Design and Evaluation of a Web Platform for Connecting Instructors and Practitioners – Instructors’ Perspective

References

• Abudayyeh, O., Russell, J., Johnston, D., & Rowings, J. (2000). Construction engineering and management undergraduate education. Journal of Construction Engineering and Management, 126(3), 169–175. https://doi.org/10.1061/(ASCE)0733-9364(2000)126:3(169)
   Web of Science ®Google Scholar
• Afonso, A., Ramírez, J. J., & Díaz-Puente, J. M. (2012). University-industry cooperation in the education domain to foster competitiveness and employment. Procedia-Social and Behavioral Sciences, 46, 3947–3953. https://doi.org/10.1016/j.sbspro.2012.06.177
   Google Scholar
• Alharbi, S., & Drew, S. (2014). Using the technology acceptance model in understanding academics’ behavioural intention to use learning management systems. International Journal of Advanced Computer Science and Applications, 5(1), 143–155. https://doi.org/10.14569/IJACSA.2014.050120
   Google Scholar
• Anderson, D., & Mourgues, C. (2014). Industry participation in construction capstone courses: A company’s experience. Practice Periodical on Structural Design & Construction, 19(1), 73–76. https://doi.org/10.1061/(ASCE)SC.1943-5576.0000178
   Google Scholar
• Bangor, A., Kortum, P., & Miller, J. (2009). Determining what individual SUS scores mean: Adding an adjective rating scale. Journal of Usability Studies, 4(3), 114–123.
   Google Scholar
• Blix Germundsson, L. (2020). SLU partnership alnarp: Connecting academia, industry, and society. https://pub.epsilon.slu.se/17242
   Google Scholar
• Bojko, A. (2006). Using eye tracking to compare web page designs: A case study. Journal of Usability Studies, 1(3), 112–120.
   Google Scholar
• Bozoglu, J. (2016). Collaboration and coordination learning modules for BIM education. Journal of Information Technology in Construction, 21, 152–163. https://itcon.org/papers/2016_10.content.0324
   Web of Science ®Google Scholar
• Brooke, J. (1996). SUS: A quick and dirty usability. Usability Evaluation in Industry, 189(3), 189–194.
   Google Scholar
• Bruneel, J., d’Este, P., & Salter, A. (2010). Investigating the factors that diminish the barriers to university-industry collaboration. Research Policy, 39(7), 858–868. https://doi.org/10.1016/j.respol.2010.03.006
   Web of Science ®Google Scholar
• Bujang, M. A., & Baharum, N. (2016). Sample size guideline for correlation analysis. World Journal of Social Science Research, 3(1), 37–46. https://doi.org/10.22158/wjssr.v3n1p37
   Google Scholar
• Cappelli, P. H. (2015). Skill gaps, skill shortages, and skill mismatches: Evidence and arguments for the United States. ILR Review, 68(2), 251–290. https://doi.org/10.1177/0019793914564961
   Web of Science ®Google Scholar
• Carbone, A., Rayner, G. M., Ye, J., & Durandet, Y. (2020). Connecting curricula content with career context: The value of engineering industry site visits to students, academics, and industry. European Journal of Engineering Education, 45(6), 971–984. https://doi.org/10.1080/03043797.2020.1806787
   Web of Science ®Google Scholar
• 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), 13–19. https://doi.org/10.3991/ijac.v8i2.4408
   Google Scholar
• Chen, H.-E., Sonntag, C. C., Pepley, D. F., Prabhu, R. S., Han, D. C., Moore, J. Z., & Miller, S. R. (2019). Looks can be deceiving: Gaze pattern differences between novices and experts during placement of central lines. The American Journal of Surgery, 217(2), 362–367. https://doi.org/10.1016/j.amjsurg.2018.11.007
   PubMed Web of Science ®Google Scholar
• Christo-Baker, E. A., Sindone, A., & Roper, C. (2017). Addressing the skills gap: A regional analysis. The Journal of Applied Business and Economics, 19(8), 10–21.
   Google Scholar
• Confalonieri, R., & Janes, A. (2022). A technology transfer portal to promote industry-academia collaboration in South-Tyrol. In G. Kotsis (Ed.), Database and expert systems applications - DEXA 2022 workshops communications in computer and information science (Vol. 1633, pp. 221–231). Springer. https://doi.org/10.1007/978-3-031-14343-4_21
   Google Scholar
• Cowen, L., Ball, L. J., & Delin, J. (2002). An eye movement analysis of web page usability. In X. Faulkner, J. Finlay, & F. Détienne (Eds.), People and computers XVI - Memorable Yet Invisible (pp. 317–335). Springer. https://doi.org/10.1007/978-1-4471-0105-5_19
   Google Scholar
• Cruess, R. L., Cruess, S. R., Boudreau, J. D., Snell, L., & Steinert, Y. (2015). A schematic representation of the professional identity formation and socialization of medical students and residents: A guide for medical educators. Academic Medicine, 90(6), 718–725. https://doi.org/10.1097/ACM.0000000000000700
   PubMed Web of Science ®Google Scholar
• Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results [ Unpublished doctoral dissertation]. Massachusetts Institute of Technology.
   Google Scholar
• Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions, and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475–487. https://doi.org/10.1006/imms.1993.1022
   Google Scholar
• Deng, M., & Gu, X. (2021). Information acquisition, emotion experience, and behaviour intention during online shopping: An eye-tracking study. Behaviour & Information Technology, 40(7), 635–645. https://doi.org/10.1080/0144929X.2020.1713890
   Web of Science ®Google Scholar
• Detsimas, N., Coffey, V., Sadiqi, Z., & Li, M. (2016). Workplace training and generic and technical skill development in the Australian construction industry. Journal of Management Development, 35(4), 486–504. https://doi.org/10.1108/JMD-05-2015-0073
   Web of Science ®Google Scholar
• Downes, S. (2005). An introduction to connective knowledge. Conference Series. https://library.oapen.org/bitstream/handle/20.500.12657/33882/449459.pdf?sequenc#page=84
   Google Scholar
• Downes, S. (2008). Places to go: Connectivism & connective knowledge. Innovate: Journal of Online Education, 5(1), 1–6.
   Google Scholar
• Edward Back, W., & Sanders, S. R. (1998). Industry expectations for engineering graduates. Engineering, Construction & Architectural Management, 5(2), 137–143. https://doi.org/10.1108/eb021068
   Google Scholar
• Eiris Pereira, R., & Gheisari, M. (2019). Site visit application in construction education: A descriptive study of faculty members. International Journal of Construction Education and Research, 15(2), 83–99. https://doi.org/10.1080/15578771.2017.1375050
   Google Scholar
• Fu, H., Zhu, H., Xue, P., Hu, X., Guo, X., & Liu, B. (2022). Eye-tracking study of public acceptance of 5G base stations in the context of the COVID-19 pandemic. Engineering, Construction & Architectural Management, 30(8), 3416–3437. https://doi.org/10.1108/ECAM-10-2021-0946
   Web of Science ®Google Scholar
• Garrot-Lavoué, E. (2011). Interconnection of communities of practice: A web platform for knowledge management. In Proceeding of the Fifth International Conference on Knowledge Management and Information Sharing (KMIS 2009) (pp. 13–20). Madeira, Portugal. October 6–8, 2009. https://doi.org/10.48550/arXiv.1201.1425
   Google Scholar
• Gelderblom, H., Matthee, M., Hattingh, M., & Weilbach, L. (2019). High school learners’ continuance intention to use electronic textbooks: A usability study. Education and Information Technologies, 24(2), 1753–1776. https://doi.org/10.1007/s10639-018-9850-z
   Web of Science ®Google Scholar
• Goldie, J. G. S. (2016). Connectivism: A knowledge learning theory for the digital age? Medical Teacher, 38(10), 1064–1069. https://doi.org/10.3109/0142159X.2016.1173661
   PubMed Web of Science ®Google Scholar
• Gonzalez-Holland, E., Whitmer, D., Moralez, L., & Mouloua, M. (2017). Examination of the use of Nielsen’s 10 usability heuristics & outlooks for the future. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 61(1), 1472–1475. https://doi.org/10.1177/1541931213601853
   Google Scholar
• Gould, J. D., & Lewis, C. (1985). Designing for usability: Key principles and what designers think. Communications of the ACM, 28(3), 300–311. https://doi.org/10.1145/3166.3170
   Web of Science ®Google Scholar
• Grimes, J., Ehrlich, K., & Vaske, J. (1986). User interface design: Are human factors principles used? ACM SIGCHI Bulletin, 17(3), 22–26. https://doi.org/10.1145/15671.15672
   Google Scholar
• Gruzd, A., Staves, K., & Wilk, A. (2012). Connected scholars: Examining the role of social media in research practices of faculty using the UTAUT model. Computers in Human Behavior, 28(6), 2340–2350. https://doi.org/10.1016/j.chb.2012.07.004
   Web of Science ®Google Scholar
• Hartson, R., & Pyla, P. S. (2012). The UX Book: Process and guidelines for ensuring a quality user experience. Elsevier.
   Google Scholar
• Hu, Z., Ding, S., Li, S., Chen, L., & Yang, S. (2019). Adoption intention of fintech services for bank users: An empirical examination with an extended technology acceptance model. Symmetry, 11(3), 1–16. https://doi.org/10.3390/sym11030340
   Google Scholar
• Idrees, A. R., Kraft, R., Winter, M., Küchler, A.-M., Baumeister, H., Reilly, R., Reichert, M., & Pryss, R. (2023). Exploring the usability of an internet-based intervention and its providing eHealth platform in an eye-tracking study. Journal of Ambient Intelligence and Humanized Computing, 14(7), 9621–9636. https://doi.org/10.1007/s12652-023-04635-4
   PubMedGoogle Scholar
• Inada, Y. (2023). A comparative study of physical versus online classrooms: Co-creation in industry-academia collaborative education. Review of Integrative Business and Economics Research, 12(2), 97–117.
   Google Scholar
• Irizarry, J., & Adams, W. (2006). Benefits of industry involvement in construction education. In 2nd Specialty Conference on Leadership and Management in Construction. https://www.academia.edu/download/30278937/finalproceedings.pdf#page=25
   Google Scholar
• Iyer, T. (2014). Role of industry-academia interface for filling the skill gap. CLEAR International Journal of Research in Commerce and Management, 5(3), 52–54.
   Google Scholar
• Jacobs, F., Cain, W., Lu, R., & Daugherty, A. (2022). Case study: Teaching with industry (TWI) using new videoconferencing technology and innovative classroom setups. Education Sciences, 12(2), 1–18. https://doi.org/10.3390/educsci12020128
   Web of Science ®Google Scholar
• Johansson-Pajala, R.-M., Gusdal, A., Eklund, C., Florin, U., & Wågert, P. V. H. (2023). A codesigned web platform for reducing social isolation and loneliness in older people: A feasibility study. Informatics for Health and Social Care, 48(2), 109–124. https://doi.org/10.1080/17538157.2022.2070068
   PubMed Web of Science ®Google Scholar
• Joseph, A. W., & Murugesh, R. (2020). Potential eye tracking metrics and indicators to measure cognitive load in human-computer interaction research. Journal of Scientific Research, 64(1), 168–175. https://doi.org/10.37398/JSR.2020.640137
   Google Scholar
• Just, M. A., & Carpenter, P. A. (1976). Eye fixations and cognitive processes. Cognitive Psychology, 8(4), 441–480. https://doi.org/10.1016/0010-0285(76)90015-3
   Web of Science ®Google Scholar
• Kamal, S. A., Shafiq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60, 1–10. https://doi.org/10.1016/j.techsoc.2019.101212
   Web of Science ®Google Scholar
• Karimi, H., Taylor, T. R., Dadi, G. B., Goodrum, P. M., & Srinivasan, C. (2018, 7). Impact of skilled labor availability on construction project cost performance. Journal of Construction Engineering and Management, 144(7). https://doi.org/10.1061/(ASCE)CO.1943-7862.0001512
   Web of Science ®Google Scholar
• Kaya, A., Ozturk, R., & Altin Gumussoy, C. (2019). Usability measurement of mobile applications with system usability scale (SUS). In: F. Calisir, E. Cevikcan, & H. Camgoz Akdag (Eds.). Industrial Engineering in the Big Data Era: Selected Papers from the Global Joint Conference on Industrial Engineering and Its Application Areas, GJCIE 2018 (pp.389–400). Springer, Cham, Nevsehir, Turkey. https://doi.org/10.1007/978-3-030-03317-0_32
   Google Scholar
• Kaymaz, K., & Eryiğit, K. Y. (2011). Determining factors hindering university-industry collaboration: An analysis from the perspective of academicians in the context of entrepreneurial science paradigm. International Journal of Social Inquiry, 4(1), 185–213.
   Google Scholar
• Khan, R., & Gogos, A. (2013). Online mentoring for biotechnology graduate students: An industry-academia partnership. Journal of Asynchronous Learning Networks, 17(1), 89–107. https://doi.org/10.24059/olj.v17i1.294
   Google Scholar
• King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740–755. https://doi.org/10.1016/j.im.2006.05.003
   Web of Science ®Google Scholar
• Kop, R., & Hill, A. (2008). Connectivism: Learning theory of the future or vestige of the past? International Review of Research in Open & Distributed Learning, 9(3), 1–13. https://doi.org/10.19173/irrodl.v9i3.523
   Google Scholar
• Lewis, J. R. (2018). The system usability scale: Past, present, and future. International Journal of Human-Computer Interaction, 34(7), 577–590. https://doi.org/10.1080/10447318.2018.1455307
   Web of Science ®Google Scholar
• Lu, R., & Jacobs, F. (2022). An innovative teaching model: Involvement of industry practitioners in the teaching of construction management curriculum. A paper presented at the 2022 ASEE Annual Conference & Exposition, Minneapolis, Minnesota, USA. June 26–29, 2022. https://peer.asee.org/40578
   Google Scholar
• Manesh, S. N., Choi, J. O., & Shrestha, P. (2020). Critical literature review on the diversity and inclusion of women and ethnic minorities in construction and civil engineering industry and education. Construction Research Congress 2020: Safety, Workforce, and Education. https://doi.org/10.1061/9780784482872.020
   Google Scholar
• Marangunić, N., & Granić, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14(1), 81–95. https://doi.org/10.1007/s10209-014-0348-1
   Web of Science ®Google Scholar
• Mohamed, A. H. H., Tawfik, H., Al-Jumeily, D., & Norton, L. (2011). MoHTAM: A technology acceptance model for mobile health applications. 2011 Developments in E-Systems Engineering, Dubai, United Arab Emirates, 13–18. https://doi.org/10.1109/DeSE.2011.79
   Google Scholar
• NASEM. (2016). National academies of sciences, engineering, medicine: Promising practices for strengthening the regional STEM workforce development ecosystem. National Academies Press. https://doi.org/10.17226/21894
   Google Scholar
• Niedergassel, B., & Leker, J. (2011). Different dimensions of knowledge in cooperative R&D projects of university scientists. Technovation, 31(4), 142–150. https://doi.org/10.1016/j.technovation.2010.10.005
   Web of Science ®Google Scholar
• Nielsen, J. (1994). Enhancing the explanatory power of usability heuristics. In Proceedings of the SIGCHI conference on Human Factors in Computing Systems. https://dl.acm.org/doi/pdf/10.1145/191666.191729
   Google Scholar
• Nielsen, J. (2000a). Jakob’s law of internet user experience. https://www.nngroup.com/videos/jakobs-law-internet-ux/
   Google Scholar
• Nielsen, J. (2000b). Why you only need to test with 5 users. Nielsen Norman Group. https://www.nngroup.com/articles/why-you-only-need-to-test-with-5-users/
   Google Scholar
• Oyekunle, R., Bello, O., Jubril, Q., Sikiru, I., & Balogun, A. (2020). Usability evaluation using eye-tracking on e-commerce and education domains. Journal of Information Technology and Computing, 1(1), 1–13. https://doi.org/10.48185/jitc.v1i1.43
   Google Scholar
• Pal, D., & Vanijja, V. (2020). Perceived usability evaluation of Microsoft teams as an online learning platform during COVID-19 using system usability scale and technology acceptance model in India. Children and Youth Services Review, 119, 105535. https://doi.org/10.1016/j.childyouth.2020.105535
   PubMed Web of Science ®Google Scholar
• Pamidimukkala, A., & Kermanshachi, S. (2021). Impact of COVID-19 on field and office workforce in the construction industry. Project Leadership and Society, 2, 1–10. https://doi.org/10.1016/j.plas.2021.100018
   Google Scholar
• Pereira, E. T., Vilas-Boas, M., & Rebelo, C. C. (2019). Graduates’ skills and employability: The view of students from different European countries. Higher Education, Skills & Work-Based Learning, 9(4), 758–774. https://doi.org/10.1108/HESWBL-10-2018-0098
   Web of Science ®Google Scholar
• Peters, D. L., & Lucietto, A. M. (2016). A survey of types of industry-academia collaboration. Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. https://doi.org/10.18260/p.26455
   Google Scholar
• Rafique, H., Almagrabi, A. O., Shamim, A., Anwar, F., & Bashir, A. K. (2020). Investigating the acceptance of mobile library applications with an extended technology acceptance model (TAM). Computers & Education, 145, 1–13. https://doi.org/10.1016/j.compedu.2019.103732
   Web of Science ®Google Scholar
• Revythi, A., & Tselios, N. (2019). Extension of technology acceptance model by using system usability scale to assess behavioral intention to use e-learning. Education and Information Technologies, 24(4), 2341–2355. https://doi.org/10.1007/s10639-019-09869-4
   Web of Science ®Google Scholar
• Rizvi, I. A., & Aggarwal, A. (2005). Enhancing student employability: Higher education and workforce development. In Proceedings of the 9th Quality in Higher Education Seminar, Birmingham, UK. https://www.qualityresearchinternational.com/esecttools/eseconferencepapers/aggarwal.doc
   Google Scholar
• Rizvi, W. H. (2020). Brand Visual Eclipse (BVE): When the brand fixation spent is minimal in relation to the celebrity. In F. Davis, R. Riedl, J. vom Brocke, P. Léger, A. Randolph, & T. Fischer (Eds.), Information systems and Neuroscience. Lecture notes in information systems and organisation (Vol. 32, pp. 295–303. (pp. 295–303). Springer. https://doi.org/10.1007/978-3-030-28144-1_33
   Google Scholar
• Sagnier, C., Loup-Escande, E., Lourdeaux, D., Thouvenin, I., & Valléry, G. (2020). User acceptance of virtual reality: An extended technology acceptance model. International Journal of Human-Computer Interaction, 36(11), 993–1007. https://doi.org/10.1080/10447318.2019.1708612
   Web of Science ®Google Scholar
• Salloum, S. A., Alhamad, A. Q. M., Al-Emran, M., Monem, A. A., & Shaalan, K. (2019). Exploring students’ acceptance of e-learning through the development of a comprehensive technology acceptance model. IEEE Access, 7, 128445–128462. https://doi.org/10.1109/ACCESS.2019.2939467
   Web of Science ®Google Scholar
• Sharma, V., & Sriraman, V. (2012). Development and implementation of an industry-sponsored construction management capstone course. In Proceedings of 2012 ASEE Annual Conference & Exposition. https://peer.asee.org/21203
   Google Scholar
• Shroff, R. H., Deneen, C. C., & Ng, E. M. (2011). Analysis of the technology acceptance model in examining students’ behavioural intention to use an e-portfolio system. Australasian Journal of Educational Technology, 27(4), 600–618. https://doi.org/10.1474/2ajet.940
   Web of Science ®Google Scholar
• Siemens, G. (2005). Connectivism: Learning as network-creation. ASTD Learning News, 10(1), 1–28.
   Google Scholar
• Sihombing, J. H., Laksitowening, K. A., & Darwiyanto, E. (2020). Personalized e-learning content based on Felder-Silverman learning style model. 2020 8th International Conference on Information and Communication Technology (ICoICT) (pp. 1–6), Yogyakarta, Indonesia. https://doi.org/10.1109/ICoICT49345.2020.9166452
   Google Scholar
• Snell, S., & Snell-Siddle, C. (2017). Connecting the dots: Industry| academia| online. In New Zealand Association for Cooperative Education 2017 Conference Proceedings. https://www.wilnz.nz/wp-content/uploads/2017/10/NZACE-Proceedings-2017.pdf#page=49
   Google Scholar
• Suleman, F., & Laranjeiro, A. M. C. (2018). The employability skills of graduates and employers’ options in Portugal: An explorative study of anticipative and remedial strategies. Education+ Training, 60(9), 1097–1111. https://doi.org/10.1108/ET-10-2017-0158
   Web of Science ®Google Scholar
• Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273–1296. https://doi.org/10.1007/s11165-016-9602-2
   Web of Science ®Google Scholar
• Tayeh, R., & Issa, R. R. (2021). Developing construction information systems courses based on collaboration between industry and academia. Journal of Architectural Engineering, 27(3), 1–11. https://doi.org/10.1061/(ASCE)AE.1943-5568.0000476
   Web of Science ®Google Scholar
• Teo, T., Lee, C. B., Chai, C. S., & Wong, S. L. (2009). Assessing the intention to use technology among pre-service teachers in Singapore and Malaysia: A multigroup invariance analysis of the technology acceptance Model (TAM). Computers & Education, 53(3), 1000–1009. https://doi.org/10.1016/j.compedu.2009.05.017
   Web of Science ®Google Scholar
• Vasseur, A., Léger, P.-M., & Sénécal, S. (2019). Eye-tracking for IS research: A literature review. In Proceedings of the Eighteenth Annual Pre-ICIS Workshop on HCI Research in MIS, Munich, Germany. December 15, 2019. https://sighci.org/uploads/2019_sighci_proceedings/FullPaper1.pdf
   Google Scholar
• Vlachogianni, P., & Tselios, N. (2022). Perceived usability evaluation of educational technology using the System Usability Scale (SUS): A systematic review. Journal of Research on Technology in Education, 54(3), 392–409. https://doi.org/10.1080/15391523.2020.1867938
   Web of Science ®Google Scholar
• Vlachopoulos, D., & Makri, A. (2019). Online communication and interaction in distance higher education: A framework study of good practice. International Review of Education, 65(4), 605–632. https://doi.org/10.1007/s11159-019-09792-3
   Web of Science ®Google Scholar
• Wang, Q., Yang, S., Liu, M., Cao, Z., & Ma, Q. (2014). An eye-tracking study of website complexity from a cognitive load perspective. Decision Support Systems, 62, 1–10. https://doi.org/10.1016/j.dss.2014.02.007
   Web of Science ®Google Scholar
• Wellman, B. (2004). Connecting communities: On and offline. Contexts, 3(4), 22–28. https://doi.org/10.1525/ctx.2004.3.4.22
   Google Scholar
• Yusuf, A. O., Afolabi, A., Akanmu, A., Murzi, H., Nieto Leal, A., Ball, S., & Ofori-Boadu, A. (2024). Collaborations between industry and academia for future workforce development: Construction practitioners’ perspective. Engineering, Construction and Architectural Management, Vol Ahead-Of-Print No Ahead-Of-Print. https://doi.org/10.1108/ECAM-07-2023-0683
   Google Scholar
• Zardari, B. A., Hussain, Z., Arain, A. A., Rizvi, W. H., & Vighio, M. S. (2021). QUEST e-learning portal: Applying heuristic evaluation, usability testing, and eye tracking. Universal Access in the Information Society, 20(3), 531–543. https://doi.org/10.1007/s10209-020-00774-z
   Web of Science ®Google Scholar
• Zender, F., Wright, M., Richey, M., Gorrell, S., McPherson, K., & Madni, A. M. (2014). Aerospace Partners for the Advancement of Collaborative Engineering (AerosPACE)-connecting industry and academia through a novel capstone course. https://cmapspublic3.ihmc.us/rid=1NWPXTBPG-34964K-17GG/+++++201403014%20ICELW%20-%20Connecting%20Industry%20and%20Academia%20E-Learning%20v6.pdf
   Google Scholar
• Zou, Z., & Ergan, S. (2019). Where do we look? an eye-tracking study of architectural features in building design. In I. Mutis & T. Hartmann (Eds.), Advances in informatics and computing in civil and construction engineering (pp. 439–446). Springer. https://doi.org/10.1007/978-3-030-00220-6_52
   Google Scholar