Abstract
Smartphone is nowadays the most prevalent computer system, thus a lot of attention from academia and industries has been put to evaluate its quality of use. However, Smartphone has more complex interaction modes and usage scenarios than PC and laptop. And therefore assessing its quality using a conventional usability evaluation is not sufficient. Meanwhile, the mental load serves as an acknowledged index of effort that operators have put in human-machine interaction, especially under high-demanding context. Mental load contains a set of parameters in multiple dimensions, such as primitive task performance, biological measurement(s) and subjective mental load scale, which assesses the efforts of tasks under a particular environment and operating conditions. Thus, it is suitable for evaluating complex mental work, and may indicate the use of Smartphones.
The aim of this paper is to apply a multi-dimensional method to assess the mental load of users, and find out which measurement(s) is the most suitable one to evaluate the efforts for using a smartphone. During this study, the effort on conducting tasks with four difficulty levels were assessed using measurements in three dimensions, which were (1) user performance (task accomplishment and secondary task), (2) subjective rating (NASA-TLX scale) and (3) physiological function (EDA). The values of these measurements were compared across novice, average and skilled users. The results show that: task duration and number of usability error are significantly related with mental load and change with the difficulty level of tasks; in subjective rating, Mental Demand, Effort and Frustration were highly related with mental load.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
ISO (2001) ISO/IEC 9126-1:2001 Software engineering – Product quality – Part 1: Quality model. International Standard. International Organization for Standardization, Switzerland
ISO: ISO9241-11(1998) 1998 Ergonomic requirements for office work with visual display terminals (VDT’s) – Part 11: Guidance on usability. International Standard, International Organization for Standardization, Switzerland
Li LS (1999) Action theory and cognitive psychology in industrial design: User models and user interfaces. Dissertation, Art University of Braunschweig, Braunschweig
Kantowitz BH (1987) Mental Workload. In: Hancock PA. (ed) Advances in Psychology, vol 47, pp 81–121
Liao JQ (1995) Mental workload and its measurement. J Syst Eng 10(3):119–123
Kang WY, Yuan XG, Liu ZQ, Liu W (2008) Synthetic Evaluation method of mental workload on visual display interface in airplane cockpit. Space Med Med Eng 21(2):103–107
Li L, Yuan M (2011) Influential factors analysis of drivers’ mental workload with the use of vehicle navigation system. J Saf Environ 11(6):202–204
Cui K, Sun LY, Feng TW, Xing X (2008) New developments in measurement methodologies of mental workload. Industr Eng J 11(5):1–5
Cooper GE, Harper RP, Jr (1969) The Use of Pilot Rating in the Evaluation of Aircraft Handling Qualities. Report No NASA TN-D-5153. Technical Report, Ames Research Center, National Aeronautics and Space Administration. Moffett Field
Hart SG (2006) NASA-task load index (NASA-TLX); 20 years later. In: Proceedings of the human factors and ergonomics society 50th annual meeting, vol 50. Sage Publications, Los Angeles, pp 904–908
Reid GB, Nygren TE (1988) The subjective workload assessment technique: a scaling procedure for measuring mental workload. Adv Psychol 52:185–218 Elsevier Science Publishers, North Holland
Boles DB, Bursk JH, Phillips JB, Perdelwitz JR (2007) Predicting dual-task performance with the multiple resources questionnaire (MRQ). Hum Factors 49(1):32–45
Galy E, Cariou M, Mélan C (2011) What is the relationship between mental workload factors and cognitive load types? Int J Psychophysiol 83(3):269–275
Shingledecker CA, Crabtree MS, Simons JC et al (1980) Subsidiary Radio Communications Tasks for Workload Assessment in R&D Simulations I. Task Development and Workload Scaling. Technical Report, Systems Research Labs Inc, Dayton Ohio
Horst RL, Johnson R, Donchin E (1980) Event-related brain potentials and subjective probability in a learning task. Mem Cognit 8(5):476–488
Ahlstrom U, Friedman-Berg FJ (2006) Using eye movement activity as a correlate of cognitive workload. Int J Industr Ergon 36(7):623–636
Gunn CG, Wolf S, Block RT et al (1972) Psychophysiology of the cardiovascular system. In: Greenfield NS, Sternbach RA (eds) Handbook of psychophysiology. Holt, Rinehart & Winston, New York, pp 457–483
Suzuki S, Kumano H, Sakano Y (2003) Effects of effort and distress coping processes on psychophysiological and psychological stress responses. Int J Psychophysiol 47(2):117–128
Reinhardt T, Schmahl C, Wüst S, Bohus M (2012) Salivary cortisol, heart rate, electrodermal activity and subjective stress responses to the mannheim multicomponent stress test (MMST). Psychiatry Res 198(1):106–111
Moya-Albiol L, Sanchis-Calatayud MV, Sariñana-González P, De Andrés-García S, Romero-Martínez Á, González-Bono E (2012) P03-425 - Electrodermal activity in response to a set of mental tasks in caregivers of persons with autism spectrum disorders. Eur Psychiatry 26(1):1595
Affectiva (2012) Liberate yourself from the lab: Q Sensor measures EDA in the wild. Affectiva QTM Solutions White Paper
Wang J, Fang WN, Li GY (2010) Mental workload evaluation method based on multi-resource theory model. J. Beijing Jiaotong Univ. 34(6):107–110
Peng XW, He QC, Ji T, Wang ZL, Yang L (2006) Mental workload for mental arithmetic on visual display terminal. Chin J Industr Hyg Occup Dis 24(12):726–729
Li JB, Xu BH (2009) synthetic assessment of cognitive load in human-machine interaction process. Acta Psychologica Sinica 41(1):35–43
Yu YH, Li ZJ (2011) Study of sonically enhanced menu interaction for mobile terminals. Appl Res Comput 28(10):3742–3745
Jimenez-Molina A, Retamal C, Lira H (2018) Using psychophysiological sensors to assess mental workload during web browsing. Sensors 18(2):458
Li M (2008) Comparison of Usability Evaluation Method Based-on Needs of Software Development. Master Thesis, Xi’an Jiaotong University, Xi’an
O’Donnell RD, Eggemeier FT (1986) Workload assessment methodology. In: Boff KR, Kaufman L, Thomas JP (eds) Handbook of perception and human performance, vol II. Wiley, New York, pp 42–43
Galy E, Cariou M, Mélan C (2012) What is the relationship between mental workload factors and cognitive load types? Int J Psychophysiol 83(3):269–275
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, M., Albayrak, A., Zhang, Y., van Eijk, D. (2019). Multiple Factors Mental Load Evaluation on Smartphone User Interface. In: Bagnara, S., Tartaglia, R., Albolino, S., Alexander, T., Fujita, Y. (eds) Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018). IEA 2018. Advances in Intelligent Systems and Computing, vol 827. Springer, Cham. https://doi.org/10.1007/978-3-319-96059-3_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-96059-3_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-96058-6
Online ISBN: 978-3-319-96059-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)