- Negar Mohammadhassan ORCID:orcid.org/0000-0003-4761-582211 &
- Antonija Mitrovic ORCID:orcid.org/0000-0003-0936-080611
Part of the book series:Lecture Notes in Computer Science ((LNCS,volume 13355))
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Abstract
Video-based Learning (VBL) is a popular form of online learning, which may lead to passive video watching and low learning outcomes. Besides potential low engagement, VBL often provides very limited feedback on student’s progress. As a way to overcome these challenges, we present student-facing visual learning analytics (VLA) designed for the AVW-Space VBL platform. Using a quasi-experimental design, we compared data collected in the same first-year university course in 2020 (control group, 294 participants using the original version of AVW-Space) to the 2021 data when 351 participants used the enhanced version of AVW-Space (experimental group). We analysed various measures of engagement (number of watched videos, comments, etc.) and learning (pre/post-study knowledge scores). The findings show that VLA encourage constructive behaviour and increase learning. This research contributes to using student-facing VLA in VBL platforms to boost engagement and learning.
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References
Scagnoli, N.I., Choo, J., Tian, J.: Students’ insights on the use of video lectures in online classes. Br. J. Edu. Technol.50, 399–414 (2019)
Yousef, A.M.F., Chatti, M.A., Schroeder, U.: The state of video-based learning: a review and future perspectives. Adv. Life Sci.6, 122–135 (2014)
Chatti, M.A., et al.: Video annotation and analytics in CourseMapper. Smart Learn. Environ.3, 10 (2016)
Cummins, S., Beresford, A.R., Rice, A.: Investigating engagement with in-video quiz questions in a programming course. IEEE Trans. Learn. Technol.9, 57–66 (2016)
Giannakos, M.N., Sampson, D.G., Kidziński, Ł: Introduction to smart learning analytics: foundations and developments in video-based learning. Smart Learn. Environ.3(1), 1–9 (2016).https://doi.org/10.1186/s40561-016-0034-2
Wang, M., Peng, J., Cheng, B., Zhou, H., Liu, J.: Knowledge visualization for self-regulated learning. J. Educ. Technol. Soc.14, 28–42 (2011)
Hooshyar, D., Pedaste, M., Saks, K., Leijen, Ä., Bardone, E., Wang, M.: Open learner models in supporting self-regulated learning in higher education: a systematic literature review. Comput. Educ.154, 103878 (2020)
Bodily, R., et al.: Open learner models and learning analytics dashboards: a systematic review. In: Proceedings of 8th International Conference on Learning Analytics and Knowledge, pp. 41–50 (2018)
Bull, S., Kay, J.: Open learner models. In: Advances in Intelligent Tutoring Systems, pp. 301–322 (2010).https://doi.org/10.1007/978-3-642-14363-2_15
Aguilar, S., Karabenick, S.A., Teasley, S.D., Baek, C.: Associations between learning analytics dashboard exposure and motivation and self-regulated learning. Comput. Educ.162, 104085 (2021).https://doi.org/10.1016/j.compedu.2020.104085
Aguilar, S., Lonn, S., Teasley, S.D.: Perceptions and use of an early warning system during a higher education transition program. In: Proceedings of Learning Analytics and Knowledge, pp. 113–117 (2014)
Ruiz, J.S., Díaz, H.J.P., Ruipérez-Valiente, J.A., Muñoz-Merino, P.J., Kloos, C.D.: Towards the development of a learning analytics extension in open EdX. In: Proceedings of 2nd International Conference on Technological Ecosystems for Enhancing Multiculturality, pp. 299–306 (2014)
Mitrovic, A., Dimitrova, V., Weerasinghe, A., Lau, L.: Reflective experiential learning: using active video watching for soft skills training. In: International Conference on Computers in Education, pp. 192–201 (2016)
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
Mohammadhassan, N., Mitrovic, A., Neshatian, K.: Investigating the effect of nudges for improving comment quality in active video watching. Comput. Educ.176, 104340 (2022)
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
Matcha, W., Uzir, N.A., Gašević, D., Pardo, A.: A systematic review of empirical studies on learning analytics dashboards: a self-regulated learning perspective. IEEE Trans. Learn. Technol.13, 226–245 (2020)
Sedrakyan, G., Malmberg, J., Verbert, K., Järvelä, S., Kirschner, P.A.: Linking learning behavior analytics and learning science concepts: designing a learning analytics dashboard for feedback to support learning regulation. Comput. Hum. Behav.107, 105512 (2020)
Chou, C.-Y., et al.: Open student models of core competencies at the curriculum level: using learning analytics for student reflection. IEEE Trans. Emerg. Top. Comput.5, 32–44 (2017)
Majumdar, R., Akçapınar, A., Akçapınar, G., Flanagan, B., Ogata, H.: LAView: learning analytics dashboard towards evidence-based education. In: Companion Proceedings of 9th International Conference on Learning Analytics & Knowledge, pp. 68–73 (2019)
Broos, T., Peeters, L., Verbert, K., Van Soom, C., Langie, G., De Laet, T.: Dashboard for actionable feedback on learning skills: scalability and usefulness. In: Zaphiris, P., Ioannou, A. (eds.) LCT 2017. LNCS, vol. 10296, pp. 229–241. Springer, Cham (2017).https://doi.org/10.1007/978-3-319-58515-4_18
Ez-zaouia, M., Tabard, A., Lavoué, E.: EMODASH: a dashboard supporting retrospective awareness of emotions in online learning. Human-Comput. Stud.139, 102411 (2020)
Ruiz, S., Charleer, S., Urretavizcaya, M., Klerkx, J., Fernández-Castro, I., Duval, E.: Supporting learning by considering emotions: tracking and visualization a case study. In: International Conference on Learning Analytics & Knowledge, pp. 254–263 (2016)
Guerra, J., Hosseini, R., Somyurek, S., Brusilovsky, P.: An intelligent interface for learning content: combining an open learner model and social comparison to support self-regulated learning and engagement. In: Intelligent User Interfaces, pp. 152–163 (2016)
Corrin, L., de Barba, P.: Exploring students’ interpretation of feedback delivered through learning analytics dashboards. In: Rhetoric and Reality: Critical Perspectives on Educational Technology. Proceedings ASCILITE, pp. 629–633 (2014)
Lim, L., Dawson, S., Joksimovic, S., Gašević, D.: Exploring students’ sensemaking of learning analytics dashboards: does frame of reference make a difference? In: International Conference on Learning Analytics & Knowledge, pp. 250–259 (2019)
Lonn, S., Aguilar, S., Teasley, S.D.: Investigating student motivation in the context of a learning analytics intervention during a summer bridge program. Comput. Hum. Behav.47, 90–97 (2015)
Srivastava, N., Velloso, E., Lodge, J.M., Erfani, S., Bailey, J.: Continuous evaluation of video lectures from real-time difficulty self-report. In: Proceedings of Human Factors in Computing Systems, pp. 1–12 (2019)
Yoon, M., Hill, J., Kim, D.: Designing supports for promoting self-regulated learning in the flipped classroom. J. Comput. High. Educ.33(2), 398–418 (2021).https://doi.org/10.1007/s12528-021-09269-z
Mohammadhassan, N., Mitrovic, A., Neshatian, K., Dunn, J.: Automatic assessment of comment quality in active video watching. In: International Conference on Computers in Education, pp. 1–10 (2020)
Taskin, Y., Hecking, T., Hoppe, H.U., Dimitrova, V., Mitrovic, A.: Characterizing comment types and levels of engagement in video-based learning as a basis for adaptive nudging. In: Scheffel, M., Broisin, J., Pammer-Schindler, V., Ioannou, A., Schneider, J. (eds.) EC-TEL 2019. LNCS, vol. 11722, pp. 362–376. Springer, Cham (2019).https://doi.org/10.1007/978-3-030-29736-7_27
Chi, M.T.H., Wylie, R.: The ICAP framework: linking cognitive engagement to active learning outcomes. Educ. Psychol.49, 219–243 (2014)
Mohammadhassan, N., Mitrovic, A.: 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 (2022).https://doi.org/10.1007/978-3-030-93859-8_24
Dimitrova, V., Mitrovic, A., Piotrkowicz, A., Lau, L., Weerasinghe, A.: Using learning analytics to devise interactive personalised nudges for active video watching. In: User Modeling, Adaptation and Personalization, pp. 22–31 (2019)
Hu, L., Bentler, P.M.: Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct. Equ. Model.6, 1–55 (1999)
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University of Canterbury, Christchurch, New Zealand
Negar Mohammadhassan & Antonija Mitrovic
- Negar Mohammadhassan
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- Antonija Mitrovic
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Correspondence toNegar Mohammadhassan orAntonija Mitrovic.
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Ateneo De Manila University, Quezon, Philippines
Maria Mercedes Rodrigo
Department of Computer Science, North Carolina State University, Raleigh, NC, USA
Noburu Matsuda
Durham University, Durham, UK
Alexandra I. Cristea
University of Leeds, Leeds, UK
Vania Dimitrova
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Mohammadhassan, N., Mitrovic, A. (2022). Investigating the Effectiveness of Visual Learning Analytics in Active Video Watching. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2022. Lecture Notes in Computer Science, vol 13355. Springer, Cham. https://doi.org/10.1007/978-3-031-11644-5_11
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