diplomatic ties and enhance geopolitical influence. By examining cross-border educational partnerships and the mobility of international students from strategically significant regions, this research highlights both opportunities and challenges in Turkey’s educational branding. Despite notable progress, contradictions related to political interference, academic freedom, and quality assurance complicate Turkey’s global reputation as an education hub. The study employs a library research methodology, analyzing secondary sources to provide an integrative perspective on how education management intersects with foreign policy objectives. Findings suggest that Turkey’s educational diplomacy serves as a vital tool for geopolitical leverage but requires addressing internal governance issues to sustain credibility and effectiveness in the global education landscape.

Through literature analysis, this study aims to evaluate the effectiveness of the guided inquiry learning paradigm in teaching science at the primary school level. The methodology used in this research is qualitative, characterized by a literature review that analyzes various publications related to the implementation of the guided inquiry model. The results of the analysis show that the inquiry learning approach is effective for increasing student learning achievement, improving science processes, and increasing student learning motivation in science subjects. This model facilitates students to be actively involved in the learning process while still receiving direction from the teacher. The results of this study indicate that a directed inquiry learning model can serve as an effective alternative for science learning in primary schools.

The COVID-19 pandemic has exposed fundamental vulnerabilities in global supply chain systems, such as over-reliance on single suppliers and a lack of operational visibility. This has highlighted the urgent need for a new approach to risk management—one that leverages smart technologies. Artificial Intelligence (AI) has emerged as a promising solution, thanks to its capabilities in predictive analytics and adaptive, data-driven decision-making in real time. This study aims to develop an AI-based predictive system framework to enhance the resilience of global supply chains in the face of post-pandemic disruptions. Using the Design Science Research (DSR) methodology, the research designs and evaluates a system that integrates algorithms such as LSTM, Random Forest, Natural Language Processing (NLP), and Reinforcement Learning. It also applies a federated learning approach to ensure data privacy among supply chain partners. The study analyzes over 12,000 data entries from diverse sources, including IoT devices, weather data, demand trends, and social media. The system's effectiveness is evaluated through a combination of quantitative methods (PLS-SEM analysis on 103 respondents) and qualitative methods (interviews with 12 industry executives). The findings show that AI-driven predictive analytics significantly improve supply chain resilience (β = 0.67; p < 0.001), with demand forecasting accuracy increasing by up to 40% and delivery times reduced by 30%. Conceptually, the study contributes by designing a resilient model that integrates real-time visibility, adaptability, and cross-organizational collaborative learning. Unlike traditional approaches focused solely on automation, this framework offers a more holistic solution, addressing key gaps in the literature. The implication is clear: AI is becoming a strategic asset in building sustainable, resilient supply chains amid ongoing global uncertainty.

This study is founded on the issues identified, specifically the dearth of learning resources, the LKPD's lack of training in mathematical critical thinking, and the fact that the LKPD that is currently available was created by educators who did not follow a scientific methodology, which has led to students' low levels of mathematical critical thinking. In order to educate mathematical critical thinking skills in class VIII MTsS Asy-Syarif Koto Laweh for the 2023–2024 academic year, this project intends to produce LKPD based on a scientific approach that satisfies the requirements of validity, practicality, and effectiveness. Using Sugiyono's development paradigm, this study is called research and development, or R&D. Student answer surveys, mathematical critical thinking ability assessments, and validation sheets are the equipment. The techniques for data analysis that are employed are validity analysis, practicality analysis, analysis of effectiveness. Based on development study conducted, LKPD is obtained with a validity level of 84.2%, of which 82.33% are on extremely practical criteria, 78.94% are on effective criteria, and 82.33% are on highly valid criteria for the level of practicality. We may conclude that the LKPD Asy-Syarif Koto Laweh created, which is based on the Scientific Approach to Train Mathematical Critical Thinking Skills in Class VIII MTsS, satisfies the requirements of being valid, practical, and efficient.

Introduction: The integration of Digital Twin (DT) technology and the Internet of Things (IoT) into Building Energy Management Systems (BEMS) offers a transformative approach to optimizing energy consumption in buildings. This study explores the development of a Digital Twin based BEMS prototype, which leverages real time data collection, predictive analytics, and machine learning to enhance energy efficiency, reduce costs, and support sustainability goals in modern buildings. The research also addresses key gaps in current energy management systems, including real time adaptive control and integration with smart grid platforms. Literature Review: Previous research highlights the limitations of traditional BEMS, which often rely on static control strategies and lack real time adaptability. Recent advancements, including predictive maintenance and machine learning integration, have improved energy optimization. However, challenges such as data interoperability, scalability, and cybersecurity remain. This review consolidates current approaches and identifies opportunities for enhancing BEMS through the integration of DT technology, IoT, and machine learning. Materials and Method: The methodology employed involves the design of a Digital Twin based BEMS prototype, incorporating IoT sensors for real time data collection on variables such as HVAC load, occupancy, and environmental factors. The system uses time series forecasting and adaptive control strategies to optimize energy consumption. A case study building is used for validation, with performance metrics such as energy savings, CO₂ footprint reduction, and peak load reduction assessed to evaluate the system's effectiveness. Results and Discussion: The results demonstrate a significant reduction in energy consumption (up to 50%) compared to traditional BEMS, along with improved forecasting accuracy and sustainability performance. The prototype achieved a high R² score in predicting energy usage, validated through real world application in the case study building. The economic feasibility analysis showed substantial cost savings and a strong return on investment, making the system a financially viable solution for energy efficient building management.

The rapid penetration of mobile devices among students has created significant opportunities to optimize mobile-assisted language learning (MALL) for enhancing language acquisition in digital environments. Despite its potential, the implementation of MALL often remains suboptimal due to technical limitations, lack of pedagogical planning, and insufficient integration of interactive features, which limits its effectiveness in supporting structured learning. This study aims to examine how MALL applications influence student engagement, self-regulation, and language learning outcomes, while addressing the gap between technological affordances and instructional practices. Employing a mixed-methods design, the research integrates quantitative surveys, learning analytics, and qualitative interviews with higher education students who regularly use mobile applications for language learning. The methodology includes pre- and post-surveys to measure changes in engagement and self-regulation, log data to track usage patterns, and interviews to capture students’ perceptions and experiences. The findings reveal that MALL significantly enhances engagement through increased active participation, higher attendance, longer study duration, and more frequent interaction with materials. At the same time, MALL improves self-regulation by strengthening skills in time management, goal setting, monitoring progress, and maintaining persistence, leading to greater learner autonomy. Moreover, students with stronger self-regulation capacities were found to utilize MALL features more effectively, resulting in measurable improvements in vocabulary, grammar, and communicative competence. The study concludes that MALL’s effectiveness lies not only in delivering learning content but also in fostering engagement and supporting self-regulated learning. These results highlight the need for pedagogical designs that integrate technological interactivity with scaffolding strategies to maximize the potential of MALL as a learner-centered and sustainable approach to language learning in the digital era.  

This study explores the application of machine learning algorithms, specifically Linear Regression and Decision Tree Regressor, for predicting student academic performance using academic grade data from Kaggle. The analyzed factors include attendance, assignment grades, midterm exam grades, and final exam grades. The research methodology encompasses data collection, preprocessing, model development, training, and validation. This study contributes to the field of educational data analytics by demonstrating how machine learning can provide actionable insights into students' learning patterns and academic outcomes. The findings emphasize the effectiveness of Linear Regression for linearly distributed data and Decision Tree Regressor for capturing complex, non-linear relationships. The implications of this research suggest that machine learning models can assist educators in identifying key factors influencing student performance, enabling targeted interventions to enhance learning outcomes. Future research should explore larger, more diverse datasets and incorporate ensemble methods, such as Random Forest or Gradient Boosting, to improve model generalization and prediction accuracy. Additionally, integrating socio-economic and psychological factors could provide a more holistic perspective on academic achievement.

This study explored the perceptions and challenges faced by senior high school students in utilizing web-based platforms for English language learning. Employing a literature review methodology, the study synthesized findings from recent studies to analyze the effectiveness, user experiences, and obstacles associated with digital learning tools in the context of language education. The study highlighted students' positive attitudes towards the accessibility and interactive features of web-based platforms while identifying challenges such as technical difficulties, limited digital literacy, and motivational issues. The review also examined the pedagogical implications of integrating web-based platforms into traditional learning environments, emphasizing the need for teacher training and support mechanisms to maximize learning outcomes. By providing a comprehensive overview of existing research, this study aimed to contribute to the discourse on leveraging technology for language learning in senior high schools and offers insights for future research and policy development.