Unveiling cognitive qualities: Evaluating popular YouTube videos for physics education

Abstract

YouTube has revolutionized learning by providing access to instructional videos; however, concerns about video quality and instructional effectiveness persist. Despite the popularity of instructional YouTube videos, limited research exists on their cognitive characteristics, particularly in science education. This knowledge gap hampers effective video selection/integration into educational settings. This study aims to identify the association between such videos’ cognitive quality and their popularity, as measured by the Video Power Index, along with content and duration. The goal is to provide comparative information and better means to guide educators in video selection. To achieve this, the study proposes using the Multimedia Principles Rubric (MPR) as a filtering tool to assess cognitive quality based on instructional and multimedia content. A multistage cluster sampling method was employed to select the 60 most-viewed YouTube videos from five content areas of physics, considering three ranges of video length and a specific online age. Regression analysis revealed that the mean MPR could be weakly predicted from popularity. Additionally, three-to-six-minute videos demonstrated better cognitive quality, while six-to-twenty-minute videos were more popular. ANOVAs indicated that cognitive quality was significantly dependent on content and video length. The results shed light on the cognitive landscape of such videos, providing insights. Popularity alone is not a good measure of cognitive quality. It is important to assess the cognitive characteristics of instructional science videos. Integrating online videos into courses requires consideration of instructional design principles and cognitive processes. Careful evaluation using the MPR can help select high-quality instructional resources for effective science education.

Keywords

• filtering tool
• cognitive
• instructional video
• video popularity
• rubric
• YouTube

References

1. Abdulrahaman, M. D., Faruk, N., Oloyede, A. A., Surajudeen-Bakinde, N. T., Olawoyin, L. A., Mejabi, O. V., Imam-Fulani, Y. O., Fahm, A. O., & Azeez, A. L. (2020). Multimedia tools in the teaching and learning processes: A systematic review. Heliyon, 6(11), e05312. doi: 10.1016/j.heliyon.2020.e05312
2. Allchin, D. (2017). From test tubes to YouTube: Nature of science in socioscientific issues and history. In epiSTEME 6: Proceedings of the Sixth International Conference to Review Research in Science, Technology and Mathematics Education (pp. 1–4). Homi Bhabha Centre for Science Education. https://douglasallchin.net/papers/Allchin-2017-Test-tubes-to-YouTube.pdf
3. Barlas, T., Ecem Avci, D., Cinici, B., Ozkilicaslan, H., Muhittin Yalcin, M., & Eroglu Altinova, A. (2023). The quality and reliability analysis of YouTube videos about insulin resistance. International Journal of Medical Informatics, 170, 104960. doi: 10.1016/j.ijmedinf.2022.104960
4. Bengfort, J. (2019, April 8). How K–12 schools can use next-generation content filtering to keep students safe. EdTech: Focus on K–12, Security. https://edtechmagazine.com/k12/article/2019/04/how-k-12-schools-can-use-next-generation-content-filtering-keep-students-safe-perfcon
5. Berk, R. A. (2009). Multimedia teaching with video clips: TV, movies, YouTube, and mtvU in the college classroom. International Journal of Technology in Teaching & Learning, 5(1), 1–21.
6. Bozarth, J. (2021). Video Viewer Study 2021: Why people start, stop, and keep watching instructional and informational videos. TechSmith, Okemos, MI, USA. https://www.techsmith.com/blog/video-statistics
7. Brame, C. J. (2016). Effective educational videos: Principles and guidelines for maximizing student learning from video content. CBE—Life Sciences Education, 15(4), es6. doi: 10.1187/cbe.16-03-0125
8. Chandra, V., & Watters, J. J. (2012). Re-thinking physics teaching with web-based learning. Computers & Education, 58(1), 631–640. doi: 10.1016/j.compedu.2011.09.010

9. Erdem, M. N., & Karaca, S. (2018). Evaluating the accuracy and quality of the information in kyphosis videos shared on YouTube. Spine, 43(22), E1334–E1339. doi: 10.1097/BRS.0000000000002691
10. Fiorella, L., & Mayer, R. E. (2018). What works and doesn’t work with instructional video. Computers in Human Behavior, 89, 465–470. doi: 10.1016/j.chb.2018.07.015
11. Fischer, G., Lundin, J., & Lindberg, O. J. (2023). The challenge for the digital age: Making learning a part of life. The International Journal of Information and Learning Technology, 40(1), 1–16. doi: 10.1108/IJILT-04-2022-0079
12. Fishman, E. (2023, May 11). Optimal video length: How long should a marketing video be? Learning Center - Marketing. Wistia. https://wistia.com/learn/marketing/optimal-video-length
13. Ford, C. ( 2018, August 17). Interpreting log transformations in a linear model. University of Virginia Library, Research Data Services + Sciences. Retrieved July 6 from https://data.library.virginia.edu/interpreting-log-transformations-in-a-linear-model/
14. Frick, T. W. (2020). Education systems and technology in 1990, 2020, and beyond. TechTrends, 64(5), 693–703. doi: 10.1007/s11528-020-00527-y
15. Galatsopoulou, F., Kenterelidou, C., Kotsakis, R., & Matsiola, M. (2022). Examining students’ perceptions towards video-based and video-assisted active learning scenarios in journalism and communication courses. Education Sciences, 12(2), 74. doi: 10.3390/educsci12020074
16. Guo, P. J., Kim, J., & Rubin, R. (2014). How video production affects student engagement: an empirical study of MOOC videos. Proceedings of the First ACM Conference on Learning @ Scale Conference, Atlanta, Georgia, USA. doi: 10.1145/2556325.2566239
17. Gustafsson, P. (2013). How physics teaching is presented on YouTube videos. Educational Research for Social Change, 2(1), 117–129. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-20881
18. Haridakis, P., & Hanson, G. (2009). Social interaction and co-viewing with YouTube: Blending mass communication reception and social connection. Journal of Broadcasting & Electronic Media, 53(2), 317–335. doi: 10.1080/08838150902908270
19. Hsin, W.-J., & Cigas, J. (2013). Short videos improve student learning in online education. Journal of Computing Sciences in Colleges, 28(5), 253–259.
20. Jian-hua, S., & hong, L. (2012). Explore the effective use of multimedia technology in college physics teaching. Energy Procedia, 17(Part B), 1897–1900. doi: 10.1016/j.egypro.2012.02.329
21. Kay, R. H. (2012). Exploring the use of video podcasts in education: A comprehensive review of the literature. Computers in Human Behavior, 28(3), 820–831. doi: 10.1016/j.chb.2012.01.011
22. Kay, R. H. (2014). Developing a framework for creating effective instructional video podcasts. International Journal of Emerging Technologies in Learning (iJET), 9(1), 22-30. doi: 10.3991/ijet.v9i1.3335
23. Khan, M. L. (2017). Social media engagement: What motivates user participation and consumption on YouTube? Computers in Human Behavior, 66, 236–247. doi: 10.1016/j.chb.2016.09.024
24. Knott, R. (n.d.). Video length: How long should instructional videos be? (New data). Research & Guides. TechSmith Corp. https://www.techsmith.com/blog/video-length/
25. Koehler, M., & Mishra, P. (2009). What is Technological Pedagogical Content Knowledge (TPACK)? Contemporary Issues in Technology and Teacher Education, 9(1), 60–70.
26. Kunze, K. N., Cohn, M. R., Wakefield, C., Hamati, F., LaPrade, R. F., Forsythe, B., Yanke, A. B., & Chahla, J. (2019). YouTube as a source of information about the posterior cruciate ligament: A content-quality and reliability analysis. Arthroscopy, Sports Medicine, and Rehabilitation, 1(2), e109–e114. doi: 10.1016/j.asmr.2019.09.003
27. Kuru, T., & Erken, H. Y. (2020). Evaluation of the quality and reliability of YouTube videos on rotator cuff tears. Cureus, 12(2), e6852. doi: 10.7759/cureus.6852
28. Lee, D. Y., & Lehto, M. R. (2013). User acceptance of YouTube for procedural learning: An extension of the Technology Acceptance Model. Computers & Education, 61, 193–208. doi: 10.1016/j.compedu.2012.10.001
29. Lloyd, S. A., & Robertson, C. L. (2012). Screencast tutorials enhance student learning of statistics. Teaching of Psychology, 39(1), 67–71. doi: 10.1177/0098628311430640
30. Martinez, V. H., Ojo, D., Gutierrez-Naranjo, J. M., Proffitt, M., & Hartzler, R. U. (2023). The most popular YouTube videos about shoulder replacement are of poor quality for patient education. Arthroscopy, Sports Medicine, and Rehabilitation, 5(3), e623–e628. doi: 10.1016/j.asmr.2023.03.001
31. Mayer, R. E. (2014). Cognitive Theory of Multimedia Learning. In R. E. Mayer (Ed.), The Cambridge Handbook of Multimedia Learning (2 ed., pp. 43–71). Cambridge University Press. doi: 10.1017/CBO9781139547369.005
32. Merrill, D. M. (2008, August 11). Merrill on instructional design [Video]. YouTube. https://www.youtube.com/watch?v=i_TKaO2-jXA
33. Moore, W. A., & Smith, A. R. (2012). Effects of video podcasting on psychomotor and cognitive performance, attitudes and study behaviour of student physical therapists. Innovations in Education and Teaching International, 49(4), 401–414. doi: 10.1080/14703297.2012.728876
34. Mthembu, S., & Roodt, S. (2017, July 19–21). Engaging the net generation via YouTube: An academic approach for undergraduate teaching. 2017 1st International Conference on Next Generation Computing Applications (NextComp), Mauritius. doi: 10.1109/NEXTCOMP.2017.8016180
35. Muller, D. (2014, December 1). The most persistent myth [Video]. YouTube. https://www.youtube.com/watch?v=GEmuEWjHr5c
36. Ng, Y. M. M., & Taneja, H. (2023). Web use remains highly regional even in the age of global platform monopolies. PLoS One, 18(1), e0278594. doi: 10.1371/journal.pone.0278594
37. Orús, C., Barlés, M. J., Belanche, D., Casaló, L., Fraj, E., & Gurrea, R. (2016). The effects of learner-generated videos for YouTube on learning outcomes and satisfaction. Computers & Education, 95, 254–269. doi: 10.1016/j.compedu.2016.01.007
38. Pires, F., Masanet, M.-J., Tomasena, J. M., & Scolari, C. A. (2022). Learning with YouTube: Beyond formal and informal through new actors, strategies and affordances. Convergence, 28(3), 838–853. doi: 10.1177/13548565211020545
39. Rackaway, C. (2012). Video killed the textbook star?: Use of multimedia supplements to enhance student learning. Journal of Political Science Education, 8(2), 189–200. doi: 10.1080/15512169.2012.667684
40. Resurreccion, R. D. (2014). The effects of using videos on teaching selected topics in physics towards the development of higher-order thinking skills. Asia Pacific Journal of Multidisciplinary Research, 2(5), 38–45.
41. Schultz, D., Duffield, S., Rasmussen, S. C., & Wageman, J. (2014). Effects of the flipped classroom model on student performance for advanced placement high school chemistry students. Journal of Chemical Education, 91(9), 1334–1339. doi: 10.1021/ed400868x
42. Serway, R. A., & Kirkpatrick, L. D. (1988). Physics for scientists and engineers with modern physics. The Physics Teacher, 26(4), 254–255. doi: 10.1119/1.2342517
43. Shoufan, A. (2019). Estimating the cognitive value of YouTube’s educational videos: A learning analytics approach. Computers in Human Behavior, 92, 450–458. doi: 10.1016/j.chb.2018.03.036
44. Stockwell, B. R., Stockwell, M. S., Cennamo, M., & Jiang, E. (2015). Blended Learning Improves Science Education. Cell, 162(5), 933-936. doi: 10.1016/j.cell.2015.08.009
45. Taher, F. (2021). Analyzing physics instructional videos on YouTube with respect to the Cognitive Theory of Multimedia Learning [Master’s Thesis, Bahcesehir University]. Istanbul.
46. Ten Hove, P. E. (2014). Characteristics of instructional videos for conceptual knowledge development [Master’s Thesis, University of Twente].
47. Ten Hove, P. E., & Van der Meij, H. (2015). Like it or not. What characterizes YouTube’s more popular instructional videos? Technical Communication, 62(1), 48–62.
48. Yurdaisik, I. (2020). Analysis of the most viewed first 50 videos on YouTube about breast cancer. BioMed Research International, 2020, 2750148. doi: 10.1155/2020/2750148