Evaluating Machine Learning Techniques for Performance Monitoring and Continuous Improvement in Learning Factory Education
DOI:
https://doi.org/10.33050/itee.v4i1.959Keywords:
Continuous Improvement, Educational Data Mining, Machine Learning, Learning Factory Education, Performance MonitoringAbstract
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.
References
S. Brown, J. Jones et al., “Creating educational solutions for optimizing learning factory operations and outcomes,” International Transactions on Education Technology (ITEE), vol. 3, no. 2, pp. 134–146, 2025. [2] M. Vaz, B. Baetz, and T. Wanyama, “Organizational model of a learning factory for integrated engineering technology and business education,” in INTED2024 Proceedings. IATED, 2024, pp. 248–255.
E. I. Ezepue, “Integrating industrial process optimization models into higher education management: A framework for resource efficiency, decision-making and quality assurance.”
C. H. Pangaribuan, A. Valerry et al., “Data-driven approaches to optimize learning experiences in learning factories,” International Transactions on Education Technology (ITEE), vol. 3, no. 2, pp. 158–170, 2025.
R. Frielinck, “Learning factory configuration tool: An approach for preserving the value of educational learning factories,” Master’s thesis, University of Twente, 2023.
C. Van Slyke, R. D. Johnson, and J. Sarabadani, “Generative artificial intelligence in information systems education: Challenges, consequences, and responses,” Communications of the Association for Information Systems, vol. 53, no. 1, pp. 1–21, 2023.
F. Zidan and D. E. Febriyanti, “Optimizing agricultural yields with artificial intelligence-based climate adaptation strategies,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 5, no. 2, pp. 136–147, 2024.
Q. Fu, A. A. Abdul Rahman, H. Jiang, J. Abbas, and U. Comite, “Sustainable supply chain and business performance: The impact of strategy, network design, information systems, and organizational structure,” Sustainability, vol. 14, no. 3, p. 1080, 2022.
C. Wissuchek and P. Zschech, “Prescriptive analytics systems revised: a systematic literature review from an information systems perspective,” Information systems and e-business management, vol. 23, no. 2, pp. 279–353, 2025.
M. Hatta, W. N. Wahid, F. Yusuf, F. Hidayat, N. A. Santoso, and Q. Aini, “Enhancing predictive models in system development using machine learning algorithms,” International Journal of Cyber and IT Service Management, vol. 4, no. 2, pp. 80–87, 2024.
J. Massa, “Developing a multi-perspective design guide for effective learning factories,” Master’s thesis, University of Twente, 2023.
X. Wang, N. Anwer, Y. Dai, and A. Liu, “Chatgpt for design, manufacturing, and education,” Procedia CIRP, vol. 119, pp. 7–14, 2023.
A. Sutarman, R. Aprianto, R. Adyatama, K. C. Pokkali, and M. Yusup, “Influence of digital technology & data analytics on strategic decision making,” Startupreneur Business Digital (SABDA Journal), vol. 4, no. 1, pp. 12–23, 2025.
U. Rahardja, Q. Aini, D. Manongga, I. Sembiring, and Y. Sanjaya, “Enhancing machine learning with lowcost p m2. 5 air quality sensor calibration using image processing,” APTISI Transactions on Management, vol. 7, no. 3, pp. 201–209, 2023.
D. Hawkridge, New information technology in education. Routledge, 2022.
A. Novak, D. Bennett, and T. Kliestik, “Product decision-making information systems, real-time sensor networks, and artificial intelligence-driven big data analytics in sustainable industry 4.0,” Economics, management and financial markets, vol. 16, no. 2, pp. 62–72, 2021.
M. Hardini, M. H. R. Chakim, L. Magdalena, H. Kenta, A. S. Rafika, and D. Julianingsih, “Image-based air quality prediction using convolutional neural networks and machine learning,” Aptisi Transactions on Technopreneurship (ATT), vol. 5, no. 1Sp, pp. 109–123, 2023.
E. B. Moraes, L. M. Kipper, A. C. Hackenhaar Kellermann, L. Austria, P. Leivas, J. A. R. Moraes, and M. Witczak, “Integration of industry 4.0 technologies with education 4.0: advantages for improvements in learning,” Interactive Technology and Smart Education, vol. 20, no. 2, pp. 271–287, 2023.
K. C. Gonugunta and K. Leo, “Role of data-driven decision making in enhancing higher education performance: A comprehensive analysis of analytics in institutional management,” International Journal of Acta Informatica, vol. 3, no. 1, pp. 149–159, 2024.
B. Zhang, V. Velmayil, and V. Sivakumar, “A deep learning model for innovative evaluation of ideological and political learning,” Progress in Artificial Intelligence, vol. 12, no. 2, pp. 119–131, 2023.
J. Nathalie, G. Jacqueline, N. A. Yusuf, and L. W. Ming, “Optimizing digital business processes through artificial intelligence: A case study in e-commerce systems,” ADI Journal on Recent Innovation, vol. 6, no. 1, pp. 89–98, 2024.
M. U. Mojumder, “Impact of lean six sigma on manufacturing efficiency using a digital twin-based performance evaluation framework,” ASRC Procedia: Global Perspectives in Science and Scholarship, vol. 1, no. 01, pp. 343–375, 2025.
S. Kumar, T. Gopi, N. Harikeerthana, M. K. Gupta, V. Gaur, G. M. Krolczyk, and C. Wu, “Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control,” Journal of Intelligent Manufacturing, vol. 34, no. 1, pp. 21–55, 2023.
A. S. Bist, B. Rawat, S. Kosasi, Q. Aini, F. P. Oganda, and A. B. Yadila, “Proposing a novel framework for prediction of stock using machine learning,” in 2023 11th International Conference on Cyber and IT Service Management (CITSM). IEEE, 2023, pp. 1–5.
R. H. Jhaveri, A. Revathi, K. Ramana, R. Raut, and R. K. Dhanaraj, “A review on machine learning strategies for real-world engineering applications,” Mobile Information Systems, vol. 2022, no. 1, p. 1833507, 2022.
M. Afzal, M. T. Shafiq, and H. Al Jassmi, “Improving construction safety with virtual-design construction technologies-a review.” Journal of Information Technology in Construction, vol. 26, 2021.
A. Rizky, M. Ramaditya, and A. A. Kamal, “Leveraging big data analytics to strategically expand digital microcredit access for msmes,” ADI Journal on Recent Innovation, vol. 7, no. 1, pp. 13–24, 2025.
U.S. Department of Education, Office of Educational Technology, “Artificial intelligence and the future of teaching and learning: Insights and recommendations,” 2023.
O. E. Oluyisola, S. Bhalla, F. Sgarbossa, and J. O. Strandhagen, “Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study,” Journal of Intelligent Manufacturing, vol. 33, no. 1, pp. 311–332, 2022.
M. Chen, Q. Liu, S. Huang, and C. Dang, “Environmental cost control system of manufacturing enterprises using artificial intelligence based on value chain of circular economy,” Enterprise Information Systems, vol. 16, no. 8-9, p. 1856422, 2022.
T. Abass, E. O. Itua, T. Bature, and M. A. Eruaga, “Concept paper: Innovative approaches to food quality control: Ai and machine learning for predictive analysis,” World Journal of Advanced Research and Reviews, vol. 21, no. 3, pp. 823–828, 2024.
M. Migunani, A. Setiawan, and I. Sembiring, “Optimizing automated machine learning for ensemble performance and overfitting mitigation,” Aptisi Transactions on Technopreneurship (ATT), vol. 7, no. 3, pp. 808–822, 2025.
H. Jin and J. Yang, “Using computer-aided design software in teaching environmental art design.” Computer-Aided Design & Applications, vol. 19, 2022.
R. K. Rainer and B. Prince, Introduction to information systems. John Wiley & Sons, 2021.
A. Sevinc¸, “Evaluation of the contribution of model factories to productivity with multi-criteria decision making method: Application of learn & transform program,” Duzce University Journal of Science and Technology, vol. 13, no. 2, pp. 1005–1021, 2025.
M. C. ˇZiˇzi´c, A. A. Meˇstrovi´c, B. Crnoki´c, and I. Peko, “Hands-on training for human-centric digital twin in a learning factory environment,” in International Conference on Digital Transformation in Education and Artificial Intelligence Application. Springer, 2025, pp. 124–136.
J. Massa, W. de Boer, and E. Lutters, “Concurrent development of educational modules and learning factories: A case study in industrial design engineering,” in Conference on Learning Factories. Springer, 2025, pp. 139–146.
F. Saleem, Z. Ullah, B. Fakieh, and F. Kateb, “Intelligent decision support system for predicting student’s e-learning performance using ensemble machine learning,” Mathematics, vol. 9, no. 17, p. 2078, 2021.
A. Sutarman, E. Kallas, and O. Jayanagara, “The effectiveness of using blockchain technology as a machine learning program,” Blockchain Frontier Technology, vol. 4, no. 1, pp. 29–34, 2024.
A. Aljohani, “Predictive analytics and machine learning for real-time supply chain risk mitigation and agility,” Sustainability, vol. 15, no. 20, p. 15088, 2023.
F. Assad, S. Konstantinov, E. Rushforth, D. A. Vera, and R. Harrison, “A literature survey of energy sustainability in learning factories,” in 2020 IEEE 18th international conference on industrial informatics (INDIN). IEEE, 2021.
D. Irawan, S. Suhermanto, and E. K. Mindarta, “Implementation of six sigma to develop teaching factory product quality at smk islam 1 blitar,” in 5th Vocational Education International Conference (VEIC-5 2023). Atlantis Press, 2024, pp. 417–425.
A. Frank´o, G. Holl´osi, D. Ficzere, and P. Varga, “Applied machine learning for iiot and smart production—methods to improve production quality, safety and sustainability,” Sensors, vol. 22, no. 23, p. 9148, 2022.
P. Hareesh, P. Venkataraman, A. Thangaraj, T. Chaudhari, and V. S. Peddada, “Smart manufacturing learning factory integrating cyber-physical systems, digital twins, and remote troubleshooting,” in Conference on Learning Factories. Springer, 2025, pp. 164–171.
E. Cano-Su˜n´en, I. Mart´ınez, ´A. Fern´andez, B. Zalba, and R. Casas, “Internet of things (iot) in buildings: A learning factory,” Sustainability, vol. 15, no. 16, p. 12219, 2023.
M. G. Hardini, T. Khaizure, and G. Godwin, “Exploring the effectiveness of e-learning in fostering innovation and creative entrepreneurship in higher education,” Startupreneur Business Digital (SABDA Jour- nal), vol. 3, no. 1, pp. 34–42, 2024.
T. Xiong, Z. Jia, and W. Zhang, “Teaching management system based on 5g-driven identification resolution for learning factories,” in Conference on Learning Factories. Springer, 2025, pp. 217–224.
J. Willebrand, “Exploring integration of extended reality in an industry 4.0 educational learning factory,” 2025.
R. Bradley, S. S. Salim, and B. W. Anthony, “Learning through development of a digital manufacturing system in a learning factory using low-code/no-code platforms,” Manufacturing Letters, 2025.
U. Rahardja, A. Sari, A. H. Alsalamy, S. Askar, A. H. R. Alawadi, and B. Abdullaeva, “Tribological properties assessment of metallic glasses through a genetic algorithm-optimized machine learning model,” Metals and Materials International, vol. 30, no. 3, pp. 745–755, 2024.
C. Yeh, C. Meng, S. Wang, A. Driscoll, E. Rozi, P. Liu, J. Lee, M. Burke, D. B. Lobell, and S. Ermon, “Sustainbench: Benchmarks for monitoring the sustainable development goals with machine learning,” arXiv preprint arXiv:2111.04724, 2021.
L. Chen, X. Yao, P. Xu, S. K. Moon, and G. Bi, “Rapid surface defect identification for additive manufacturing with in-situ point cloud processing and machine learning,” Virtual and Physical Prototyping, vol. 16, no. 1, pp. 50–67, 2021.
A. Alwiyah and W. Setyowati, “A comprehensive survey of machine learning applications in medical image analysis for artificial vision,” International Transactions on Artificial Intelligence, vol. 2, no. 1, pp. 90–98, 2023.
R. Amaral, H. Castro, F. Pereira, J. Bastos, J. Moreira, A. Mota, and P. ´Avila, “Learning factory 5.0: An open design model for human-centric, sustainable, and flexible technical education,” in Working Conference on Virtual Enterprises. Springer, 2025, pp. 36–51.
U. D. Atmojo, G. ¨Ogmundsd´ottir, R. Bejarano, D. Dowling, and V. Vyatkin, “A digital twin model for an educational turbocharger demonstrator,” in Proceedings of the 12th Conference on Learning Factories (CLF 2022), 2022.
E. Cuan-Urquizo, C. Vazquez, and A. Roman, “Insights into the “pitech academy” learning platform for small manufacturing businesses and learning factories,” in Learning Factories of the Future: Proceedings of the 14th Conference on Learning Factories 2024, Volume 2, vol. 1060. Springer Nature, 2024, p. 233.
P. H¨afner, V. Bergmann, V. H¨afner, F. L. Michels, M. Grethler, and A. Karande, “An adaptive learning environment for industry 4.0 competencies based on a learning factory and its immersive digital twin.” in CSEDU (1), 2024, pp. 720–731.
D. Deepa, S. Yoganand, N. Balaji, and K. Bhattacharjee, “Cost effective machine vision system for real- time defect detection and energy efficiency in learning factories,” in 2025 IEEE International Conference on Emerging Technologies and Applications (MPSec ICETA). IEEE, 2025, pp. 1–5.
G. Schneikart and W. Mayrhofer, “Teaching hybrid project management in a learning studio setting: A logistics use case and a method for measuring learning performance,” in Conference on Learning Factories. Springer, 2025, pp. 37–44.
O. Bianchi and H. P. Putro, “Artificial intelligence in environmental monitoring: Predicting and managing climate change impacts,” International Transactions on Artificial Intelligence, vol. 3, no. 1, pp. 85–96, 2024.
A. S. Rathore, S. Nikita, G. Thakur, and S. Mishra, “Artificial intelligence and machine learning applications in biopharmaceutical manufacturing,” Trends in Biotechnology, vol. 41, no. 4, pp. 497–510, 2023.
N. L. Rane, M. Paramesha, S. P. Choudhary, and J. Rane, “Artificial intelligence, machine learning, and deep learning for advanced business strategies: a review,” Partners Universal International Innovation Journal, vol. 2, no. 3, pp. 147–171, 2024.
T. Gaber, A. El-Ghamry, and A. E. Hassanien, “Injection attack detection using machine learning for smart iot applications,” Physical Communication, vol. 52, p. 101685, 2022.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Elika Setiawaty, Rifqa Nabila Muti, Kristina Vaher, Zulfadli Ardiansyah, Marta Rodriguez
This work is licensed under a Creative Commons Attribution 4.0 International License.
