International Transactions on Education Technology (ITEE)

Enhancing Adaptive Learning Environments in Learning Factories through Artificial Intelligence

2025-11-03T15:55:05+00:00

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

Evaluating the Impact of VR, AR, and Wearable Devices on Outcome-Driven Learning in Engineering Education

2025-11-04T12:30:16+00:00

The integration of immersive technologies such as Virtual Reality (VR), (AR), and wearable devices has transformed the landscape of engineering education, offering new possibilities for interactive and outcome driven learning. This study aims to evaluate the impact of these technologies on students’ learn- ing performance, engagement, and skill acquisition within engineering learn- ing environments. Employing a quantitative research design, data were col- lected from 180 engineering students across three universities through structured pre and post tests, supported by validated engagement and usability question- naires. Statistical analyses, including paired tests and regression models, were conducted to measure the effectiveness of technology assisted learning inter- ventions compared to traditional instructional methods. The results reveal a significant improvement in students’ cognitive performance, practical task effi- ciency, and overall motivation when VR, AR, and wearable technologies were integrated into the curriculum. Moreover, students reported enhanced spatial understanding and problem solving capabilities, indicating that immersive tools foster deeper experiential learning and higher knowledge retention. The find- ings suggest that the systematic implementation of immersive technologies can significantly enhance learning outcomes, bridging the gap between theoretical knowledge and hands on engineering practice. This research highlights the criti- cal role of technology driven innovation in promoting outcome based education, providing valuable insights for educators and policymakers aiming to optimize the use of emerging technologies in engineering education.

Designing Educational Information Systems to Optimize Learning Factory Operations

2025-11-03T08:41:17+00:00

Interdisciplinary teamwork within the learning factory framework has emerged as an effective approach to bridging theoretical knowledge and practical inno- vation, encouraging students from diverse academic backgrounds to collaborate and solve complex real world problems creatively. This educational model pro- motes experiential learning, yet the specific mechanisms through which inter- disciplinary collaboration enhances creativity and innovation remain underex- plored. This study aims to investigate how teamwork across multiple disciplines fosters innovation and creativity in learning factory projects and to identify the factors that facilitate or hinder this process. Using a qualitative case study design, data were collected through semi structured interviews, direct observa- tions, and project documentation involving students and instructors from engi- neering, design, and business programs participating in interdisciplinary learn- ing factory initiatives. The data were analyzed thematically to identify key pat- terns of collaboration, communication, and idea generation. The findings reveal that interdisciplinary teamwork significantly stimulates creative problem solv- ing by integrating diverse perspectives, promoting mutual learning, and creating an environment that values experimentation and iteration. However, challenges such as disciplinary boundaries, communication gaps, and differing work cul- tures occasionally impede the collaborative process. The study concludes that effective facilitation, open communication, and reflective practices are crucial for maximizing innovation and creativity in interdisciplinary learning factory teams. These insights highlight the importance of designing structured yet flexi- ble learning environments that encourage knowledge integration, creative think- ing, and cross disciplinary synergy to prepare students for complex, innovation driven professional contexts.

AI-Driven Educational Data Analytics and Intelligent Tutoring in Learning Factory Environments

2025-11-02T06:46:32+00:00

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.

Evaluating Machine Learning Techniques for Performance Monitoring and Continuous Improvement in Learning Factory Education

2025-11-04T12:17:56+00:00

The rapid advancement of data-driven technologies has transformed the landscape of educational innovation, particularly within learning factory environments that simulate real industrial settings for experiential learning. This study aims to evaluate the effectiveness of various machine learning techniques in monitoring student performance and facilitating continuous improvement in the learning process. Using a quantitative approach, data were collected from student activities, production logs, and performance metrics within a university-based learning factory over one academic term. Several machine learning algorithms, including Decision Tree, Random Forest, and Support Vector Machine, were applied to predict student performance levels and identify critical factors influencing learning efficiency. The analysis revealed that ensemble based models, especially Random Forest, achieved the highest prediction accuracy and provided valuable insights into performance trends, enabling proactive instructional interventions. Additionally, the integration of predictive analytics contributed to improved feedback mechanisms and optimization of task allocation, fostering both individual and group learning outcomes. The findings highlight the significant potential of machine learning in enhancing performance monitoring systems and promoting data informed decision-making in educational manufacturing contexts. This study concludes that the strategic adoption of machine learning techniques can substantially strengthen the feedback loop between learners and instructors, leading to more adaptive, efficient, and sustainable learning processes within the learning factory framework.

Designing an Educational Information System to Enhance Learning Factory Management in Higher Education

2025-11-04T12:41:18+00:00

The increasing adoption of Learning Factory concepts in higher education has highlighted the need for effective educational information systems that can support operational efficiency, resource utilization, and knowledge integration. However, many Learning Factories still face challenges in coordinating instruc- tional activities, managing training equipment scheduling, and ensuring align- ment between educational objectives and industrial practices. This study aims to design an educational information system that optimizes Learning Factory operations through the integration of workflow management, resource alloca- tion, and instructional monitoring components. The research employs a qual- itative design involving observations of operational processes, semi-structured interviews with instructors and laboratory managers, and document analysis to identify functional requirements and system specifications. The findings reveal that existing management practices are largely manual, leading to inefficiencies such as scheduling conflicts, inconsistent training documentation, and limited real-time feedback mechanisms. The proposed system design features central- ized resource scheduling, digital competency tracking, and process visualization dashboards, which collectively support more structured learning activities and improved synchronization between theoretical instruction and practical engage- ment. The study concludes that developing an educational information system tailored to the Learning Factory environment enhances operational coordination, improves transparency in instructional management, and strengthens the linkage between educational outcomes and industrial competencies, thereby contribut- ing to more effective and industry-aligned learning experiences.