Calculation of mental load from e-learning student with NASA TLX and SOFI method

Anastasia Febiyani; Atik Febriani; Jauhar Ma'Sum

doi:10.30656/jsmi.v5i1.2789

Anastasia Febiyani Institut Teknologi Telkom Purwokerto
Atik Febriani Institut Teknologi Telkom Purwokerto
Jauhar Ma'Sum Institut Teknologi Telkom Purwokerto

https://doi.org/10.30656/jsmi.v5i1.2789

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Keywords: Mental workload, E-learning, NASA-TLX, SOFI

Abstract

The learning process between students and lecturers usually occurs face-to-face in class. Technological developments and a continuous pandemic change the learning process to be a face-to-face e-learning process. The mental load during face-to-face learning is very different from learning in e-learning. This study was built using ergonomic thinking that is integrated with the use of e-learning. Cognitive ergonomics see from the point of view of students' comfort in cognitive thinking processes when doing e-learning. Data processing and testing will use a questionnaire derived from the NASA-TLX method. The results obtained from this study are the mental load calculations of each NASA TLX calculation. NASA TLX calculations show that efforts with a value of 267.29 dominate students. It could indicate that in e-learning lectures, students need more effort in conducting lectures. In addition, students experience fatigue while participating in online learning. It can be seen from the average SOFI measurement, which is only 1.26.

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References

E. Kahiigi Kigozi, L. Ekenberg, H. Hansson, M. Danielson, and F. F. Tusubira, “Explorative study of e-learning in Developing countries: A case of the Uganda education system,” in IADIS International Conference e-Learning 2008, Amsterdam, Netherlands, 2008, pp. 195–199. Available: https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A185035&dswid=-6960.

W. Hartanto, “Penggunaan E-Learning sebagai Media Pembelajaran,” J. Pendidik. Ekon. J. Ilm. Ilmu Pendidikan, Ilmu Ekon. dan Ilmu Sos. Vol 10 No 1, vol. 10, no. 1, pp. 1–14, Nov. 2016. Available: https://jurnal.unej.ac.id/index.php/JPE/article/view/3438.

A. H. Afridi, “Transparency for Beyond-Accuracy Experiences: A Novel User Interface for Recommender Systems,” Procedia Comput. Sci., vol. 151, pp. 335–344, 2019, doi: https://doi.org/10.1016/j.procs.2019.04.047.

N. Meshkati, P. A. Hancock, M. Rahimi, and S. M. Dawes, “Techniques in mental workload assessment.,” Eval. Hum. Work. a Pract. Ergon. Methodol., pp. 605–627, 1995. Available: https://psycnet.apa.org/record/1995-98869-013.

R. K. Mehta and M. J. Agnew, “Effects of concurrent physical and mental demands for a short duration static task,” Int. J. Ind. Ergon., vol. 41, no. 5, pp. 488–493, 2011, doi: https://doi.org/10.1016/j.ergon.2011.04.005.

S. G. Hart and L. E. Staveland, “Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research,” in Human Mental Workload, vol. 52, P. A. Hancock and N. B. T.-A. in P. Meshkati, Eds. North-Holland, 1988, pp. 139–183. doi: https://doi.org/10.1016/S0166-4115(08)62386-9.

P. A. Hancock and N. Meshkati, Human mental workload. North-Holland Amsterdam, 1988. Available: https://human-factors.arc.nasa.gov/publications/Hart_Staveland_ORIGINAL_1.pdf.

D. Schuff, K. Corral, and O. Turetken, “Comparing the understandability of alternative data warehouse schemas: An empirical study,” Decis. Support Syst., vol. 52, no. 1, pp. 9–20, 2011, doi: https://doi.org/10.1016/j.dss.2011.04.003.

L. M. Mazur et al., “Quantitative Assessment of Workload and Stressors in Clinical Radiation Oncology,” Int. J. Radiat. Oncol., vol. 83, no. 5, pp. e571–e576, 2012, doi: https://doi.org/10.1016/j.ijrobp.2012.01.063.

J. Akyeampong, S. Udoka, G. Caruso, and M. Bordegoni, “Evaluation of hydraulic excavator Human–Machine Interface concepts using NASA TLX,” Int. J. Ind. Ergon., vol. 44, no. 3, pp. 374–382, 2014, doi: https://doi.org/10.1016/j.ergon.2013.12.002.

E. Darvishi, A. Maleki, O. Giahi, and A. Akbarzadeh, “Subjective Mental Workload and Its Correlation With Musculoskeletal Disorders in Bank Staff,” J. Manipulative Physiol. Ther., vol. 39, no. 6, pp. 420–426, 2016, doi: https://doi.org/10.1016/j.jmpt.2016.05.003.

D. S. Dewi and T. Septiana, “Workforce Scheduling Considering Physical and Mental Workload: A Case Study of Domestic Freight Forwarding,” Procedia Manuf., vol. 4, pp. 445–453, 2015, doi: https://doi.org/10.1016/j.promfg.2015.11.061.

M. K. Choi, S. M. Lee, J. S. Ha, and P. H. Seong, “Development of an EEG-based workload measurement method in nuclear power plants,” Ann. Nucl. Energy, vol. 111, pp. 595–607, 2018, doi: https://doi.org/10.1016/j.anucene.2017.08.032.

M. Fallahi, M. Motamedzade, R. Heidarimoghadam, A. R. Soltanian, and S. Miyake, “Effects of mental workload on physiological and subjective responses during traffic density monitoring: A field study,” Appl. Ergon., vol. 52, pp. 95–103, 2016, doi: https://doi.org/10.1016/j.apergo.2015.07.009.

M. Aniţei, M. Chraif, and E. Ioniţă, “Gender Differences in Workload and Self-perceived Burnout in a Multinational Company from Bucharest,” Procedia - Soc. Behav. Sci., vol. 187, pp. 733–737, 2015, doi: https://doi.org/10.1016/j.sbspro.2015.03.155.

C.-Y. Cho, Y.-S. Hwang, and R.-J. Cherng, “Musculoskeletal Symptoms and Associated Risk Factors Among Office Workers With High Workload Computer Use,” J. Manipulative Physiol. Ther., vol. 35, no. 7, pp. 534–540, 2012, doi: https://doi.org/10.1016/j.jmpt.2012.07.004.

S. Neupane and C.-H. Nygård, “Physical and mental strain at work: Relationships with onset and persistent of multi-site pain in a four-year follow up,” Int. J. Ind. Ergon., vol. 60, pp. 47–52, 2017, doi: https://doi.org/10.1016/j.ergon.2016.03.005.

D. L. Setyowati, Z. Shaluhiyah, and B. Widjasena, “Penyebab Kelelahan Kerja pada Pekerja Mebel,” Kesmas Natl. Public Heal. J., vol. 8, no. 8, pp. 386–392, May 2014, doi: https://doi.org/10.21109/kesmas.v8i8.409.

A. M. S. Budiono, R. M. S. Jusuf, and A. Pusparini, Bunga rampai higiene perusahaan ergonomi (HIPERKES) dan kesehatan dan keselamatan kerja. Semarang: Universitas Diponegoro, 2008. Available: https://kink.onesearch.id/Record/IOS2902.YOGYA000000000002375.

U. Sekaran, Research Methods For Business: Metodologi Penelitian Untuk Bisnis. Jakarta: Salemba Empat, 2011. Available: https://onesearch.id/Record/IOS3239.slims-77268.

S. G. Hart, “Nasa-Task Load Index (NASA-TLX); 20 Years Later,” Proc. Hum. Factors Ergon. Soc. Annu. Meet., vol. 50, no. 9, pp. 904–908, Oct. 2006, doi: https://doi.org/10.1177/154193120605000909.

A. Mital, Advances in Industrial Ergonomics and Safety: Proceedings of the Annual International Industrial Ergonomics and Safety Conference Held in Cincinnati. Taylor & Francis, 1989. Available: https://books.google.co.id/books?id=xuV4Bb7vsvkC.

T. E. Nygren, “Psychometric Properties of Subjective Workload Measurement Techniques: Implications for Their Use in the Assessment of Perceived Mental Workload,” Hum. Factors, vol. 33, no. 1, pp. 17–33, Feb. 1991, doi: https://doi.org/10.1177/001872089103300102.

M. R. Endsley, S. J. Selcon, T. D. Hardiman, and D. G. Croft, “A Comparative Analysis of Sagat and Sart for Evaluations of Situation Awareness,” Proc. Hum. Factors Ergon. Soc. Annu. Meet., vol. 42, no. 1, pp. 82–86, Oct. 1998, doi: https://doi.org/10.1177/154193129804200119.

C. M. Carswell et al., “Hands-free administration of subjective workload scales: Acceptability in a surgical training environment,” Appl. Ergon., vol. 42, no. 1, pp. 138–145, 2010, doi: https://doi.org/10.1016/j.apergo.2010.06.003.

S. Rubio, E. Díaz, J. Martín, and J. M. Puente, “Evaluation of Subjective Mental Workload: A Comparison of SWAT, NASA-TLX, and Workload Profile Methods,” Appl. Psychol., vol. 53, no. 1, pp. 61–86, Jan. 2004, doi: https://doi.org/10.1111/j.1464-0597.2004.00161.x.

Widyanti, A., Hasudungan, S., & Park, J. (2020). e-Learning readiness and perceived learning workload among students in an Indonesian university. Knowledge Management & E-Learning, 12(1), 18–29. doi: https://doi.org/10.34105/j.kmel.2020.12.002.

Calculation of mental load from e-learning student with NASA TLX and SOFI method

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