Skip to main content
SpringerLink
Log in

Discovering Differences in Learning Behaviours During Active Video Watching Using Epistemic Network Analysis

  • Conference paper
  • First Online:
Advances in Quantitative Ethnography (ICQE 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1522))

Included in the following conference series:

Abstract

AVW-Space is an online video-based learning platform that aims to improve engagement by providing a note-taking environment, personalised support and peer-reviewing. The effectiveness of AVW-Space in supporting active video watching has been evaluated in several studies, using quantitative analyses of learning outcomes and engagement based on student logs. However, there have been no qualitative analyses on the longitudinal data of student interactions. This paper uses Epistemic Network Analysis (ENA) to identify behavioural differences in video-based learning. We first investigate how students interact with the platform and then compare the interactions and performance of students who started late with the early starters. The work presented in this paper demonstrates the potentials of applying ENA in understanding learning behaviours and evaluating the effectiveness of educational support in computer-based learning environments more comprehensively.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Yousef, A.M.F., Chatti, M.A., Schroeder, U.: The state of video-based learning: a review and future perspectives. Int. J. Adv. Life Sci. 6, 122–135 (2014)

    Google Scholar 

  2. Gilboy, M.B., Heinerichs, S., Pazzaglia, G.: Enhancing student engagement using the flipped classroom. J. Nutr. Educ. Behav. 47, 109–114 (2015)

    Article  Google Scholar 

  3. Zhang, H., Miller, K.F., Sun, X., Cortina, K.S.: Wandering eyes: eye movements during mind wandering in video lectures. Appl. Cogn. Psychol. 34, 449–464 (2020)

    Article  Google Scholar 

  4. Mitrovic, A., Dimitrova, V., Weerasinghe, A., Lau, L.: Reflective experiential learning: using active video watching for soft skills training. In: Proceedings of 24th International Conference Computers in Education, pp. 192–201. Asia-Pacific Society for Computers in Education (2016)

    Google Scholar 

  5. Mitrovic, A., Gordon, M., Piotrkowicz, A., Dimitrova, V.: Investigating the effect of adding nudges to increase engagement in active video watching. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds.) AIED 2019. LNCS (LNAI), vol. 11625, pp. 320–332. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-23204-7_27

    Chapter  Google Scholar 

  6. Mohammadhassan, N., Mitrovic, A., Neshatian, K., Dunn, J.: Investigating the effect of nudges for improving comment quality in active video watching. Comput. Educ. 176, 104340 (2022). https://doi.org/10.1016/j.compedu.2021.104340

    Article  Google Scholar 

  7. Mitrovic, A., Dimitrova, V., Lau, L., Weerasinghe, A., Mathews, M.: Supporting constructive video-based learning: requirements elicitation from exploratory studies. In: André, E., Baker, R., Hu, X., Rodrigo, M.M.T., du Boulay, B. (eds.) AIED 2017. LNCS (LNAI), vol. 10331, pp. 224–237. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61425-0_19

    Chapter  Google Scholar 

  8. Chi, M.T.H., Wylie, R.: The ICAP framework: linking cognitive engagement to active learning outcomes. Educ. Psychol. 49, 219–243 (2014)

    Article  Google Scholar 

  9. Caglayan, E., Ustunluoglu, E.: A Study exploring students’ usage patterns and adoption of lecture capture. Technol. Knowl. Learn. 26(1), 13–30 (2020). https://doi.org/10.1007/s10758-020-09435-9

    Article  Google Scholar 

  10. Giannakos, M., Jaccheri, L., Krogstie, J.: Exploring the relationship between video lecture usage patterns and students’ attitudes. Brit. J. Educ. Technol. 47, 1259–1275 (2015)

    Article  Google Scholar 

  11. Lallé, S., Conati, C.: A Data-Driven Student Model to Provide Adaptive Support During Video Watching Across MOOCs. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12163, pp. 282–295. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52237-7_23

    Chapter  Google Scholar 

  12. Paquette, L., Grant, T., Zhang, Y., Biswas, G., Baker, R.: Using epistemic networks to analyze self-regulated learning in an open-ended problem-solving environment. In: Ruis, A.R., Lee, S.B. (eds.) ICQE 2021. CCIS, vol. 1312, pp. 185–201. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67788-6_13

    Chapter  Google Scholar 

  13. Gasevic, D., Jovanovic, J., Pardo, A., Dawson, S.: Detecting learning strategies with analytics: links with self-reported measures and academic performance. Learn. Anal. 4, 113–128 (2017)

    Google Scholar 

  14. Zhou, J., Bhat, S.: Modeling consistency using engagement patterns in online courses. In: LAK21: 11th International Learning Analytics and Knowledge Conference, pp. 226–236. Association for Computing Machinery, New York (2021)

    Google Scholar 

  15. Shabaninejad, S., Khosravi, H., Leemans, S.J.J., Sadiq, S., Indulska, M.: Recommending insightful drill-downs based on learning processes for learning analytics dashboards. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12163, pp. 486–499. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52237-7_39

    Chapter  Google Scholar 

  16. Shaffer, D.W., Collier, W., Ruis, A.R.: A tutorial on epistemic network analysis: analyzing the structure of connections in cognitive, social, and interaction data. Learn. Anal. 3, 9–45 (2016)

    Article  Google Scholar 

  17. Gamage, D., Perera, I., Fernando, S.: Exploring MOOC user behaviors beyond platforms. Int. J. Emerg. Technol. Learn. 15, 161–179 (2020)

    Article  Google Scholar 

  18. Saint, J., Gašević, D., Matcha, W., Uzir, N.A., Pardo, A.: Combining analytic methods to unlock sequential and temporal patterns of self-regulated learning. In: Proceedings of 10th International Conference Learning Analytics & Knowledge, pp. 402–411. ACM, New York (2020)

    Google Scholar 

  19. Scianna, J., Gagnon, D., Knowles, B.: Counting the game: visualizing changes in play by incorporating game events. In: Ruis, A.R., Lee, S.B. (eds.) ICQE 2021. CCIS, vol. 1312, pp. 218–231. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67788-6_15

    Chapter  Google Scholar 

  20. Karumbaiah, S., Baker, R.S., Barany, A., Shute, V.: Using epistemic networks with automated codes to understand why players quit levels in a learning game. In: Eagan, B., Misfeldt, M., Siebert-Evenstone, A. (eds.) ICQE 2019. CCIS, vol. 1112, pp. 106–116. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33232-7_9

    Chapter  Google Scholar 

  21. Dimitrova, V., Mitrovic, A., Piotrkowicz, A., Lau, L., Weerasinghe, A.: Using learning analytics to devise interactive personalised nudges for active video watching. In: Proceedings of 25th Conference User Modeling, Adaptation and Personalization, pp. 22–31. ACM (2017)

    Google Scholar 

  22. Mohammadhassan, N., Mitrovic, A., Neshatian, K., Dunn, J.: Automatic assessment of comment quality in active video watching. In: Proceedings of 28th International Conference Computers in Education, pp. 1–10. Asia-Pacific Society for Computers in Education. (2020)

    Google Scholar 

  23. Pintrich, P.R., de Groot, E.V.: Motivational and self-regulated learning components of classroom academic performance. J. Educ. Psychol. 82, 33–40 (1990)

    Article  Google Scholar 

  24. Marquart, C.L., Hinojosa, C., Swiecki, Z., Eagan, B., Shaffer, D.W.: Epistemic Network Analysis (Version 1.7.0) [Software] (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Canterbury, Christchurch, New Zealand

    Negar Mohammadhassan & Antonija Mitrovic

Authors
  1. Negar Mohammadhassan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Antonija Mitrovic
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Negar Mohammadhassan .

Editor information

Editors and Affiliations

  1. University of Bergen, Bergen, Norway

    Barbara Wasson

  2. Semmelweis University, Budapest, Hungary

    Szilvia Zörgő

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mohammadhassan, N., Mitrovic, A. (2022). Discovering Differences in Learning Behaviours During Active Video Watching Using Epistemic Network Analysis. In: Wasson, B., Zörgő, S. (eds) Advances in Quantitative Ethnography. ICQE 2021. Communications in Computer and Information Science, vol 1522. Springer, Cham. https://doi.org/10.1007/978-3-030-93859-8_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-93859-8_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93858-1

  • Online ISBN: 978-3-030-93859-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation