1. Abdulrahaman, M., Faruk, N., Oloyede, A., Surajudeen-Bakinde, N., Olawoyin, L., Mejabi, O., Imam-Fulani, Y., Fahm, A., & Azeez, A. (2020). Multimedia tools in the teaching and learning processes: A systematic review. Heliyon, 6(11). DOI: 10.1016/j.heliyon.2020.e05312
2. 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. DOI: 10.1061/(ASCE)0733-9364(2000)126:3(169).
3. 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. DOI: 10.1016/j.sbspro.2012.06.177.
4. Aronson, J. D. (2018). Computer vision and machine learning for human rights video analysis: Case studies, possibilities, concerns, and limitations. Law & Social Inquiry, 43(4), 1188-1209. DOI: 10.1111/lsi.12353
5. Bojko, A. (2013). Eye tracking the user experience: A practical guide to research. Rosenfeld Media. https://www.perlego.com/book/1257094/eye-tracking-the-user-experience-a-practical-guide-to-research-pdf
6. Bozoglu, J. (2016). Collaboration and coordination learning modules for BIM education. J. Inf. Technol. Constr., 21, 152-163. http://www.itcon.org/2016/10
7. Carter, B. T., & Luke, S. G. (2020). Best practices in eye tracking research. International Journal of Psychophysiology, 155, 49-62. DOI: 10.1016/j.ijpsycho.2020.05.010
8. Castro-Alonso, J. C., Wong, M., Adesope, O. O., Ayres, P., & Paas, F. (2019). Gender imbalance in instructional dynamic versus static visualizations: A meta-analysis. Educational Psychology Review, 31, 361-387. DOI: 10.1007/s10648-019-09469-1
9. Chiu, P.-S., Chen, H.-C., Huang, Y.-M., Liu, C.-J., Liu, M.-C., & Shen, M.-H. (2018). A video annotation learning approach to improve the effects of video learning. Innovations in Education and Teaching International, 55(4), 459-469. DOI: 10.1080/14703297.2016.1213653
10. Choi, J. O., Shane, J. S., & Chih, Y.-Y. (2022). Diversity and inclusion in the engineering-construction industry. Journal of Management in Engineering, 38(2). DOI: 10.1061/(ASCE)ME.1943-5479.0001005
11. De Koning, B. B., Tabbers, H. K., Rikers, R. M., & Paas, F. (2009). Towards a framework for attention cueing in instructional animations: Guidelines for research and design. Educational Psychology Review, 21, 113-140. DOI: 10.1007/s10648-009-9098-7
12. Dousay, T. A., & Trujillo, N. P. (2019). An examination of gender and situational interest in multimedia learning environments. British Journal of Educational Technology, 50(2), 876-887. DOI: 10.1111/bjet.12610
13. 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. DOI: 10.1080/15578771.2017.1375050
14. Ferreira, C., Baptista, M., & Arroio, A. (2013). Teachers’ pedagogical strategies for integrating multimedia tools in science teaching. Journal of Baltic Science Education, 12(4), 509. https://pdfs.semanticscholar.org/5124/0abc5a17c7c8ee0c390904f2747e50992f0b.pdf
15. Grimley, M. (2007). Learning from multimedia materials: The relative impact of individual differences. Educational Psychology, 27(4), 465-485. DOI: 10.1080/01443410601159795
16. Gunhan, S. (2015). Collaborative learning experience in a construction project site trip. Journal of Professional Issues in Engineering Education and Practice, 141(1), 04014006. DOI: 10.1061/(ASCE)EI.1943-5541.0000207.
17. Hashimoto, D. A., Rosman, G., Witkowski, E. R., Stafford, C., Navarrete-Welton, A. J., Rattner, D. W., Lillemoe, K. D., Rus, D. L., & Meireles, O. R. (2019). Computer vision analysis of intraoperative video: automated recognition of operative steps in laparoscopic sleeve gastrectomy. Annals of surgery, 270(3), 414. DOI: 10.1097/SLA.0000000000003460
18. Homer, B. D., Plass, J. L., & Blake, L. (2008). The effects of video on cognitive load and social presence in multimedia-learning. Computers in Human Behavior, 24(3), 786-797. DOI: 10.1016/j.chb.2007.02.009
19. Kalyuga, S. (2013). Effects of learner prior knowledge and working memory limitations on multimedia learning. Procedia-Social and Behavioral Sciences, 83, 25-29. DOI: 10.1016/j.sbspro.2013.06.005
20. Kalyuga, S., Chandler, P., & Sweller, J. (2000). Incorporating learner experience into the design of multimedia instruction. Journal of Educational Psychology, 92(1), 126. DOI: 10.1037/0022-0663.92.1.126
21. Kelley, K., Clark, B., Brown, V., & Sitzia, J. (2003). Good practice in the conduct and reporting of survey research. International Journal for Quality in health care, 15(3), 261-266. DOI: 10.1093/intqhc/mzg031
22. Latifzadeh, K., Amiri, S., Bosaghzadeh, A., Rahimi, M., & Ebrahimpour, R. (2020). Evaluating cognitive load of multimedia learning by eye-tracking data analysis. Technology of Education Journal (TEJ), 15(1), 33-50. DOI: 10.22061/tej.2020.6237.2368
23. Law, E. L.-C., Mattheiss, E. E., Kickmeier-Rust, M. D., & Albert, D. (2010, November 4-5, 2010.). Vicarious learning with a digital educational game: Eye-tracking and survey-based evaluation approaches. 6th Symposium of the Workgroup Human-Computer Interaction and Usability Engineering, USAB 2010., Klagenfurt, Austria. DOI: 10.1007/978-3-642-16607-5_33
24. Mayer, R. E., & Fiorella, L. (2014). 12 principles for reducing extraneous processing in multimedia learning: Coherence, signaling, redundancy, spatial contiguity, and temporal contiguity principles. In The Cambridge handbook of multimedia learning (Vol. 279). Cambridge University Press New York, NY. DOI: 10.1017/CBO9781139547369.015
25. Molina, A. I., Navarro, Ó., Ortega, M., & Lacruz, M. (2018). Evaluating multimedia learning materials in primary education using eye tracking. Computer Standards & Interfaces, 59, 45-60. DOI: 10.1016/j.csi.2018.02.004
26. Moreno, R., & Flowerday, T. (2006). Students’ choice of animated pedagogical agents in science learning: A test of the similarity-attraction hypothesis on gender and ethnicity. Contemporary educational psychology, 31(2), 186-207. DOI: 10.1016/j.cedpsych.2005.05.002
27. Navarro, O., Molina, A. I., Lacruz, M., & Ortega, M. (2015). Evaluation of multimedia educational materials using eye tracking. Procedia-Social and Behavioral Sciences, 197, 2236-2243. DOI: 10.1016/j.sbspro.2015.07.366
28. Olayiwola, J., Akanmu, A., Gao, X., Murzi, H., & Afsari, K. (2023). Design and Usability Evaluation of an Annotated Video–Based Learning Environment for Construction Engineering Education. Journal of Computing in Civil Engineering, 37(6), 04023033. DOI: 10.1061/JCCEE5.CPENG-5206
29. Olayiwola, J., Yusuf, A. O., Akanmu, A. A., Murzi, H., Gao, X., & Afsari, K. (2023). Construction practice knowledge for complementing classroom teaching during site visits. Smart and Sustainable Built Environment, Ahead-of-print(Ahead-of-print), Ahead-of-print. DOI: 10.1108/SASBE-07-2022-0144
30. Pedro, A., Le, Q. T., & Park, C. S. (2016). Framework for integrating safety into construction methods education through interactive virtual reality. Journal of Professional Issues in Engineering Education and Practice, 142(2), 04015011. DOI: 10.1061/(ASCE)EI.1943-5541.0000261
31. Poonja, H. A., Shirazi, M. A., Khan, M. J., & Javed, K. (2023). Engagement detection and enhancement for STEM education through computer vision, augmented reality, and haptics. Image and Vision Computing, 142(2), 104720. DOI: 10.1016/j.imavis.2023.104720
32. Refat, N., Kassim, H., & Rahman, M. A. (2020). A cognitive approach-based instructional design for managing cognitive load and improving learning outcome. 2020 Emerging Technology in Computing, Communication and Electronics (ETCCE), Bangladesh. DOI: 10.1109/ETCCE51779.2020.9350864
33. Saha, S., & Halder, S. (2016). He or She: Does gender affect various modes of instructional visual design? Journal of Research on Women and Gender, 7(1), 47-58. https://hdl.handle.net/10877/12891
34. Savov, T., Terzieva, V., & Todorova, K. (2018). Computer vision and internet of things: Attention system in educational context. Proceedings of the 19th International Conference on Computer Systems and Technologies, DOI: 10.1145/3274005.3274014
35. Sharek, D. (2011). A useable, online NASA-TLX tool. Proceedings of the human factors and ergonomics society annual meeting, DOI: 10.1177/107118131155128
36. Shojaei, A., Rokooei, S., Mahdavian, A., Carson, L., & Ford, G. (2021). Using immersive video technology for construction management content delivery: a pilot study. J. Inf. Technol. Constr., 26, 886-901. DOI: 10.36680/j.itcon.2021.047
37. Sophokleous, A., Christodoulou, P., Doitsidis, L., & Chatzichristofis, S. A. (2021). Computer vision meets educational robotics. Electronics, 10(6), 730. DOI: 10.3390/electronics10060730
38. Stark, L., Brünken, R., & Park, B. (2018). Emotional text design in multimedia learning: A mixed-methods study using eye tracking. Computers & Education, 120, 185-196. DOI: 10.1016/j.compedu.2018.02.003
39. Tang, S., Roberts, D., & Golparvar-Fard, M. (2020). Human-object interaction recognition for automatic construction site safety inspection. Automation in Construction, 120, 103356. DOI: 10.1016/j.autcon.2020.103356
40. Tetiana, M., Kondratenko, Y., Sidenko, I., & Kondratenko, G. (2021). Computer vision mobile system for education using augmented reality technology. Journal of Mobile Multimedia, 17(4), 555–576. DOI: 10.13052/jmm1550-4646.1744
41. Tudić, V., Stančić, A., Kralj, D., & Tropčić, T. (2022). Application of Computer Vision in Education in Mechatronic Control System. 2022 ELEKTRO (ELEKTRO), Krakow, Poland. DOI: 10.1109/ELEKTRO53996.2022.9803616
42. Zhang, H., Kyaw, Z., Chang, S.-F., & Chua, T.-S. (2017). Visual translation embedding network for visual relation detection. Proceedings of the IEEE conference on computer vision and pattern recognition, http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_Visual_Translation_Embedding_CVPR_2017_paper.pdf
43. Zhao, Q., & Li, Z. (2021). Application of computer vision media simulation technology in distance education of new generation labor productivity. Journal of Physics: Conference Series, DOI: 10.1088/1742-6596/1992/4/042044

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