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International Transactions on Education Technology (ITEE)
Published by Pandawan Sejahtera Indonesia
ISSN : 29636078     EISSN : 29631947     DOI : https://doi.org/10.33050/itee
Core Subject : Social, Engineering,
Control & Systems Engineering Social Sciences
Computer Science/informatics, Circular Digital Economy, Computer engineering/computer systems, Software Engineering, Information Technology, Information Systems, Cyber Security, Data Science, Artificial Intelligence
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 58 Documents
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Predictive Analytics in Attendance Systems for Employee Productivity and Accountability Putri, Indri Mariska; Ramadhani, Destania Putri; Indriyani, Pifin; Aidah, Elsa Nur; Cahyani, Afifah Putri
International Transactions on Education Technology (ITEE) Vol. 3 No. 2 (2025): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v3i2.718

Abstract

The integration of predictive analytics in attendance systems is becoming a critical approach to improving employee productivity and accountability. However, its impact on technology readiness, employee engagement, and attendance regularity remains underexplored, particularly in educational and professional settings. This study aims to evaluate how Predictive Analytics Utilization (PAU) influences Technology Readiness (TR) and Employee Engagement (EE), and how these variables contribute to Attendance Regularity (AR) and overall employee satisfaction. A quantitative approach was employed using Structural Equation Modeling (SEM) with SmartPLS 4.1. Data were gathered via 40 item questionnaires distributed to Information Systems students at Raharja University. Each variable PAU, TR, EE, and AR was measured through 10 questions to ensure robust data collection and analysis. The findings demonstrate a strong model fit, with R² values of 0.895 for AR, 0.701 for EE, and 0.847 for TR. PAU significantly influences TR and EE, which in turn positively affect AR. Higher levels of technology readiness and engagement enhance attendance regularity, reflecting the effectiveness of predictive analytics. This study highlights the pivotal role of predictive analytics in fostering technological readiness, enhancing employee engagement, and improving attendance regularity. Organizations can leverage these findings to optimize their systems and achieve a more productive workforce. Future research should explore diverse population samples, different organizational contexts, and the integration of advanced analytics tools, such as AI and IoT, to further enhance attendance systems and employee outcomes.
Transforming Learning Experiences With Advanced Educational Technology Solutions Saputro, Janu Ilham; Sa'adah, Lailatus Ferahma; Syifa, Yasyfiyani; Ramadhanti, Kinanti Dara; Gojali, Muhamad Fikri
International Transactions on Education Technology (ITEE) Vol. 3 No. 2 (2025): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v3i2.721

Abstract

The rapid advancements in educational technology have transformed the landscape of learning, necessitating innovative solutions to enhance learner engagement and accessibility. This study examines the development and application of advanced educational technology solutions aimed at revolutionizing learning experiences. The objective is to explore how cutting-edge tools, including adaptive learning systems, artificial intelligence, and virtual reality, can address traditional educational challenges and foster personalized learning environments. Employing a mixed-methods research approach, this study integrates quantitative analysis of learner outcomes with qualitative feedback from educators and students to evaluate the effectiveness of these solutions. The findings reveal significant improvements in learner engagement, comprehension, and retention when utilizing technology-enhanced platforms compared to conventional methods. Furthermore, the integration of real-time analytics enables educators to tailor instructional strategies effectively, promoting inclusivity and accessibility across diverse learning communities. The research concludes that advanced educational technology solutions are pivotal in bridging the gap between traditional education models and the evolving demands of modern learners, offering scalable, efficient, and learner-centric approaches to education. This study contributes to the growing body of knowledge in educational software engineering by highlighting the potential of technology-driven innovations to reshape the future of education, providing actionable insights for stakeholders in academia, industry, and policy-making.
Opportunities and Challenges in Implementing Circular Economy within Digital Platforms Nolan Liam; Arthur Simanjuntak; Henry Newell; Tan, Wei Xiang
International Transactions on Education Technology (ITEE) Vol. 3 No. 2 (2025): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v3i2.765

Abstract

The rapid advancement of digital platforms has opened new avenues for integrating circular economy practices, particularly in optimizing resource use, reducing waste, and fostering sustainable growth. This study aims to investigate both the opportunities and challenges that organizations encounter when implementing circular economy principles within digital environments, focusing on how these platforms can drive more sustainable operations. Adopting a mixed-methods approach, the research gathers quantitative data from digital platform users through surveys and qualitative insights from in-depth interviews with industry experts and business owners across various sectors. The findings indicate that digital platforms present significant opportunities for enhancing resource efficiency, promoting product life extension through recycling and reuse options, and enabling collaborative networks that support circular practices. However, substantial challenges are identified, including high initial investment costs, technical and regulatory barriers, and a lack of widespread digital literacy, especially among small and medium-sized enterprises. Additionally, the research highlights issues related to data privacy and technological compatibility, which can limit broader adoption and effective implementation of circular strategies. The study concludes that while digital platforms hold transformative potential for advancing circular economy goals, success depends on developing supportive policies, fostering collaborative ecosystems, and enhancing digital skills across industries to overcome these obstacles. This research provides valuable insights for policymakers, business leaders, and technology providers seeking to leverage digital tools in the shift toward a sustainable circular economy.
Creating Educational Solutions for Optimizing Learning Factory Operations and Outcomes Henry; Sarah Brown; Jack Jones
International Transactions on Education Technology (ITEE) Vol. 3 No. 2 (2025): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v3i2.790

Abstract

The rapid development of industrial education has prompted a growing need for effective Learning Factory (LF) management systems that integrate educational principles with industrial practices. This paper investigates the development of educational information systems tailored to optimize learning factory operations. The study aims to design an innovative information system and e-learning platform that streamlines the operational management of learning factories, ensuring effective resource allocation and maximizing the educational value of these settings. A mixed-methods approach, combining qualitative interviews with industry experts and quantitative surveys from educational institutions, was used to gather data on the needs and effectiveness of current management tools. Additionally, a prototype system was developed using agile software development methodologies. The findings reveal that the proposed system significantly improves the management of resources, enhances the learning experience, and bridges the gap between theoretical education and practical industrial applications. Moreover, the e-learning platform supports continuous knowledge transfer and facilitates real-time decision-making in the factory environment. The study concludes that the integration of tailored information systems and e-learning platforms in learning factories not only optimizes operational efficiency but also enriches the educational outcomes for students. This research offers valuable insights for educational institutions and industries aiming to align their training programs with the latest industrial advancements.
Utilizing Wearable Technologies to Foster Outcome-Based Education in Learning Factories Sofiyan; Lucas Lawrence; Lily Maria Evans; Khaizure Mirdad; Chen Yu
International Transactions on Education Technology (ITEE) Vol. 3 No. 2 (2025): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v3i2.793

Abstract

The integration of wearable technologies into educational settings has opened new avenues for enhancing experiential and outcome-based learning, particularly in practice-oriented environments such as learning factories. This study investigates how wearable devices such as smart glasses, biometric trackers, and haptic feedback systems can be effectively utilized to support real-time performance monitoring, contextual learning, and continuous skill assessment in engineering and manufacturing training. The objective of this research is to explore the potential of these technologies in reinforcing the principles of outcome-based education (OBE), where learner competence is measured through demonstrable performance rather than passive knowledge acquisition. A mixed-method approach was adopted, combining qualitative field observations and interviews with quantitative data collected through controlled experiments involving wearable technology use in a simulated learning factory environment. The findings reveal that wearables significantly contribute to increased learner engagement, improved task efficiency, and enhanced feedback mechanisms, leading to better alignment between learning outcomes and industrial competency demands. Moreover, the results suggest that wearable-assisted learning environments foster reflective learning and support personalized instruction by capturing granular data on learner behaviors and outcomes. This research concludes that integrating wearable technologies into learning factories not only enhances the quality and relevance of vocational and technical education but also supports broader sustainable development goals by promoting inclusive, adaptive, and technologically enriched learning systems. The study provides a foundation for future research into scalable, data-driven educational models and the role of emerging technologies in transforming skill-based education.
Data-Driven Approaches to Optimize Learning Experiences in Learning Factories Christian Haposan Pangaribuan; Adele Valerry; Stephanie
International Transactions on Education Technology (ITEE) Vol. 3 No. 2 (2025): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v3i2.796

Abstract

This research investigates the application of data-driven approaches to optimize learning experiences in learning factories, a key area for advancing industrial and educational integration. The background of the study highlights the increasing relevance of data science in educational settings, particularly in learning factories, which combine practical learning environments with industrial technologies. The objective of this research is to explore how data science techniques, such as machine learning and predictive analytics, can be utilized to improve learning outcomes, efficiency, and engagement within these settings. The method involves a comprehensive analysis of student performance data collected from learning factory environments, employing statistical tools and data visualization techniques to identify patterns, trends, and areas for improvement. The results reveal that the integration of data-driven methodologies leads to enhanced learning experiences by tailoring content delivery, improving resource allocation, and providing real-time feedback to learners. The study concludes that data science can significantly optimize learning processes in learning factories by providing actionable insights that support both instructors and students in achieving better educational outcomes. These findings underscore the practical applicability of data science in real-world educational scenarios, suggesting that the use of data analytics in learning factories can bridge the gap between theory and practice, fostering a more effective and personalized learning experience.
Enhancing Adaptive Learning Environments in Learning Factories through Artificial Intelligence Natasya, Ersa Aura; Lestari Santoso, Nuke Puji; Lukita Pasha; Hua, Chua Toh; Carlos Perez
International Transactions on Education Technology (ITEE) Vol. 4 No. 1 (2025): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v4i1.957

Abstract

The rapid advancement of Artificial Intelligence (AI) has significantly trans- formed educational paradigms, particularly in adaptive learning environments where real-time personalization and intelligent feedback are essential. This study aims to explore how AI-driven mechanisms can enhance adaptive learning within learning factory environments by utilizing data analytics to personalize learning processes and optimize instructional delivery. Employing a quantita- tive research design, the data collection process involved distributing question- naires to 200 university students enrolled in AI-supported learning factory pro- grams. From this distribution, 120 valid responses were successfully obtained and analyzed, consisting of 80 students and 40 instructors across three universi- ties, representing the final usable dataset for this study. Statistical analysis was performed using regression and correlation models to assess the impact of AI- based adaptivity on learning performance, engagement, and cognitive retention. The findings reveal that AI integration within learning factories leads to sig- nificant improvements in learner adaptability, interaction efficiency, and overall academic achievement. The adaptive AI models dynamically adjusted learning content based on individual performance metrics, resulting in higher engage- ment rates and enhanced skill mastery compared to traditional non-AI-based environments. The outcomes confirm that AI can function as a critical enabler of responsive and data-driven education by bridging theoretical and practical as- pects of industrial learning. This research underscores the transformative poten- tial of Artificial Intelligence in reshaping adaptive learning environments within learning factories, emphasizing the need for further development of AI systems that prioritize personalization, continuous assessment, and the seamless integra- tion of human and machine intelligence
AI-Driven Educational Data Analytics and Intelligent Tutoring in Learning Factory Environments Abas Sunarya; Sunarjo, Richard Andre; Abbas, Maulana; Al-Kamari, Omar Arif; Sabda Maulana
International Transactions on Education Technology (ITEE) Vol. 4 No. 1 (2025): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v4i1.950

Abstract

The rapid growth of artificial intelligence in higher education creates new op- portunities to make learning factory environments more adaptive, data-informed, and aligned with industrial practice. This study examines how the integration of educational data analytics and intelligent tutoring systems supports smarter learning factory models that connect theoretical instruction with hands-on indus- trial training. Using a quantitative research design, data were collected from 180 higher education students participating in AI-supported learning factory sessions. Log data on learning interactions, performance metrics, and system- generated feedback were analyzed using statistical modeling to test the effects of AI-driven interventions on learning outcomes. The results show that ed- ucational data analytics significantly increases the adaptability of instructional content, enabling the intelligent tutoring system to personalize learning paths in real time based on individual performance profiles. Students who engaged with AI-based tutoring reported higher learning engagement and achieved better problem-solving scores and stronger retention of practical concepts than those in conventional learning factory settings. These findings indicate that combining educational data analytics with intelligent tutoring systems improves both the efficiency and effectiveness of learning factory models by enabling continuous feedback loops, dynamic adjustment of learning tasks, and learner-centered in- struction. The study concludes that AI-driven, data-informed learning factories can play a strategic role in preparing students with industry-relevant compe- tences and offers practical implications for educational technologists and insti- tutions designing next-generation education technology solutions.

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