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Erschienen in:
Constructing Interpretations with Participants Through Epistemic Network Analysis: Towards Participatory Approaches in Quantitative Ethnography Kapitel lesen Erstes Kapitel lesen

2022 | OriginalPaper | Buchkapitel

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

verfasst von : Negar Mohammadhassan, Antonija Mitrovic

Erschienen in: Advances in Quantitative Ethnography

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Exploring Interactions Between Computational and Critical Thinking in Model-Eliciting Activities Through Epistemic Network Analysis
Nächstes Kapitel Exploring Group Discussion with Conversational Agents Using Epistemic Network Analysis
Literatur
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)
2.
Gilboy, M.B., Heinerichs, S., Pazzaglia, G.: Enhancing student engagement using the flipped classroom. J. Nutr. Educ. Behav. 47, 109–114 (2015)CrossRef
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)CrossRef
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)
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_​27CrossRef
6.
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_​19CrossRef
8.
Chi, M.T.H., Wylie, R.: The ICAP framework: linking cognitive engagement to active learning outcomes. Educ. Psychol. 49, 219–243 (2014)CrossRef
9.
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)CrossRef
11.
12.
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)
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)
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_​39CrossRef
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)CrossRef
17.
Gamage, D., Perera, I., Fernando, S.: Exploring MOOC user behaviors beyond platforms. Int. J. Emerg. Technol. Learn. 15, 161–179 (2020)CrossRef
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)
19.
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_​9CrossRef
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)
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)
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)CrossRef
24.
Marquart, C.L., Hinojosa, C., Swiecki, Z., Eagan, B., Shaffer, D.W.: Epistemic Network Analysis (Version 1.7.0) [Software] (2018)
Metadaten
Titel
Discovering Differences in Learning Behaviours During Active Video Watching Using Epistemic Network Analysis
verfasst von
Negar Mohammadhassan
Antonija Mitrovic
Copyright-Jahr
2022
Buch
Advances in Quantitative Ethnography

Print ISBN: 978-3-030-93858-1

Electronic ISBN: 978-3-030-93859-8

Copyright-Jahr: 2022

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

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