{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T15:29:37Z","timestamp":1759332577091,"version":"3.40.3"},"publisher-location":"Cham","reference-count":56,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030142728"},{"type":"electronic","value":"9783030142735"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-14273-5_6","type":"book-chapter","created":{"date-parts":[[2019,2,22]],"date-time":"2019-02-22T02:34:34Z","timestamp":1550802874000},"page":"92-111","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Analysing the Impact of Machine Learning to Model Subjective Mental Workload: A Case Study in Third-Level Education"],"prefix":"10.1007","author":[{"given":"Karim","family":"Moustafa","sequence":"first","affiliation":[]},{"given":"Luca","family":"Longo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,2,23]]},"reference":[{"key":"6_CR1","doi-asserted-by":"publisher","first-page":"359","DOI":"10.3389\/fnhum.2017.00359","volume":"11","author":"H Aghajani","year":"2017","unstructured":"Aghajani, H., Garbey, M., Omurtag, A.: Measuring mental workload with EEG+ fNIRS. Front. Hum. Neurosci. 11, 359 (2017)","journal-title":"Front. Hum. Neurosci."},{"issue":"251\u2013260","key":"6_CR2","first-page":"48","volume":"87","author":"GE Batista","year":"2002","unstructured":"Batista, G.E., Monard, M.C.: A study of K-nearest neighbour as an imputation method. HIS 87(251\u2013260), 48 (2002)","journal-title":"HIS"},{"issue":"2","key":"6_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/380995.380999","volume":"2","author":"Kristin P. Bennett","year":"2000","unstructured":"Bennett, K.P., Campbell, C.: Support vector machines. ACM SIGKDD Explor. Newsl. 2(2), 1\u201313 (2000). \n                    http:\/\/portal.acm.org\/citation.cfm?doid=380995.380999","journal-title":"ACM SIGKDD Explorations Newsletter"},{"key":"6_CR4","unstructured":"Cain, B.: A review of the mental workload literature. Technical report, Defence Research and Development Canada Toronto Human System Integration Section; 2007. Report Contract No. RTO-TRHFM-121-Part-II (2004)"},{"issue":"1","key":"6_CR5","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1177\/1094428110392383","volume":"15","author":"KD Carlson","year":"2012","unstructured":"Carlson, K.D., Herdman, A.O.: Understanding the impact of convergent validity on research results. Organ. Res. Methods 15(1), 17\u201332 (2012)","journal-title":"Organ. Res. Methods"},{"issue":"3","key":"6_CR6","doi-asserted-by":"publisher","first-page":"1247","DOI":"10.5194\/gmd-7-1247-2014","volume":"7","author":"T Chai","year":"2014","unstructured":"Chai, T., Draxler, R.R.: Root mean square error (RMSE) or mean absolute error (MAE)?-arguments against avoiding RMSE in the literature. Geosci. Model Dev. 7(3), 1247\u20131250 (2014)","journal-title":"Geosci. Model Dev."},{"key":"6_CR7","unstructured":"Chapman, P., Clinton, J., Khabaza, T., Reinartz, T., Wirth, R.: The crisp-dmprocess model. The CRIP\u2013DM Consortium 310 (1999)"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Cortes Torres, C.C., Sampei, K., Sato, M., Raskar, R., Miki, N.: Workload assessment with eye movement monitoring aided by non-invasive and unobtrusive micro-fabricated optical sensors. In: Adjunct Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology, pp. 53\u201354. ACM (2015)","DOI":"10.1145\/2815585.2817808"},{"key":"6_CR9","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1007\/978-3-319-61061-0_6","volume-title":"Human Mental Workload: Models and Applications","author":"J Fan","year":"2017","unstructured":"Fan, J., Smith, A.P.: The impact of workload and fatigue on performance. In: Longo, L., Leva, M.C. (eds.) H-WORKLOAD 2017. CCIS, vol. 726, pp. 90\u2013105. Springer, Cham (2017). \n                    https:\/\/doi.org\/10.1007\/978-3-319-61061-0_6"},{"issue":"4","key":"6_CR10","doi-asserted-by":"publisher","first-page":"1360","DOI":"10.1214\/08-AOAS191","volume":"2","author":"A Gelman","year":"2008","unstructured":"Gelman, A., Jakulin, A., Pittau, M.G., Su, Y.S.: A weakly informative default prior distribution for logistic and other regression models. Ann. Appl. Stat. 2(4), 1360\u20131383 (2008)","journal-title":"Ann. Appl. Stat."},{"issue":"1","key":"6_CR11","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","volume":"63","author":"P Geurts","year":"2006","unstructured":"Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3\u201342 (2006)","journal-title":"Mach. Learn."},{"key":"6_CR12","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-319-61061-0_1","volume-title":"Human Mental Workload: Models and Applications","author":"PA Hancock","year":"2017","unstructured":"Hancock, P.A.: Whither workload? Mapping a path for its future development. In: Longo, L., Leva, M.C. (eds.) H-WORKLOAD 2017. CCIS, vol. 726, pp. 3\u201317. Springer, Cham (2017). \n                    https:\/\/doi.org\/10.1007\/978-3-319-61061-0_1"},{"key":"6_CR13","volume-title":"Human Mental Workload","author":"PA Hancock","year":"1988","unstructured":"Hancock, P.A., Meshkati, N.: Human Mental Workload. Elsevier, Amsterdam (1988)"},{"issue":"C","key":"6_CR14","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/S0166-4115(08)62386-9","volume":"52","author":"SG Hart","year":"1988","unstructured":"Hart, S.G., Staveland, L.E.: Development of NASA-TLX (task load index): results of empirical and theoretical research. Adv. Psychol. 52(C), 139\u2013183 (1988)","journal-title":"Adv. Psychol."},{"issue":"9","key":"6_CR15","doi-asserted-by":"publisher","first-page":"904","DOI":"10.1177\/154193120605000909","volume":"50","author":"Sandra G. Hart","year":"2006","unstructured":"Hart, S.G.: NASA-task load index (NASA-TLX); 20 years later. In: Human Factors and Ergonomics Society Annual Meting, pp. 904\u2013908 (2006)","journal-title":"Proceedings of the Human Factors and Ergonomics Society Annual Meeting"},{"key":"6_CR16","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1007\/978-3-319-39955-3_19","volume-title":"Foundations of Augmented Cognition: Neuroergonomics and Operational Neuroscience","author":"SW Hincks","year":"2016","unstructured":"Hincks, S.W., Afergan, D., Jacob, R.J.K.: Using fNIRS for real-time cognitive workload assessment. In: Schmorrow, D.D.D., Fidopiastis, C.M.M. (eds.) AC 2016. LNCS (LNAI), vol. 9743, pp. 198\u2013208. Springer, Cham (2016). \n                    https:\/\/doi.org\/10.1007\/978-3-319-39955-3_19"},{"key":"6_CR17","unstructured":"Jonsson, P., Wohlin, C.: An evaluation of k-nearest neighbour imputation using Likert data. In: 2004 Proceedings of 10th International Symposium on Software Metrics, pp. 108\u2013118, September 2004"},{"key":"6_CR18","unstructured":"Kotsiantis, S.B.: Supervised machine learning: a review of classification techniques. Informatica 31(2), 249\u2013268 (2007). \n                    https:\/\/books.google.co.in\/books?hl=en&lr=&id=vLiTXDHr_sYC&oi=fnd&pg=PA3&dq=survey+machine+learning&ots=CVsyuwYHjo&redir_esc=y#v=onepage&q=survey%20machine%20learning&f=false"},{"issue":"4","key":"6_CR19","first-page":"279","volume":"39","author":"TO Kv\u00e5lseth","year":"1985","unstructured":"Kv\u00e5lseth, T.O.: Cautionary note about R\n                    \n                      \n                    \n                    $$^2$$\n                    \n                      \n                        \n                          \n                          2\n                        \n                      \n                    \n                  . Am. Stat. 39(4), 279\u2013285 (1985)","journal-title":"Am. Stat."},{"key":"6_CR20","doi-asserted-by":"publisher","first-page":"389","DOI":"10.3389\/fnhum.2017.00389","volume":"11","author":"Y Liu","year":"2017","unstructured":"Liu, Y., Ayaz, H., Shewokis, P.A.: Multisubject \u201clearning\u201d for mental workload classification using concurrent EEG, fNIRS, and physiological measures. Front. Hum. Neurosci. 11, 389 (2017)","journal-title":"Front. Hum. Neurosci."},{"key":"6_CR21","unstructured":"Longo, L.: Formalising human mental workload as a defeasible computational concept. Ph.D. thesis, Trinity College, Dublin (2014)"},{"issue":"8","key":"6_CR22","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1080\/0144929X.2015.1015166","volume":"34","author":"L Longo","year":"2015","unstructured":"Longo, L.: A defeasible reasoning framework for human mental workload representation and assessment. Behav. Inf. Technol. 34(8), 758\u2013786 (2015)","journal-title":"Behav. Inf. Technol."},{"key":"6_CR23","doi-asserted-by":"crossref","unstructured":"Longo, L.: Designing medical interactive systems via assessment of human mental workload. In: International Symposium on Computer-Based Medical Systems, pp. 364\u2013365 (2015)","DOI":"10.1109\/CBMS.2015.67"},{"key":"6_CR24","doi-asserted-by":"crossref","unstructured":"Longo, L.: Mental workload in medicine: foundations, applications, open problems, challenges and future perspectives. In: 2016 IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS), pp. 106\u2013111. IEEE (2016)","DOI":"10.1109\/CBMS.2016.36"},{"key":"6_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1007\/978-3-319-67684-5_13","volume-title":"Human-Computer Interaction - INTERACT 2017","author":"L Longo","year":"2017","unstructured":"Longo, L.: Subjective usability, mental workload assessments and their impact on objective human performance. In: Bernhaupt, R., Dalvi, G., Joshi, A., Balkrishan, D.K., O\u2019Neill, J., Winckler, M. (eds.) INTERACT 2017. LNCS, vol. 10514, pp. 202\u2013223. Springer, Cham (2017). \n                    https:\/\/doi.org\/10.1007\/978-3-319-67684-5_13"},{"issue":"8","key":"6_CR26","doi-asserted-by":"publisher","first-page":"e0199661","DOI":"10.1371\/journal.pone.0199661","volume":"13","author":"Luca Longo","year":"2018","unstructured":"Longo, L.: Experienced mental workload, perception of usability, their interaction and impact on task performance. PloS ONE 13(8), 1\u201336 (2018). \n                    https:\/\/doi.org\/10.1371\/journal.pone.0199661","journal-title":"PLOS ONE"},{"key":"6_CR27","doi-asserted-by":"publisher","unstructured":"Longo, L.: On the reliability, validity and sensitivity of three mental workload assessment techniques for the evaluation of instructional designs: a case study in a third-level course. In: Proceedings of the 10th International Conference on Computer Supported Education, CSEDU 2018, Funchal, Madeira, Portugal, 15\u201317 March 2018, vol. 2, pp. 166\u2013178 (2018). \n                    https:\/\/doi.org\/10.5220\/0006801801660178","DOI":"10.5220\/0006801801660178"},{"key":"6_CR28","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/978-3-642-15314-3_6","volume-title":"Brain Informatics","author":"L Longo","year":"2010","unstructured":"Longo, L., Barrett, S.: Cognitive effort for multi-agent systems. In: Yao, Y., Sun, R., Poggio, T., Liu, J., Zhong, N., Huang, J. (eds.) BI 2010. LNCS (LNAI), vol. 6334, pp. 55\u201366. Springer, Heidelberg (2010). \n                    https:\/\/doi.org\/10.1007\/978-3-642-15314-3_6"},{"key":"6_CR29","doi-asserted-by":"crossref","unstructured":"Longo, L., Dondio, P.: On the relationship between perception of usability and subjective mental workload of web interfaces. In: 2015 IEEE\/WIC\/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 1, pp. 345\u2013352. IEEE (2015)","DOI":"10.1109\/WI-IAT.2015.157"},{"key":"6_CR30","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-61061-0","volume-title":"Human Mental Workload: Models and Applications","year":"2017","unstructured":"Longo, L., Leva, M.C. (eds.): H-WORKLOAD 2017. CCIS, vol. 726. Springer, Cham (2017). \n                    https:\/\/doi.org\/10.1007\/978-3-319-61061-0"},{"key":"6_CR31","unstructured":"Longo, L., Rusconi, F., Noce, L., Barrett, S.: The importance of human mental workload in web-design. In: 8th International Conference on Web Information Systems and Technologies, pp. 403\u2013409, April 2012"},{"key":"6_CR32","doi-asserted-by":"crossref","unstructured":"Mannaru, P., Balasingam, B., Pattipati, K., Sibley, C., Coyne, J.: Cognitive context detection in UAS operators using eye-gaze patterns on computer screens. In: Next-Generation Analyst IV, vol. 9851, p. 98510F. International Society for Optics and Photonics (2016)","DOI":"10.1117\/12.2224184"},{"key":"6_CR33","doi-asserted-by":"crossref","unstructured":"Mayer, R.E.: Cognitive theory of multimedia learning, 2nd edn. In: Cambridge Handbooks in Psychology, pp. 43\u201371. Cambridge University Press, Cambridge (2014)","DOI":"10.1017\/CBO9781139547369.005"},{"key":"6_CR34","unstructured":"Meshkati, N., Loewenthal, A.: An eclectic and critical review of four primary mental workload assessment methods: a guide for developing a comprehensive model. Adv. Psychol. 52(1978), 251\u2013267 (1988). \n                    http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0166411508623912"},{"key":"6_CR35","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-319-61061-0_14","volume-title":"Human Mental Workload: Models and Applications","author":"P Mijovi\u0107","year":"2017","unstructured":"Mijovi\u0107, P., Milovanovi\u0107, M., Kovi\u0107, V., Gligorijevi\u0107, I., Mijovi\u0107, B., Ma\u010du\u017ei\u0107, I.: Neuroergonomics method for measuring the influence of mental workload modulation on cognitive state of manual assembly worker. In: Longo, L., Leva, M.C. (eds.) H-WORKLOAD 2017. CCIS, vol. 726, pp. 213\u2013224. Springer, Cham (2017). \n                    https:\/\/doi.org\/10.1007\/978-3-319-61061-0_14"},{"issue":"4","key":"6_CR36","doi-asserted-by":"publisher","first-page":"280","DOI":"10.15171\/hpp.2015.033","volume":"5","author":"Mohsen Mohammadi","year":"2016","unstructured":"Mohammadi, M., Mazloumi, A., Kazemi, Z., Zeraati, H.: Evaluation of mental workload among ICU ward\u2019s nurses. Health Promot. Perspect. 5(4), 280\u20137 (2015). \n                    http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/26933647\n                    \n                  , \n                    http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?artid=PMC4772798","journal-title":"Health Promotion Perspectives"},{"key":"6_CR37","doi-asserted-by":"crossref","unstructured":"Monfort, S.S., Sibley, C.M., Coyne, J.T.: Using machine learning and real-time workload assessment in a high-fidelity UAV simulation environment. In: Next-Generation Analyst IV, vol. 9851, p. 98510B. International Society for Optics and Photonics (2016)","DOI":"10.1117\/12.2219703"},{"key":"6_CR38","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1007\/978-3-319-61061-0_3","volume-title":"Human Mental Workload: Models and Applications","author":"K Moustafa","year":"2017","unstructured":"Moustafa, K., Luz, S., Longo, L.: Assessment of mental workload: a comparison of machine learning methods and subjective assessment techniques. In: Longo, L., Leva, M.C. (eds.) H-WORKLOAD 2017. CCIS, vol. 726, pp. 30\u201350. Springer, Cham (2017). \n                    https:\/\/doi.org\/10.1007\/978-3-319-61061-0_3"},{"issue":"4","key":"6_CR39","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1111\/j.1745-3984.1985.tb01065.x","volume":"22","author":"B Nevo","year":"1985","unstructured":"Nevo, B.: Face validity revisited. J. Educ. Meas. 22(4), 287\u2013293 (1985)","journal-title":"J. Educ. Meas."},{"key":"6_CR40","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1007\/978-3-319-40548-3_38","volume-title":"HCI International 2016 \u2013 Posters\u2019 Extended Abstracts","author":"T Ott","year":"2016","unstructured":"Ott, T., Wu, P., Paullada, A., Mayer, D., Gottlieb, J., Wall, P.: ATHENA \u2013 a zero-intrusion no contact method for workload detection using linguistics, keyboard dynamics, and computer vision. In: Stephanidis, C. (ed.) HCI 2016. CCIS, vol. 617, pp. 226\u2013231. Springer, Cham (2016). \n                    https:\/\/doi.org\/10.1007\/978-3-319-40548-3_38"},{"key":"6_CR41","series-title":"Smart Innovation, Systems and Technologies","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/978-3-319-33747-0_28","volume-title":"Advances in Neural Networks","author":"TT Pham","year":"2016","unstructured":"Pham, T.T., Nguyen, T.D., Van Vo, T.: Sparse fNIRS feature estimation via unsupervised learning for mental workload classification. In: Bassis, S., Esposito, A., Morabito, F.C., Pasero, E. (eds.) Advances in Neural Networks. SIST, vol. 54, pp. 283\u2013292. Springer, Cham (2016). \n                    https:\/\/doi.org\/10.1007\/978-3-319-33747-0_28"},{"key":"6_CR42","doi-asserted-by":"crossref","unstructured":"Reid, G.B., Nygren, T.E.: The subjective workload assessment technique: a scaling procedure for measuring mental workload. In: Advances in Psychology, vol. 52, pp. 185\u2013218. Elsevier (1988)","DOI":"10.1016\/S0166-4115(08)62387-0"},{"key":"6_CR43","series-title":"IFIP Advances in Information and Communication Technology","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1007\/978-3-319-44944-9_19","volume-title":"Artificial Intelligence Applications and Innovations","author":"L Rizzo","year":"2016","unstructured":"Rizzo, L., Dondio, P., Delany, S.J., Longo, L.: Modeling mental workload via rule-based expert system: a comparison with NASA-TLX and workload profile. In: Iliadis, L., Maglogiannis, I. (eds.) AIAI 2016. IAICT, vol. 475, pp. 215\u2013229. Springer, Cham (2016). \n                    https:\/\/doi.org\/10.1007\/978-3-319-44944-9_19"},{"key":"6_CR44","unstructured":"Rizzo, L., Longo, L.: Representing and inferring mental workload via defeasible reasoning: a comparison with the NASA task load index and the workload profile. In: Proceedings of the 1st Workshop on Advances In Argumentation In Artificial Intelligence Co-located with XVI International Conference of the Italian Association for Artificial Intelligence (AI * IA 2017), Bari, Italy, 16\u201317 November 2017, pp. 126\u2013140 (2017)"},{"key":"6_CR45","unstructured":"Rizzo, L., Longo, L.: Inferential models of mental workload with defeasible argumentation and non-monotonic fuzzy reasoning: a comparative study. In: Proceedings of the 2nd Workshop on Advances In Argumentation In Artificial Intelligence Co-located with XVII International Conference of the Italian Association for Artificial Intelligence (AI*IA 2018), Trento, Italy, 20\u201323 November 2018, pp. 11\u201326 (2018)"},{"issue":"1","key":"6_CR46","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1111\/j.1464-0597.2004.00161.x","volume":"53","author":"S Rubio","year":"2004","unstructured":"Rubio, S., D\u00edaz, E., Mart\u00edn, J., Puente, J.M.: Evaluation of subjective mental workload: a comparison of swat, NASA-TLX, and workload profile methods. Appl. Psychol. 53(1), 61\u201386 (2004). \n                    https:\/\/doi.org\/10.1111\/j.1464-0597.2004.00161.x","journal-title":"Appl. Psychol."},{"key":"6_CR47","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1007\/978-3-319-61061-0_17","volume-title":"Human Mental Workload: Models and Applications","author":"AP Smith","year":"2017","unstructured":"Smith, A.P., Smith, H.N.: Workload, fatigue and performance in the rail industry. In: Longo, L., Leva, M.C. (eds.) H-WORKLOAD 2017. CCIS, vol. 726, pp. 251\u2013263. Springer, Cham (2017). \n                    https:\/\/doi.org\/10.1007\/978-3-319-61061-0_17"},{"key":"6_CR48","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/978-3-319-61061-0_5","volume-title":"Human Mental Workload: Models and Applications","author":"KT Smith","year":"2017","unstructured":"Smith, K.T.: Observations and issues in the application of cognitive workload modelling for decision making in complex time-critical environments. In: Longo, L., Leva, M.C. (eds.) H-WORKLOAD 2017. CCIS, vol. 726, pp. 77\u201389. Springer, Cham (2017). \n                    https:\/\/doi.org\/10.1007\/978-3-319-61061-0_5"},{"key":"6_CR49","series-title":"Smart Innovation, Systems and Technologies","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1007\/978-3-319-28109-4_26","volume-title":"Recent Advances in Nonlinear Speech Processing","author":"J Su","year":"2016","unstructured":"Su, J., Luz, S.: Predicting cognitive load levels from speech data. In: Esposito, A., et al. (eds.) Recent Advances in Nonlinear Speech Processing. SIST, vol. 48, pp. 255\u2013263. Springer, Cham (2016). \n                    https:\/\/doi.org\/10.1007\/978-3-319-28109-4_26"},{"issue":"3","key":"6_CR50","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1080\/00140139608964470","volume":"39","author":"PS Tsang","year":"1996","unstructured":"Tsang, P.S., Velazquez, V.L.: Diagnosticity and multidimensional subjective workload ratings. Ergonomics 39(3), 358\u2013381 (1996)","journal-title":"Ergonomics"},{"key":"6_CR51","unstructured":"Walter, C., Cierniak, G., Gerjets, P., Rosenstiel, W., Bogdan, M.: Classifying mental states with machine learning algorithms using alpha activity decline. In: 2011 Proceedings of 19th European Symposium on Artificial Neural Networks, ESANN 2011, Bruges, Belgium, April 27\u201329 (2011). \n                    https:\/\/www.elen.ucl.ac.be\/Proceedings\/esann\/esannpdf\/es2011-35.pdf"},{"issue":"3","key":"6_CR52","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1518\/001872008X288394","volume":"50","author":"CD Wickens","year":"2008","unstructured":"Wickens, C.D.: Multiple resources and mental workload. Hum. Factors 50(3), 449\u2013455 (2008)","journal-title":"Hum. Factors"},{"key":"6_CR53","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1007\/978-3-319-61061-0_2","volume-title":"Human Mental Workload: Models and Applications","author":"CD Wickens","year":"2017","unstructured":"Wickens, C.D.: Mental workload: assessment, prediction and consequences. In: Longo, L., Leva, M.C. (eds.) H-WORKLOAD 2017. CCIS, vol. 726, pp. 18\u201329. Springer, Cham (2017). \n                    https:\/\/doi.org\/10.1007\/978-3-319-61061-0_2"},{"key":"6_CR54","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/978-1-4471-0123-9_3","volume-title":"Soft Computing and Industry","author":"DH Wolpert","year":"2002","unstructured":"Wolpert, D.H.: The supervised learning no-free-lunch theorems. In: Roy, R., K\u00f6ppen, M., Ovaska, S., Furuhashi, T., Hoffmann, F. (eds.) Soft Computing and Industry, pp. 25\u201342. Springer, London (2002). \n                    https:\/\/doi.org\/10.1007\/978-1-4471-0123-9_3"},{"issue":"3","key":"6_CR55","doi-asserted-by":"publisher","first-page":"210","DOI":"10.7763\/IJMLC.2014.V4.414","volume":"4","author":"Y Yoshida","year":"2014","unstructured":"Yoshida, Y., Ohwada, H., Mizoguchi, F., Iwasaki, H.: Classifying cognitive load and driving situation with machine learning. Int. J. Mach. Learn. Comput. 4(3), 210\u2013215 (2014)","journal-title":"Int. J. Mach. Learn. Comput."},{"issue":"1","key":"6_CR56","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/00140139.2014.956151","volume":"58","author":"MS Young","year":"2015","unstructured":"Young, M.S., Brookhuis, K.A., Wickens, C.D., Hancock, P.A.: State of science: mental workload in ergonomics. Ergonomics 58(1), 1\u201317 (2015)","journal-title":"Ergonomics"}],"container-title":["Communications in Computer and Information Science","Human Mental Workload: Models and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-14273-5_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,12,20]],"date-time":"2019-12-20T10:14:07Z","timestamp":1576836847000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-14273-5_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030142728","9783030142735"],"references-count":56,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-14273-5_6","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"23 February 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"H-WORKLOAD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Human Mental Workload: Models and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Amsterdam","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The Netherlands","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hworkload2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.hworkload.org\/2018","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Springer","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"31","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"15","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"48% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}