DOI QR코드

DOI QR Code

Interactive Video Player for Supporting Learner Engagement in Video-Based Online Learning

  • Received : 2022.08.26
  • Accepted : 2022.10.10
  • Published : 2022.10.30

Abstract

This study sought to design and develop an interactive video player (IVP) capable of promoting student engagement through the use of online video content. We designed features built upon interactive, constructive, active, passive (ICAP), and crowd learning frameworks. In the development stage of this study, we integrated numerous interactive features into the IVP intended to help learners shift from passive to interactive learning activities. We then explored the effectiveness and usability of the developed IVP by conducting an experiment in which we evaluated students' exam scores after using either our IVP or a conventional video player. There were 158 college students who participated in the study; 76 students in the treatment group used the IVP and 82 students in the control group used a conventional video player. Results indicate that the participants in the experiment group demonstrated better achievement than the participants in the control group. We further discuss the implications of this study based on an additional survey that was administered to disclose how usable the participants perceived the IVP to be.

Keywords

Acknowledgement

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2020S1A5C2A04092451).

References

  1. Anderson, M., & Magruder, J. (2012). Learning from the crowd: Regression discontinuity estimates of the effects of an online review database. The Economic Journal, 122(563), 957-989. https://doi.org/10.1111/j.1468-0297.2012.02512.x
  2. Angeli, C., Valanides, N., & Bonk, C. J. (2003). Communication in a web-based conferencing system: The quality of computer-mediated interactions. British Journal of Educational Technology, 34(1), 31-43. https://doi.org/10.1111/1467-8535.00302
  3. Angrave, L., Zhang, Z., Henricks, G., & Mahipal, C. (2020). Who benefits? Positive learner outcomes from behavioral analytics of online lecture video viewing using classtranscribe. Proceedings of the 51st ACM Technical Symposium on Computer Science Education, 1193-1199. https://doi.org/10.1145/3328778.3366953
  4. Ashuri, T., Dvir-Gvisman, S., & Halperin, R. (2018). Watching me watching you: How observational learning affects self-disclosure on social network sites?. Journal of Computer-Mediated Communication, 23(1), 34-68. https://doi.org/10.1093/jcmc/zmx003
  5. Bandura, A. (2008). Observational learning. The international encyclopedia of communication
  6. Chatti, M. A., Marinov, M., Sabov, O., Laksono, R., Sofyan, Z., Yousef, A. M. F., & Schroeder, U. (2016). Video annotation and analytics in CourseMapper. Smart Learning Environments, 3(1), 1-21. https://doi.org/10.1186/s40561-016-0035-1.
  7. Chen, H. C., O'Sullivan, P., Teherani, A., Fogh, S., Kobashi, B., & Ten Cate, O. (2015). Sequencing learning experiences to engage different level learners in the workplace: An interview study with excellent clinical teachers. Medical teacher, 37(12), 1090-1097. https://doi.org/10.3109/0142159X.2015.1009431
  8. Chi, M. T. (2009). Active-constructive-interactive: A conceptual framework for differentiating learning activities. Topics in cognitive science, 1(1), 73-105. https://doi.org/10.1111/j.1756-8765.2008.01005.x
  9. Chi, M. T., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational psychologist, 49(4), 219-243. https://doi.org/10.1080/00461520.2014.965823
  10. Cho, M. H., & Kim, B. J. (2013). Students' self-regulation for interaction with others in online learning environments. The Internet and Higher Education, 17, 69-75. https:// doi.org/10.1016/j.iheduc.2012.11.001.
  11. Chou, S. W., & Liu, C. H. (2005). Learning effectiveness in a web-based virtual learning environment: A learner control perspective. Journal of computer assisted learning, 21(1), 65-76. https://doi.org/10.1111/j.1365-2729.2005.00114.x
  12. Clarke, T. (2013). The advance of the MOOCs (massive open online courses). Education+ Training. https://doi.org/10.1108/00400911311326036
  13. Davis, D., Jivet, I., Kizilcec, R. F., Chen, G., Hauff, C., & Houben, G. J. (2017). Follow the successful crowd: Raising MOOC completion rates through social comparison at scale. Proceedings of the seventh international learning analytics & knowledge conference, 454-463. https://doi.org/10.1145/3027385.3027411
  14. de Barba, P. G., Kennedy, G. E., & Ainley, M. D. (2016). The role of students' motivation and participation in predicting performance in a MOOC. Journal of Computer Assisted Learning, 32(3), 218-231. https://doi.org/10.1111/jcal.12130
  15. Doan, A., Ramakrishnan, R., & Halevy, A.Y. (2011), "Crowdsourcing systems on the World-Wide Web", Communications of the ACM, 54(4), 86-96. https://doi.org/10.1145/1924421.1924442
  16. Dron, J., & Anderson, T. (2009). How the crowd can teach. In S. Hatzipanagos & S. Warburton (Eds.), Handbook of research on social software and developing community ontologies (pp. 1-17). IGI Global Information Science. www.igiglobal.com/downloads/excerpts/33011.pdf.
  17. Dumford, A. D., & Miller, A. L. (2018). Online learning in higher education: Exploring advantages and disadvantages for engagement. Journal of Computing in Higher Education, 30(3), 452-465. https://doi.org/10.1007/s12528-018-9179-z
  18. Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students' learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14(1), 4-58. https://doi.org/10.1177/1529100612453266
  19. Eisenberg, M. & Fischer, G. (2014). MOOCs: A Perspective from the Learning Sciences. In J. L. Polman, E. A. Kyza, D. K. O'Neill, I. Tabak, W. R. Penuel, A. S. Jurow, K. O'Connor, T. Lee & L. D'Amico (Eds.), Learning and Becoming in Practice: The International Conference of the Learning Sciences (ICLS) (Vol. 1, pp. 190-197). International Society of the Learning Sciences.
  20. Fiorella, L., & Mayer, R. E. (2018). What works and doesn't work with instructional video. Computers in Human Behavior, 89, 465-470. https://doi.org/10.1016/j.chb.2018.07.015
  21. Gasevic, D., Joksimovic, S., Eagan, B. R., & Shaffer, D. W. (2019). SENS: Network analytics to combine social and cognitive perspectives of collaborative learning. Computers in Human Behavior, 92, 562-577. https://doi.org/10.1016/j.chb.2018.07.003
  22. 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, 41-50. https://doi.org/10.1145/2556325.2566239
  23. Giannakos, M. N., Sampson, D. G., & Kidzinski, L. (2016). Introduction to smart learning analytics: Foundations and developments in video-based learning. Smart Learning Environments, 3(1), 1-9. https://doi.org/ 10.1186/s40561-016-0034-2
  24. Halverson, L. R., & Graham, C. R. (2019). Learner engagement in blended learning environments: A conceptual framework. Online Learning, 23(2), 145-178.
  25. Hampton, D., & Pearce, P. F. (2016). Student engagement in online nursing courses. Nurse educator, 41(6), 294-298. https://doi.org/10.1097/NNE.0000000000000275
  26. Howe, J. (2008). Crowdsourcing: How the power of the crowd is driving the future of business. Random House.
  27. Hsin, W. J., & Cigas, J. (2013). Short videos improve student learning in online education. Journal of Computing Sciences in Colleges, 28(5), 253-259.
  28. Hung, I. C., & Chen, N. S. (2018). Embodied interactive video lectures for improving learning comprehension and retention. Computers & Education, 117, 116-131. https://doi.org/10.1016/j.compedu.2017.10.005
  29. Iglesias-Pradas, S., Hernandez-Garcia, A., Chaparro-Pelaez, J., & Prieto, J. L. (2021). Emergency remote teaching and students' academic performance in higher education during the COVID-19 pandemic: A case study. Computers in Human Behavior, 119, 106713. https://doi.org/10.1016/j.chb.2021.106713
  30. Kalisz, D. E. (2016). Crowd learning: Innovative harnessing the knowledge and potential of people. Innovative management education pedagogies for preparing next-generation leaders, 55-74. 10.4018/978-1-4666-9691-4.ch004
  31. Kittur, A., Nickerson, J. V., Bernstein, M., Gerber, E., Shaw, A., Zimmerman, J., Lease, M., & Horton, J. (2013). The future of crowd work. Proceedings of the 2013 conference on Computer supported cooperative work, 1301-1318.
  32. Lucas, M., Gunawardena, C., & Moreira, A. (2014). Assessing social construction of knowledge online: A critique of the interaction analysis model. Computers in Human Behavior, 30, 574-582. https://doi.org/10.1016/j.chb.2013.07.050
  33. Martin, F., & Bolliger, D. U. (2018). Engagement matters: Student perceptions on the importance of engagement strategies in the online learning environment. Online Learning, 22(1), 205-222. doi:10.24059/olj.v22i1.1092
  34. Martin-Monje, E., Castrillo, M. D., & Manana-Rodriguez, J. (2018). Understanding online interaction in language MOOCs through learning analytics. Computer Assisted Language Learning, 31(3), 251-272. https://doi.org/10.1080/09588221.2017.1378237
  35. Martin-Ramos, P., Lopes, M. J., da Silva, M. M. L., Gomes, P. E., da Silva, P. S. P., Domingues, J. P., & Silva, M. R. (2018). Reprint of 'First exposure to Arduino through peer-coaching: Impact on students' attitudes towards programming'. Computers in Human Behavior, 80, 420-427. https://doi.org/10.1016/j.chb.2017.12.011
  36. Mierowsky, R., Marcus, N., & Ayres, P. (2020). Using mimicking gestures to improve observational learning from instructional videos. Educational Psychology, 40(5), 550-569. https://doi.org/10.1080/01443410.2019.1650896
  37. Mitrovic, A., Dimitrova, V., Lau, L., Weerasinghe, A., & Mathews, M. (2017). Supporting constructive video-based learning: requirements elicitation from exploratory studies. International Conference on Artificial Intelligence in Education, 224-237. https://doi.org/10.1007/978-3-319-61425-0_19
  38. Morgan, H. (2020). Best practices for implementing remote learning during a pandemic. The Clearing House: A Journal of Educational Strategies, Issues and Ideas, 93(3), 135-141. https://doi.org/10.1080/00098655.2020.1751480
  39. Nokelainen, P. (2005). The technical and pedagogical usability criteria for digital learning material. EdMedia + Innovative Learning, 1011-1016.
  40. Pellas, N. (2014). The influence of computer self-efficacy, metacognitive self-regulation and self-esteem on student engagement in online learning programs: Evidence from the virtual world of second life. Computers in Human Behavior, 35, 157-170. https:// doi.org/10.1016/j.chb.2014.02.048.
  41. Petrovcic, A., & Petric, G. (2014). Differences in intrapersonal and interactional empowerment between lurkers and posters in health-related online support communities. Computers in Human Behavior, 34, 39-48. https://doi.org/10.1016/j.chb.2014.01.008
  42. Rosenthal, T. L., & Zimmerman, B. J. (2014). Social learning and cognition. Academic Press.
  43. Sablic, M., Mirosavljevic, A., & Skugor, A. (2020). Video-based learning (VBL)-past, present and future: An overview of the research published from 2008 to 2019. Technology, Knowledge and Learning, 1-17. https://doi.org/10.1007/s10758-020-09455-5
  44. Seo, K., Dodson, S., Harandi, N. M., Roberson, N., Fels, S., & Roll, I. (2021). Active learning with online video: The impact of learning context on engagement. Computers & Education, 165, 104132. https://doi.org/10.1016/j.compedu.2021.104132
  45. Speily, O. R., & Kardan, A. A. (2018). Increasing information reposting behavior in online learning community. Journal of Educational Technology & Society, 21(4), 100-110.
  46. Surowiecki, J. (2005). Independent individuals and wise crowds. IT Conversations, 468. itc.conversationsnetwork.org/shows/detail468.html
  47. Stull, A. T., Fiorella, L., & Mayer, R. E. (2021). The case for embodied instruction: The instructor as a source of attentional and social cues in video lectures. Journal of Educational Psychology, 113(7), 1441. https://doi.org/10.1037/edu0000650
  48. Thomas, R. A., West, R. E., & Borup, J. (2017). An analysis of instructor social presence in online text and asynchronous video feedback comments. The Internet and Higher Education, 33, 61-73. https://doi.org/10.1016/j.iheduc.2017.01.003
  49. Woo, Y., & Reeves, T. C. (2007). Meaningful interaction in web-based learning: A social constructivist interpretation. The Internet and higher education, 10(1), 15-25. https://doi.org/10.1016/j.iheduc.2006.10.005
  50. Vukovic M, Mariana L, & Laredo J. (2010). PeopleCloud for the globally integrated enterprise. In A. Dan, F. Gittler & F. Toumani (Eds.), Service-oriented computing (pp. 109-114). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16132-2_10
  51. Vytasek, J. M., Patzak, A., & Winne, P. H. (2020). Analytics for student engagement. In M. Virvou, E. Alepis, G. Tsihrintzis & L. Jain (Eds.), Machine learning paradigms (Vol. 158, pp. 23-48). Springer. https://doi.org/10.1007/978-3-030-13743-4_3
  52. Zhang, D., Zhou, L., Briggs, R. O., & Nunamaker Jr, J. F. (2006). Instructional video in e-learning: Assessing the impact of interactive video on learning effectiveness. Information & management, 43(1), 15-27. https://doi.org/10.1016/j.im.2005.01.004