{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T05:43:53Z","timestamp":1775281433609,"version":"3.50.1"},"reference-count":84,"publisher":"American Society of Civil Engineers (ASCE)","issue":"4","content-domain":{"domain":["ascelibrary.org"],"crossmark-restriction":true},"short-container-title":["J. Comput. Civ. Eng."],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1061\/jccee5.cpeng-5169","type":"journal-article","created":{"date-parts":[[2023,3,25]],"date-time":"2023-03-25T03:54:28Z","timestamp":1679716468000},"update-policy":"https:\/\/doi.org\/10.1061\/do.news.20190416.0001","source":"Crossref","is-referenced-by-count":17,"title":["Detecting Learning Stages within a Sensor-Based Mixed Reality Learning Environment Using Deep Learning"],"prefix":"10.1061","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3852-4032","authenticated-orcid":true,"given":"Omobolanle","family":"Ogunseiju","sequence":"first","affiliation":[{"name":"Assistant Professor, School of Building of Construction, College of Design, Georgia Tech, Atlanta, GA 30332 (corresponding author). ORCID: ."}]},{"given":"Abiola","family":"Akinniyi","sequence":"additional","affiliation":[{"name":"Ph.D. Student, Myers-Lawson School of Construction, Virginia Tech, Blacksburg, VA 24060."}]},{"given":"Nihar","family":"Gonsalves","sequence":"additional","affiliation":[{"name":"Ph.D. Candidate, Myers-Lawson School of Construction, Virginia Tech, Blacksburg, VA 24060."}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8668-3022","authenticated-orcid":true,"given":"Mohammad","family":"Khalid","sequence":"additional","affiliation":[{"name":"Ph.D. Student, Myers-Lawson School of Construction, Virginia Tech, Blacksburg, VA 24060. ORCID:"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9145-4865","authenticated-orcid":true,"given":"Abiola","family":"Akanmu","sequence":"additional","affiliation":[{"name":"Associate Professor, Construction Engineering and Management, Myers-Lawson School of Construction, Virginia Tech, Blacksburg, VA 24060. ORCID:"}]}],"member":"30","reference":[{"key":"e_1_3_3_2_1","first-page":"38","volume-title":"Usability testing of a U-500 insulin syringe: A human factors approach","author":"Abraham K.","year":"2013","unstructured":"Abraham, K., B. Patail, and D. Wurth. 2013. Usability testing of a U-500 insulin syringe: A human factors approach, 38\u201343. Thousand Oaks, CA: Journal of Diabetes Science and Technology."},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-021-00444-8"},{"key":"e_1_3_3_4_1","doi-asserted-by":"publisher","DOI":"10.3758\/s13428-014-0550-3"},{"key":"e_1_3_3_5_1","doi-asserted-by":"publisher","DOI":"10.1080\/0952813X.2016.1198936"},{"key":"e_1_3_3_6_1","doi-asserted-by":"crossref","unstructured":"Asish S. M. E. Hossain A. K. Kulshreshth and C. W. Borst. 2021. \u201cDeep learning on eye gaze data to classify student distraction level in an educational VR environment.\u201d In Proc. ICAT-EGVE 2021-Int. Conf. on Artificial Reality and Telexistence and Eurographics Symp. on Virtual Environments. Geneva: Eurographics Association.","DOI":"10.1145\/3485279.3488283"},{"key":"e_1_3_3_7_1","first-page":"75","volume-title":"Detecting distracted students in educational vr environments using machine learning on eye gaze data","author":"Asish S. M.","year":"2022","unstructured":"Asish, S. M., A. Kulshreshth, and C. W. Borst. 2022. Detecting distracted students in educational vr environments using machine learning on eye gaze data, 75\u201387. Amsterdam, Netherlands: Elsevier."},{"key":"e_1_3_3_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102569"},{"key":"e_1_3_3_9_1","doi-asserted-by":"crossref","unstructured":"Azhar S. J. Kim and A. Salman. 2018. \u201cImplementing virtual reality and mixed reality technologies in construction education: Students\u2019 perceptions and lessons learned.\u201d In Proc. 11th Annual Int. Conf. of Education. Valencia Spain: International Association of Technology Education and Development.","DOI":"10.21125\/iceri.2018.0183"},{"key":"e_1_3_3_10_1","doi-asserted-by":"crossref","unstructured":"Barral O. S. Lall\u00e9 G. Guz A. Iranpour and C. Conati. 2020. \u201cEye-tracking to predict user cognitive abilities and performance for user-adaptive narrative visualizations.\u201d In Proc. 2020 Int. Conf. on Multimodal Interaction. New York: Association for Computing Machinery.","DOI":"10.1145\/3382507.3418884"},{"key":"e_1_3_3_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2020.3028738"},{"key":"e_1_3_3_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10799-021-00336-6"},{"key":"e_1_3_3_13_1","unstructured":"Cerny T. H. Vrzakova and M. Hradis. 2012. \u201cWhat do you want to do next: A novel approach for intent prediction in gaze-based interaction.\u201d In Proc. Eye Tracking Research and Applications Symp. (ETRA). New York: Association for Computing Machinery."},{"key":"e_1_3_3_14_1","doi-asserted-by":"publisher","DOI":"10.1186\/2192-1962-3-15"},{"key":"e_1_3_3_15_1","volume-title":"Hyperparameter search in machine learning","author":"Claesen M.","year":"2015","unstructured":"Claesen, M., and B. De Moor. 2015. Hyperparameter search in machine learning. Cham, Switzerland: Springer."},{"key":"e_1_3_3_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3301400"},{"key":"e_1_3_3_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2851672"},{"key":"e_1_3_3_18_1","doi-asserted-by":"crossref","unstructured":"Czuszynski K. A. Kwasniewska M. Szankin and J. Ruminski. 2018. \u201cOptical sensor based gestures inference using recurrent neural network in mobile conditions.\u201d In Proc. 2018 11th Int. Conf. on Human System Interaction. New York: IEEE.","DOI":"10.1109\/HSI.2018.8430823"},{"key":"e_1_3_3_19_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8535.2009.01038.x"},{"key":"e_1_3_3_20_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-42764-z"},{"key":"e_1_3_3_21_1","doi-asserted-by":"publisher","DOI":"10.1002\/9781119792642.ch1"},{"key":"e_1_3_3_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2020.106318"},{"key":"e_1_3_3_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPC.2012.2206190"},{"key":"e_1_3_3_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-019-08453-9"},{"key":"e_1_3_3_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/0042-6989(92)90145-9"},{"key":"e_1_3_3_26_1","volume-title":"Eye tracking: A comprehensive guide to methods and measures","author":"Holmqvist K.","year":"2011","unstructured":"Holmqvist, K., M. Nystr\u00f6m, R. Andersson, R. Dewhurst, H. Jarodzka, and J. Van de Weijer. 2011. Eye tracking: A comprehensive guide to methods and measures. Oxford, UK: OUP Oxford."},{"key":"e_1_3_3_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neubiorev.2020.09.036"},{"key":"e_1_3_3_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.petrol.2022.110544"},{"key":"e_1_3_3_29_1","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3147971"},{"key":"e_1_3_3_30_1","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1052"},{"key":"e_1_3_3_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-021-10600-0"},{"key":"e_1_3_3_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-018-6518-z"},{"key":"e_1_3_3_33_1","doi-asserted-by":"publisher","DOI":"10.1037\/0033-295X.87.4.329"},{"key":"e_1_3_3_34_1","doi-asserted-by":"crossref","unstructured":"Justus D. J. Brennan S. Bonner and A. S. McGough. 2018. \u201cPredicting the computational cost of deep learning models.\u201d In Proc. 2018 IEEE Int. Conf. on big data (Big Data). New York: IEEE.","DOI":"10.1109\/BigData.2018.8622396"},{"key":"e_1_3_3_35_1","doi-asserted-by":"publisher","DOI":"10.1177\/0018720820904229"},{"key":"e_1_3_3_36_1","doi-asserted-by":"crossref","unstructured":"Kim J.-Y. and S.-B. Cho. 2019. \u201cEvolutionary optimization of hyperparameters in deep learning models.\u201d In Proc. 2019 IEEE Congress on Evolutionary Computation (CEC). New York: IEEE.","DOI":"10.1109\/CEC.2019.8790354"},{"key":"e_1_3_3_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11045-020-00731-2"},{"key":"e_1_3_3_38_1","volume-title":"The case for retraining of ML models for IoT device identification at the edge","author":"Kolcun R.","year":"2020","unstructured":"Kolcun, R., D. A. Popescu, V. Safronov, P. Yadav, A. M. Mandalari, Y. Xie, R. Mortier, and H. Haddadi. 2020. The case for retraining of ML models for IoT device identification at the edge. Ithaca, NY: Cornell Univ."},{"key":"e_1_3_3_39_1","doi-asserted-by":"crossref","unstructured":"Koochaki F. and L. Najafizadeh. 2019. \u201cEye gaze-based early intent prediction utilizing cnn-lstm.\u201d In Proc. 2019 41st Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC). New York: IEEE.","DOI":"10.1109\/EMBC.2019.8857054"},{"key":"e_1_3_3_40_1","doi-asserted-by":"crossref","unstructured":"Koorathota S. C. K. Thakoor P. Adelman Y. Mao X. Liu and P. Sajda. 2020. \u201cSequence models in eye tracking: Predicting pupil diameter during learning.\u201d In Proc. ACM Symp. on Eye Tracking Research and Applications. New York: Association for Computing Machinery.","DOI":"10.1145\/3379157.3391653"},{"key":"e_1_3_3_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2011.06.027"},{"issue":"3","key":"e_1_3_3_42_1","first-page":"713","article-title":"A framework for using mobile based virtual reality and augmented reality for experiential construction safety education","volume":"31","author":"Le Q. T.","year":"2015","unstructured":"Le, Q. T., A. Pedro, C. R. Lim, H. T. Park, C. S. Park, and H. K. Kim. 2015. \u201cA framework for using mobile based virtual reality and augmented reality for experiential construction safety education.\u201d Int. J. Eng. Educ. 31 (3): 713\u2013725.","journal-title":"Int. J. Eng. Educ."},{"key":"e_1_3_3_43_1","doi-asserted-by":"crossref","unstructured":"Liao Y.-C. C.-C. Wang C.-H. Tu M.-C. Kao W.-Y. Liang and S.-H. Hung. 2020. \u201cPerfNetRT: Platform-aware performance modeling for optimized deep neural networks.\u201d In Proc. 2020 Int. Computer Symp. (ICS). New York: IEEE.","DOI":"10.1109\/ICS51289.2020.00039"},{"key":"e_1_3_3_44_1","doi-asserted-by":"publisher","DOI":"10.1002\/j.2168-9830.2010.tb01067.x"},{"key":"e_1_3_3_45_1","doi-asserted-by":"publisher","DOI":"10.1080\/17434440.2021.1860750"},{"key":"e_1_3_3_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2960537"},{"issue":"2","key":"e_1_3_3_47_1","first-page":"56","article-title":"The effect of experience on system usability scale ratings","volume":"7","author":"McLellan S.","year":"2012","unstructured":"McLellan, S., A. Muddimer, and S. C. Peres. 2012. \u201cThe effect of experience on system usability scale ratings.\u201d J. Usability Stud. 7 (2): 56\u201367.","journal-title":"J. Usability Stud."},{"key":"e_1_3_3_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2020.3004686"},{"key":"e_1_3_3_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3117035"},{"key":"e_1_3_3_50_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21062051"},{"key":"e_1_3_3_51_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)CO.1943-7862.0002130"},{"key":"e_1_3_3_52_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2022.101637"},{"key":"e_1_3_3_53_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2020.07.012"},{"key":"e_1_3_3_54_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cag.2005.10.004"},{"key":"e_1_3_3_55_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2019.100944"},{"key":"e_1_3_3_56_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10111-012-0234-7"},{"key":"e_1_3_3_57_1","doi-asserted-by":"crossref","unstructured":"Salminen J. M. Nagpal H. Kwak J. An S.-G. Jung and B. J. Jansen. 2019. \u201cConfusion prediction from eye-tracking data: Experiments with machine learning.\u201d In Proc. 9th Int. Conf. on Information Systems and Technologies. New York: Association for Computing Machinery.","DOI":"10.1145\/3361570.3361577"},{"key":"e_1_3_3_58_1","doi-asserted-by":"publisher","DOI":"10.2174\/1875323X01002010018"},{"key":"e_1_3_3_59_1","doi-asserted-by":"publisher","DOI":"10.1179\/jmt.2009.17.2.27E"},{"key":"e_1_3_3_60_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(96)00086-X"},{"key":"e_1_3_3_61_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2021.104124"},{"key":"e_1_3_3_62_1","doi-asserted-by":"publisher","DOI":"10.5539\/elt.v3n4p237"},{"key":"e_1_3_3_63_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-020-01117-5"},{"key":"e_1_3_3_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2016.2535233"},{"key":"e_1_3_3_65_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"e_1_3_3_66_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2020.103138"},{"key":"e_1_3_3_67_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2020.110591"},{"key":"e_1_3_3_68_1","doi-asserted-by":"crossref","unstructured":"Steed A. Y. Pan F. Zisch and W. Steptoe. 2016. \u201cThe impact of a self-avatar on cognitive load in immersive virtual reality.\u201d In Proc. 2016 IEEE Virtual Reality (VR). New York: IEEE.","DOI":"10.1109\/VR.2016.7504689"},{"key":"e_1_3_3_69_1","doi-asserted-by":"publisher","DOI":"10.4324\/9781315806167"},{"key":"e_1_3_3_70_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00464-014-3683-7"},{"key":"e_1_3_3_71_1","doi-asserted-by":"crossref","unstructured":"Toker D. B. Steichen M. Gingerich C. Conati and G. Carenini. 2014. \u201cTowards facilitating user skill acquisition: Identifying untrained visualization users through eye tracking.\u201d In Proc. 19th Int. Conf. on Intelligent User Interfaces. New York: Association for Computing Machinery.","DOI":"10.1145\/2557500.2557524"},{"key":"e_1_3_3_72_1","doi-asserted-by":"crossref","unstructured":"Um T. T. F. M. Pfister D. Pichler S. Endo M. Lang S. Hirche U. Fietzek and D. Kuli\u0107. 2017. \u201cData augmentation of wearable sensor data for parkinson\u2019s disease monitoring using convolutional neural networks.\u201d In Proc. 19th ACM Int. Conf. on Multimodal Interaction. New York: Association for Computing Machinery.","DOI":"10.1145\/3136755.3136817"},{"key":"e_1_3_3_73_1","doi-asserted-by":"publisher","DOI":"10.1080\/00140139.2014.990524"},{"key":"e_1_3_3_74_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2019.111799"},{"key":"e_1_3_3_75_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.forsciint.2017.08.012"},{"key":"e_1_3_3_76_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)CO.1943-7862.0001683"},{"key":"e_1_3_3_77_1","doi-asserted-by":"crossref","unstructured":"Wu W. A. Tesei S. Ayer J. London Y. Luo and V. Gunji. 2018. \u201cClosing the skills gap: Construction and engineering education using mixed reality\u2014A case study.\u201d In Proc. 2018 IEEE Frontiers in Education Conf. (FIE). New York: IEEE.","DOI":"10.1109\/FIE.2018.8658992"},{"key":"e_1_3_3_78_1","doi-asserted-by":"crossref","unstructured":"Xie Z. Y.-H. Huang G.-Q. Fang H. Ren S.-Y. Fang Y. Chen and J. Hu. 2018. \u201cRouteNet: Routability prediction for mixed-size designs using convolutional neural network.\u201d In Proc. 2018 IEEE\/ACM Int. Conf. on Computer-Aided Design (ICCAD). New York: IEEE.","DOI":"10.1145\/3240765.3240843"},{"key":"e_1_3_3_79_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2018.06.005"},{"key":"e_1_3_3_80_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssci.2021.105645"},{"key":"e_1_3_3_81_1","doi-asserted-by":"crossref","unstructured":"Yin Y. Y. Alqahtani J. H. Feng J. Chakraborty and M. P. McGuire. 2021. \u201cDeep learning methods for the prediction of information display type using eye tracking sequences.\u201d In Proc. 2021 20th IEEE Int. Conf. on Machine Learning and Applications (ICMLA). New York: IEEE.","DOI":"10.1109\/ICMLA52953.2021.00100"},{"key":"e_1_3_3_82_1","doi-asserted-by":"publisher","DOI":"10.5539\/mas.v11n8p47"},{"key":"e_1_3_3_83_1","doi-asserted-by":"crossref","unstructured":"Zhang H. L. Zhang and Y. Jiang. 2019. \u201cOverfitting and underfitting analysis for deep learning based end-to-end communication systems.\u201d In Proc. 2019 11th Int. Conf. on Wireless Communications and Signal Processing (WCSP). New York: IEEE.","DOI":"10.1109\/WCSP.2019.8927876"},{"key":"e_1_3_3_84_1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-its.2016.0208"},{"key":"e_1_3_3_85_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyr.2020.11.219"}],"container-title":["Journal of Computing in Civil Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ascelibrary.org\/doi\/pdf\/10.1061\/JCCEE5.CPENG-5169","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,25]],"date-time":"2023-03-25T03:54:48Z","timestamp":1679716488000},"score":1,"resource":{"primary":{"URL":"https:\/\/ascelibrary.org\/doi\/10.1061\/JCCEE5.CPENG-5169"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7]]},"references-count":84,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["10.1061\/JCCEE5.CPENG-5169"],"URL":"https:\/\/doi.org\/10.1061\/jccee5.cpeng-5169","relation":{},"ISSN":["0887-3801","1943-5487"],"issn-type":[{"value":"0887-3801","type":"print"},{"value":"1943-5487","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7]]},"assertion":[{"value":"2022-09-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-02-03","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-03-24","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"04023011"}}