Development of a Team-Based Experimental Learning (TeBEL) Model to Enhance Higher-Order Thinking Skills in Industrial Product Problem-Solving
DOI:
https://doi.org/10.56294/saludcyt20262534Keywords:
Cutting Tool, High Order Thinking, Skills, Team-Based Experiment Learning, Tool DesignAbstract
The gap between theoretical learning in higher education and the real needs of the manufacturing industry remains a significant challenge in engineering education, particularly in mastering Tool Design competencies. Students generally understand the concepts theoretically, but their analytical, evaluative, and creative abilities for solving industrial design problems are not yet fully developed. This study aims to develop and test the effectiveness of the Team-Based Experimental Learning (TeBEL) learning model in improving students’ Higher-Order Thinking Skills (HOTS) through a collaborative and experiential approach. The research method employs a Research and Development (R&D) approach, utilising a 4D model (Define, Design, Develop, and Disseminate). A total of eight validators, comprising academics and industry practitioners, assessed the construct validity, content, and feasibility of the model’s implementation, with an average score of 4,8, categorising it as very valid. The effectiveness test involved a control class using conventional methods and an experimental class implementing TeBEL. The results of the Shapiro–Wilk normality test showed a significance value of 0,921 in the control class and 0,175 in the experimental class (both > 0,05), while the Levene test produced a value of 0,610 (> 0,05), so the data were declared homogeneous and met the requirements for parametric testing. The independent t-test showed a significant difference between the two classes (Sig. 0,000 < 0,05), where the experimental class experienced a higher increase in learning outcomes with an average N-Gain of 0,59 (medium–high category) compared to the control class of 0,31. These findings confirm that the TeBEL model is effective in bridging the gap between theory and practice, improving students' analytical, evaluative, and creative abilities, and strengthening the application of collaborative and experiential learning in engineering education. This model has the potential to be replicated in various engineering courses, improving graduates’ readiness to face the challenges of the modern manufacturing industry.
References
1. Islam MA. Industry 4.0: Skill set for employability. Soc Sci Humanit Open [Internet]. 2022;6(1):100280. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2590291122000341 DOI: https://doi.org/10.1016/j.ssaho.2022.100280
2. Rikala P, Braun G, Järvinen M, Stahre J, Hämäläinen R. Understanding and measuring skill gaps in Industry 4.0 — A review. Technol Forecast Soc Change [Internet]. 2024 Apr;201:123206. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0040162524000027 DOI: https://doi.org/10.1016/j.techfore.2024.123206
3. Li L. Reskilling and Upskilling the Future-ready Workforce for Industry 4.0 and Beyond. Inf Syst Front [Internet]. 2024 Oct 13;26(5):1697–712. Available from: https://link.springer.com/10.1007/s10796-022-10308-y DOI: https://doi.org/10.1007/s10796-022-10308-y
4. Elnadi M, Abdallah YO. Industry 4.0: critical investigations and synthesis of key findings. Manag Rev Q [Internet]. 2024 Jun 1;74(2):711–44. Available from: https://link.springer.com/10.1007/s11301-022-00314-4 DOI: https://doi.org/10.1007/s11301-022-00314-4
5. Keane K, Yeow P. Blurring the Boundaries: The New Collaborative Education, Work and Skills Ecosystem. In: Design Education Across Disciplines: Transformative Learning Experiences for the 21st Century. 2023. p. 55–69. DOI: https://doi.org/10.1007/978-3-031-23152-0_4
6. Prasetya TA, Munadi S, Sukardi T, Setiyawan A. Essential Soft Skills for Engineering in Industry 4.0: A Systematic Literature Review. Int J Eng Educ [Internet]. 2025;41(5):1376–85. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-105016784210&partnerID=40&md5=9afec6f3e02254b302f68ea519c46604
7. Wang K, Wang A, Wu L, Xie G. Machine Tool Wear Prediction Technology Based on Multi-Sensor Information Fusion. Sensors [Internet]. 2024 Apr 21;24(8):2652. Available from: https://www.mdpi.com/1424-8220/24/8/2652 DOI: https://doi.org/10.3390/s24082652
8. Younas M, Khan M, Jaffery SHI, Khan Z, Khan N. Investigation of tool wear and energy consumption in machining Ti6Al4V alloy with uncoated tools. Int J Adv Manuf Technol [Internet]. 2024 Jun 16;132(7–8):3785–99. Available from: https://link.springer.com/10.1007/s00170-024-13548-1 DOI: https://doi.org/10.1007/s00170-024-13548-1
9. Rao CJ, Sreeamulu D, Mathew AT. Analysis of tool life during turning operation by determining optimal process parameters. Procedia Eng. 2014;97:241–50. DOI: https://doi.org/10.1016/j.proeng.2014.12.247
10. Tembrevilla G, Phillion A, Zeadin M. Experiential learning in engineering education: A systematic literature review. J Eng Educ [Internet]. 2024 Jan 17;113(1):195–218. Available from: https://onlinelibrary.wiley.com/doi/10.1002/jee.20575 DOI: https://doi.org/10.1002/jee.20575
11. Salinas-Navarro DE, Garay-Rondero CL, Arana-Solares IA. Digitally Enabled Experiential Learning Spaces for Engineering Education 4.0. Educ Sci [Internet]. 2023 Jan 8;13(1):63. Available from: https://www.mdpi.com/2227-7102/13/1/63 DOI: https://doi.org/10.3390/educsci13010063
12. Lunt AJG, Cayzer S, Moore Y, Young AM. Team-based learning in large cohorts: Successes and challenges in first year mechanical engineering. Int J Mech Eng Educ [Internet]. 2025 Oct 5;53(4):772–94. Available from: https://journals.sagepub.com/doi/10.1177/03064190241259305 DOI: https://doi.org/10.1177/03064190241259305
13. Sutopo S, Setiadi BR, Prasetya TA, Harjanto CT, Sasongko BT, Saputri VHL. Peer-Project-Based Learning in CNC Simulation Programming Courses. TEM J. 2024;13(4):3079–85. DOI: https://doi.org/10.18421/TEM134-42
14. Sutopo, Setiadi BR, Nashir IM, Lutviana VH, Saputri, Arifin A, et al. Enhancing students ’ self-efficacy and creativity in computer numerical control machining through peer-assisted project-based learning. J Pendidik Vokasi. 2024;14(2):232–43. DOI: https://doi.org/10.21831/jpv.v14i2.72409
15. O’Connell RM. Adapting team-based learning for application in the basic electric circuit theory sequence. IEEE Trans Educ. 2015;58(2):90–7. DOI: https://doi.org/10.1109/TE.2014.2329650
16. Minz NK, Saluja A. Developing Skills With Team-Based Learning. In 2023. p. 25–53. Available from: https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-6684-7583-6.ch002 DOI: https://doi.org/10.4018/978-1-6684-7583-6.ch002
17. Sneha Y, Aluvala S. Impact of experimental learning on graduates success in engineering education. J Eng Educ Transform. 2021;34(Special Issue):666–9. DOI: https://doi.org/10.16920/jeet/2021/v34i0/157240
18. Thiagarajan S, Semmel DS, Semmel MI. Instructional Development for Training Teacher of Exceptional Children. Leadership Training Institute/Special Education, University of Minnesota; 1974.
19. Creswell WJ, Creswell JD. Research Design: Qualitative, Quantitative and Mixed Methods Approaches. Fifth Edit. California: Sage Publication, Inc.; 2018.
20. Cohen L, Manion L, Morrison K. Research Methods in Education. 6th ed. New York: Routledge; 2007. DOI: https://doi.org/10.4324/9780203029053
21. Anderson LW, Krathwohl DR. A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives. UK: Allyn & Bacon.Boston, MA; 2001.
22. Vygotsky LS. Mind in Society: The Development of Higher Psychological Processes. Cole M, Jolm-Steiner V, Scribner S, Souberman E, editors. Cambridge: Harvard University Press; 1978.
23. Jumaat NF, Tasir Z, Halim NDA, Ashari ZM. Project-Based Learning from Constructivism Point of View. Adv Sci Lett [Internet]. 2017 Aug 1;23(8):7904–6. Available from: http://www.ingentaconnect.com/content/10.1166/asl.2017.9605 DOI: https://doi.org/10.1166/asl.2017.9605
24. López F, Contreras M, Nussbaum M, Paredes R, Gelerstein D, Alvares D, et al. Developing Critical Thinking in Technical and Vocational Education and Training. Educ Sci [Internet]. 2023 Jun 9;13(6):590. Available from: https://www.mdpi.com/2227-7102/13/6/590 DOI: https://doi.org/10.3390/educsci13060590
25. Van den Beemt A, Groothuijsen S, Ozkan L, Hendrix W. Remote labs in higher engineering education: engaging students with active learning pedagogy. J Comput High Educ [Internet]. 2023 Aug 12;35(2):320–40. Available from: https://link.springer.com/10.1007/s12528-022-09331-4 DOI: https://doi.org/10.1007/s12528-022-09331-4
26. Espey M. Enhancing critical thinking using team-based learning. High Educ Res Dev [Internet]. 2018 Jan 2;37(1):15–29. Available from: https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-6684-7583-6.ch001 DOI: https://doi.org/10.1080/07294360.2017.1344196
27. Swanson E, McCulley L V, Osman DJ, Scammacca Lewis N, Solis M. The effect of team-based learning on content knowledge: A meta-analysis. Act Learn High Educ [Internet]. 2019 Mar 21;20(1):39–50. Available from: https://journals.sagepub.com/doi/10.1177/1469787417731201 DOI: https://doi.org/10.1177/1469787417731201
28. Alinaghizadeh A, Hadad M, Azarhoushang B. Experimental Study of the Surface Quality of Form-Cutting Tools Manufactured via Wire Electrical Discharge Machining Using Different Process Parameters. Micromachines [Internet]. 2023 Oct 24;14(11):1976. Available from: https://www.mdpi.com/2072-666X/14/11/1976 DOI: https://doi.org/10.3390/mi14111976
29. Marra RM, Hacker DJ, Plumb C. Metacognition and the development of self‐directed learning in a problem‐based engineering curriculum. J Eng Educ [Internet]. 2022 Jan 23;111(1):137–61. Available from: https://onlinelibrary.wiley.com/doi/10.1002/jee.20437 DOI: https://doi.org/10.1002/jee.20437
30. Devika, Singh R. Influence of metacognitive awareness on engineering students’ performance: a study of listening skills. Procedia Manuf [Internet]. 2019;31:136–41. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2351978919303841. DOI: https://doi.org/10.1016/j.promfg.2019.03.021
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Copyright (c) 2026 Chrisna Tri Harjanto , Thomas Sukardi , Dwi Rahdiyanta , Sutopo, Bayu Rahmat Setiadi , Tri Adi Prasetya (Author)

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