This study evaluates the performance of learning CNC machining in mechanical engineering degree programs by assessing students’ mental workload (MWL). Both subjective (NASA-Task Load Index) and objective (increasing ratio of heart rate, IRH) evaluation methods were employed to assess the MWL of 46 students randomly assigned to two groups: a traditional training group and a combined simulation training group. The results demonstrate the significant impact of the training method on students’ MWL. Specifically, the MWL and IHR values of the combined simulation training group were 54.3 and 9.2, respectively, while those of the traditional training group were 58.3 and 13.2. Additionally, the correlation analysis showed a significant correlation between the IHR value and the MWL score of NASA-TLX. These findings suggest that implementing simulated software and the combined simulation training method effectively reduces students’ MWL, thereby promoting active knowledge acquisition. Consequently, students’ MWL can be utilized to evaluate the effectiveness of technical education with the aim of enhancing training efficiency and quality.
KumarKRanjanCDavimJP. CNC programming for machining. Cham, Switzerland: Springer International Publishing, 2020.
2.
TranCCNguyenVTTranCL. Prediction model and optimization of machining parameters using integrated ANN-GA method on CNC milling machine. TNU Journal of Science and Technology2021; 226: 20–29.
3.
DavimJP. Mechanical engineering education. Hoboken, New Jersey, USA: John Wiley & Sons, 2012.
4.
TaherMKhanA. Impact of simulation-based and hands-on teaching methodologies on students’ learning in an engineering technology program. In: Proceedings of the ASEE Annual Conference & Exposition, 2014, pp.1–22.
5.
AyasMSAltasIH. A virtual laboratory for system simulation and control with undergraduate curriculum. Comput Appl Eng Educ2016; 24: 122–130.
6.
SeifanMLalNBerenjianA. Effect of undergraduate research on students’ learning and engagement. Int J Mech Eng Educ2022; 50: 326–348.
7.
AdamiPRodriguesPBWoodsPJ, et al.Impact of VR-based training on human–robot interaction for remote operating construction robots. J Comput Civ Eng2022; 36: 04022006.
8.
FeiselLDRosaAJ. The role of the laboratory in undergraduate engineering education. Journal of Engineering Education2005; 94: 121–130.
9.
RobertsTG. An Interpretation of Dewey's Experiential Learning Theory. 2003.
10.
SlavinREDavisN. Educational psychology: Theory and practice. 2006.
11.
LiWGrossmanTFitzmauriceG. GamiCAD: a gamified tutorial system for first time autocad users. Proceedings of the 25th annual ACM symposium on User interface software and technology. 103–112. 2012.
12.
MarkopoulosAPFragkouAKasidiarisPD, et al.Gamification in engineering education and professional training. Int J Mech Eng Educ2015; 43: 118–131.
13.
ReddyNRSatyanarayanaVKumarJJ, et al.Gamification technique in engineering education to improve learnability. Eng Educ Transform2020; 33: 147–149.
14.
LegakiN-ZXiNHamariJ, et al.The effect of challenge-based gamification on learning: an experiment in the context of statistics education. Int J Hum Comput Stud2020; 144: 102496.
15.
BaiSHewKFGondaDE, et al.Incorporating fantasy into gamification promotes student learning and quality of online interaction. International Journal of Educational Technology in Higher Education2022; 19: 1–26.
16.
RaniNAAzizFAMuthuN, et al.Gamified Augmented Reality Application as a Learner Model for CNC Machining Course in TVET System. INNOVATING EDUCATION FOR A BETTER TOMORROW. 44–47. 2022.
17.
JonesR. Physical ergonomic and mental workload factors of mobile learning affecting performance of adult distance learners: Student perspective. Orlando, Florida, USA: University of Central Florida, 2009.
18.
TranCCHoangSHoangTD, et al.Assessing effectiveness of wood spray painting system model for undergraduate engineering education. Comput Appl Eng Educ2019; 27: 29–37.
19.
ChaoCJWuSYYauYJ, et al.Effects of three-dimensional virtual reality and traditional training methods on mental workload and training performance. Human Factors and Ergonomics in Manufacturing & Service Industries2017; 27: 187–196.
20.
LuongTArgelaguetFMartinN, et al.Introducing mental workload assessment for the design of virtual reality training scenarios. In: 2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), 2020, pp.662–671: IEEE.
KimHHongTKimJ, et al.A psychophysiological effect of indoor thermal condition on college students’ learning performance through EEG measurement. Build Environ2020; 184: 107223.
23.
WidyantiAHasudunganSParkJ. e-Learning readiness and perceived learning workload among students in an Indonesian university. Knowledge Management & E-Learning2020; 12: 18–29.
24.
FebiyaniAFebrianiAMa'SumJ. Calculation of mental load from e-learning student with NASA TLX and SOFI method. Jurnal Sistem dan Manajemen Industri2021; 5: 35–42.
25.
Therisa BeenaKKSonyM. Student workload assessment for online learning: an empirical analysis during COVID-19. Cogent Engineering2022; 9: 2010509.
26.
XiaoqinZYuRHaifangW, et al.Applying simulation teaching method to numerical control machining practice. In: 2010 International Conference on Educational and Information Technology, 2010, IEEE, V2-183-V182-186.
27.
GaoQWangYSongF, et al.Mental workload measurement for emergency operating procedures in digital nuclear power plants. Ergonomics2013; 56: 1070–1085.
28.
SheridanTStassenH. Definitions, models and measures of human workload. Mental workload: Its theory and measurement 1979: 219–233.
29.
XieBSalvendyG. Review and reappraisal of modelling and predicting mental workload in single-and multi-task environments. Work & Stress2000; 14: 74–99.
30.
ByrneA. The effect of education and training on mental workload in medical education. Human Mental Workload: Models and Applications: Second International Symposium, H-WORKLOAD 2018, Amsterdam, The Netherlands, September 20–21, 2018, Revised Selected Papers 2. Springer, 258–266. 2019.
31.
PrabaswariADBasumerdaCUtomoBW. The mental workload analysis of staff in study program of private educational organization. In: IOP Conference Series: Materials Science and Engineering, 2019, IOP Publishing, 012018.
32.
GawronVJ. Human performance, workload, and situational awareness measures handbook, −2-volume set. London, England: Crc Press, 2019.
33.
SalvendyGKarwowskiW. Handbook of human factors and ergonomics. Hoboken, New Jersey, USA: John Wiley & Sons, 2021.
34.
HancockGLongoLYoungM, et al.Mental workload. Handbook of human factors and ergonomics. 203–226, 2021.
35.
PuspawardhaniEHSuryoputroMRSariAD, et al.Mental workload analysis using NASA-TLX method between various level of work in plastic injection division of manufacturing company. In: Advances in safety management and human factors. Cham, Switzerland: Springer International Publishing, 2016, pp.311–319.
36.
AkyeampongJUdokaSCarusoG, et al.Evaluation of hydraulic excavator human–machine interface concepts using NASA TLX. Int J Ind Ergon2014; 44: 374–382.
37.
YanSTranCCChenY, et al.Effect of user interface layout on the operators’ mental workload in emergency operating procedures in nuclear power plants. Nucl Eng Des2017; 322: 266–276.
38.
BridgerRSBrasherK. Cognitive task demands, self-control demands and the mental well-being of office workers. Ergonomics2011; 54: 830–839.
39.
HartSG. NASA-task load index (NASA-TLX); 20 years later. In: Proceedings of the human factors and ergonomics society annual meeting. Los Angeles, CA: Sage Publications Sage CA, 2006, pp.904–908.
40.
YanSTranCCWeiY, et al.Driver’s mental workload prediction model based on physiological indices. Int J Occup Saf Ergon2019; 25(3): 476–484.
41.
DingYCaoYDuffyVG, et al.Measurement and identification of mental workload during simulated computer tasks with multimodal methods and machine learning. Ergonomics2020; 63: 896–908.
42.
PettitMLDeBarrKA. Perceived stress, energy drink consumption, and academic performance among college students. J Am Coll Health2011; 59: 335–341.
43.
BachmanLBachmanC. Student perceptions of academic workload in architectural education. J Archit Plann Res2006; 23(4): 271–304.
44.
CopeCStaehrL. Improving students’ learning approaches through intervention in an information systems learning environment. Studies in Higher Education2005; 30: 181–197.
45.
WeberJSkoddaSMuthT, et al.Stressors and resources related to academic studies and improvements suggested by medical students: a qualitative study. BMC Med Educ2019; 19: 1–14.
46.
YuNWuP. Effect of physical fatigue on cognitive ability of workers in furniture CNC operation. In: Man-Machine-Environment System Engineering: Proceedings of the 18th International Conference on MMESE 18, 2019, pp.49–55: Springer.
47.
Hernandez ArellanoJLSerratos PerezJNAlcarazJLG, et al.Assessment of workload, fatigue, and musculoskeletal discomfort among computerized numerical control lathe operators in Mexico. IISE Transactions on Occupational Ergonomics and Human Factors2017; 5: 65–81.
48.
De RivecourtMKuperusMNPostWJ, et al.Cardiovascular and eye activity measures as indices for momentary changes in mental effort during simulated flight. Ergonomics2008; 51: 1295–1319.
