Prediction of Cognitive Load during Industry-Academia Collaboration via a Web Platform Anthony Yusuf Virginia Tech/Myers Lawson School of Construction, United States Abiola Akanmu Virginia Tech/Myers Lawson School of Construction, United States Adedeji Afolabi Virginia Tech/Myers Lawson School of Construction, United States Homero Murzi Virginia Tech, United States This is a section of CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality (DOI: 10.36253/979-12-215-0289-3) by Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi Firenze University Press Florence 2023 https://doi.org/10.36253/10.36253/979-12-215-0289-3.06

Available for academic research purposes

Open Access

Copyright Author(s)

Content licence CC BY-NC 4.0

Metadata licence CC0 1.0

This is original content, published for academic research purposes

 Digital edition XML powered by Booksflow

Web platforms are increasingly being used to connect communities, including construction industry and academia. Design features of such platforms could impose excessive cognitive workload thereby impacting the use of the platform. This is a crucial consideration especially for new web platforms to secure users’ interest in continuous usage. Understanding users’ cognitive workloads while using web platforms could help make necessary modifications and adapt the features to users’ preferences. Users’ usage patterns can be leveraged to predict the needs of users. Hence, the pattern of cognitive demand that users experience can be used to predict the cognitive load of web platform users. This could provide insights, generate feedback, and identify areas of modification that are critical for sustaining acceptability of web platforms. Using recurrent neural network, this study adopts electroencephalogram (EEG) data as a physiological measure of brain activity to predict brain signals (cognitive load) of users while interacting with a web platform designed to connect industry and academia for future workforce development. This paper presents a Long Short-Term Memory (LSTM) based approach to develop a model for predicting users’ cognitive load via EEG signals. Nineteen (19) potential end-users of the proposed web platform were recruited as participants in this study. The participants interacted with the web-platform in a real case scenario and their brain signals were captured using a five-channel EEG device. The validity of the proposed method was evaluated using root mean square error (RMSE), coefficient of determination (R2), and comparison of the predicted and actual EEG signals and mental workload. The results revealed the reliability of the model and provided a suitable method for predicting users brain signals while using web platforms. This could be leveraged to understand users’ cognitive demand which could provide insights for web platform improvements to engender users’ continuous usage

 Cognitive load electroencephalogram industry-academia collaboration long short-term memory web platform

It is available online at https://doi.org/10.36253/10.36253/979-12-215-0289-3.06

References Adomavicius, G., & Tuzhilin, A. (2005). Personalization technologies: a process-oriented perspective. Communications of the ACM, 48(10), 83-90. Retrieved from 10.1145/1089107.1089109 Albers, M. J. (2011). Tapping as a measure of cognitive load and website usability. Paper presented at the Proceedings of the 29th ACM international conference on Design of communication. 10.1145/2038476.2038481 Alexander, D. L., Tropsha, A., & Winkler, D. A. (2015). Beware of R 2: simple, unambiguous assessment of the prediction accuracy of QSAR and QSPR models. Journal of chemical information and modeling, 55(7), 1316-1322. 10.1021/acs.jcim.5b00206 Appel, T., Sevcenko, N., Wortha, F., Tsarava, K., Moeller, K., Ninaus, M., . . . Gerjets, P. (2019). Predicting cognitive load in an emergency simulation based on behavioral and physiological measures. Paper presented at the 2019 International Conference on Multimodal Interaction. 10.1145/3340555.3353735 Caldiroli, C. L., Gasparini, F., Corchs, S., Mangiatordi, A., Garbo, R., Antonietti, A., & Mantovani, F. (2023). Comparing online cognitive load on mobile versus PC-based devices. Personal and Ubiquitous Computing, 27(2), 495-505. 10.1007/s00779-022-01707-8 Chandrasekaran, S., Littlefair, G., & Stojcevski, A. (2015). Staff and Students Views on Industry-University Collaboration in Engineering. International journal of advanced corporate learning, 8(2). 10.3991/ijac.v8i2.4408. Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, 1-24. 10.7717/peerj-cs.623 Coulibaly, P., & Baldwin, C. K. (2005). Nonstationary hydrological time series forecasting using nonlinear dynamic methods. Journal of Hydrology, 307(1-4), 164-174. 10.1016/j.jhydrol.2004.10.008 Dale, M., Basumatary, P., Iqbal, J., Khullar, R., & Shaikh, M. (2022). Case study of using Facebook groups to connect community users to archived CoRSAL content. Language Documentation and Conservation, 16, 399-416. 10.1016/j.jneumeth.2003.10.009 Das, D., Chatterjee, D., & Sinha, A. (2013). Unsupervised approach for measurement of cognitive load using EEG signals. Paper presented at the 13th IEEE International Conference on BioInformatics and BioEngineering. 10.1109/BIBE.2013.6701686 Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Massachusetts Institute of Technology, Retrieved from https://dspace.mit.edu/bitstream/handle/1721.1/15192/14927137-MIT.pdf (Ph.D. in Management) Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of neuroscience methods, 134(1), 9-21. 10.1016/j.jneumeth.2003.10.009 Desai, D. (2021). Website personalization: Strategy for user experience design & development. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(12), 3516-3523. Retrieved from https://www.turcomat.org/index.php/turkbilmat/article/view/8092/6330. Ettinger, S., Cheng, S., Caine, B., Liu, C., Zhao, H., Pradhan, S., . . . Zhou, Y. (2021). Large scale interactive motion forecasting for autonomous driving: The waymo open motion dataset. Paper presented at the Proceedings of the IEEE/CVF International Conference on Computer Vision. Retrieved from http://openaccess.thecvf.com/content/ICCV2021/papers/Ettinger_Large_Scale_Interactive_Motion_Forecasting_for_Autonomous_Driving_The_Waymo_ICCV_2021_paper.pdf. Freire, L. L., Arezes, P. M., & Campos, J. C. (2012). A literature review about usability evaluation methods for e-learning platforms. Work, 41(Supplement 1), 1038-1044. 10.3233/WOR-2012-0281-1038 Friedman, N., Fekete, T., Gal, K., & Shriki, O. (2019). EEG-based prediction of cognitive load in intelligence tests. Frontiers in human neuroscience, 13, 1-9. 10.3389/fnhum.2019.00191 Gevins, A., Smith, M. E., McEvoy, L., & Yu, D. (1997). High-resolution EEG mapping of cortical activation related to working memory: effects of task difficulty, type of processing, and practice. Cerebral cortex (New York, NY: 1991), 7(4), 374-385. 10.1093/cercor/7.4.374 Herbig, N., Düwel, T., Helali, M., Eckhart, L., Schuck, P., Choudhury, S., & Krüger, A. (2020). Investigating multi-modal measures for cognitive load detection in e-learning. Paper presented at the Proceedings of the 28th ACM conference on user modeling, adaptation and personalization. 10.1145/3340631.3394861 Hewitt, D. H., & He, Y. (2022). Cognitive Load and Website Usability: Effects of Contrast and Task Difficulty. Paper presented at the Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 10.1177/1071181322661051 Hu, P. J.-H., Hu, H.-f., & Fang, X. (2017). Examining the mediating roles of cognitive load and performance outcomes in user satisfaction with a website. MIS Quarterly, 41(3), 975-A911. Retrieved from https://www.jstor.org/stable/10.2307/26635022. Hua, Y., Zhao, Z., Li, R., Chen, X., Liu, Z., & Zhang, H. (2019). Deep learning with long short-term memory for time series prediction. IEEE Communications Magazine, 57(6), 114-119. 10.1109/MCOM.2019.1800155 Jebelli, H., Hwang, S., & Lee, S. (2018). EEG signal-processing framework to obtain high-quality brain waves from an off-the-shelf wearable EEG device. Journal of Computing in Civil Engineering, 32(1), 04017070. 10.1061/(ASCE)CP.1943-5487.0000719 Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. 10.48550/arXiv.1412.6980 Kumar, N., & Kumar, J. (2016). Measurement of cognitive load in HCI systems using EEG power spectrum: an experimental study. Procedia Computer Science, 84, 70-78. 10.1016/j.procs.2016.04.068 Lin, F.-R., & Kao, C.-M. (2018). Mental effort detection using EEG data in E-learning contexts. Computers & Education, 122, 63-79. 10.1016/j.compedu.2018.03.020 Lin, H.-F. (2009). Examination of cognitive absorption influencing the intention to use a virtual community. Behaviour & Information Technology, 28(5), 421-431. 10.1080/01449290701662169 Maher, J. M., Oropello, V., Roman, D. M. S., & Zeoli, P. (2022). Expanding broadband internet access to connect underserved communities, increase health acess, and improve health outcomes in Cleveland county, North Carolina. Capstone Project. 10.17615/dbv7-p615 Mills, C., Fridman, I., Soussou, W., Waghray, D., Olney, A. M., & D'Mello, S. K. (2017). Put your thinking cap on: detecting cognitive load using EEG during learning. Paper presented at the Proceedings of the seventh international learning analytics & knowledge conference. 10.1145/3027385.3027431 Miyamoto, K., Tanaka, H., & Nakamura, S. (2022). Online EEG-based emotion prediction and music generation for inducing affective states. IEICE TRANSACTIONS on Information and Systems, 105(5), 1050-1063. 10.1587/transinf.2021EDP7171 Moghar, A., & Hamiche, M. (2020). Stock market prediction using LSTM recurrent neural network. Procedia Computer Science, 170, 1168-1173. 10.1016/j.procs.2020.03.049 Nielsen, J. (1994). Enhancing the explanatory power of usability heuristics. Paper presented at the Proceedings of the SIGCHI conference on Human Factors in Computing Systems. Retrieved from 10.1145/191666.191729 Pyo, J. C., Ligaray, M., Kwon, Y. S., Ahn, M.-H., Kim, K., Lee, H., . . . Cho, K. H. (2018). High-spatial resolution monitoring of phycocyanin and chlorophyll-a using airborne hyperspectral imagery. Remote Sensing, 10(8), 1180. 10.3390/rs10081180 Qin, Y., & Bulbul, T. (2023). Electroencephalogram-based mental workload prediction for using Augmented Reality head mounted display in construction assembly: A deep learning approach. Automation in Construction, 152, 104892. 10.1016/j.autcon.2023.104892 Salman, A. G., Heryadi, Y., Abdurahman, E., & Suparta, W. (2018). Single layer & multi-layer long short-term memory (LSTM) model with intermediate variables for weather forecasting. Procedia Computer Science, 135, 89-98. 10.1016/j.procs.2018.08.153 Schmutz, P., Heinz, S., Métrailler, Y., & Opwis, K. (2009). Cognitive load in eCommerce applications—measurement and effects on user satisfaction. Advances in Human-Computer Interaction, 2009. 10.1155/2009/121494 Schmutz, P., Roth, S. P., Seckler, M., & Opwis, K. (2010). Designing product listing pages—Effects on sales and users’ cognitive workload. International journal of human-computer studies, 68(7), 423-431. 10.1016/j.ijhcs.2010.02.001 Seufert, T., Jänen, I., & Brünken, R. (2007). The impact of intrinsic cognitive load on the effectiveness of graphical help for coherence formation. Computers in Human Behavior, 23(3), 1055-1071. 10.1016/j.chb.2006.10.002 Staudemeyer, R. C., & Morris, E. R. (2019). Understanding LSTM--a tutorial into long short-term memory recurrent neural networks. arXiv preprint arXiv:1909.09586. 10.48550/arXiv.1909.09586 Tracy, J. P., & Albers, M. J. (2006). Measuring cognitive load to test the usability of web sites. Paper presented at the Annual Conference-society for technical communication. Retrieved from https://www.yumpu.com/en/document/view/26718604/measuring-cognitive-load-to-test-the-usability-of-web-sites. Urigüen, J. A., & Garcia-Zapirain, B. (2015). EEG artifact removal—state-of-the-art and guidelines. Journal of neural engineering, 12(3), 031001. 10.1088/1741-2560/12/3/031001 Van Houdt, G., Mosquera, C., & Nápoles, G. (2020). A review on the long short-term memory model. Artificial Intelligence Review, 53, 5929-5955. 10.1007/s10462-020-09838-1 Wellman, B. (2004). Connecting communities: On and offline. Contexts, 3(4), 22-28. 10.1525/ctx.2004.3.4.22 Yoo, G., Kim, H., & Hong, S. (2023). Prediction of Cognitive Load from Electroencephalography Signals Using Long Short-Term Memory Network. Bioengineering, 10(3), 361. 10.3390/bioengineering10030361