The interactions between student and teacher are fundamentally changed in video-based learning. Assessing cognitive load and whether learning has taken place and maintaining student engagement is imperative in determining whether a curated video library can be a viable source of learning either as a primary or as an additional resource. Reading 30+ articles I found that a number of factors contribute to balance the cognitive load in video-based learning: length (Brame, 2016); instructor presence in the video (van Wermeskerken & van Gog, 2017; Wang & Antonenko, 2017); graphics, animations (Wong, Leahy, Marcus, & Sweller, 2012); text and audio narration (Clark & Mayer, 2011); interactivity through questions and challenges (Vural, 2013); video lecture types (Chen & Wu, 2015); scaffolding (Cojean & Jamet, 2018); teaching declarative or procedural knowledge (Hong, Pi, & Yang, 2018); prior knowledge (Kalyuga & Singh, 2016); design elements.

My research topic is instructor presence in videos, but I am suggesting Lynda.com video tutorials to my students for information seeking (IS) in addition to Google (our “best friend” in web development).

Scaffolding is “providing tools that increase users’ comprehension” (Cojean & Jamet, 2018, p. 961). The goal is to find the relevant information in an efficient and effective way. Play, pause, forward, rewind, and segmentation are micro-level activities and result in microscaffolding, while structuring and providing a table of content are macro-level activities and result in macroscaffolding (Cojean & Jamet, 2018). Cojean & Jamet (2018) described that scaffolding a video helped students to engage in efficient IS, but they had less accurate mental representations of the video. When scaffolding is missing, users are more likely to develop a relevant mental model of the video content. In short, scaffolding enhances IS but does not allow the provided external conceptual model to be internalized as a mental model. Video‐based environments are used not only for IS tasks but also for learning contexts, though according to Cojean & Jamet (2018) IS and learning are closely linked, as information processing begins with the localization of the relevant information.

 

The illustrations are created by the author.

References

Brame, C. J. (2016). Effective educational videos: Principles and guidelines for maximizing student learning from video content. CBE—Life Sciences Education, 15(4), es6. https://doi.org/10.1187/cbe.16-03-0125

Chen, C.-M., & Wu, C.-H. (2015). Effects of different video lecture types on sustained attention, emotion, cognitive load, and learning performance. Computers & Education, 80, 108–121. https://doi.org/10.1016/j.compedu.2014.08.015

Clark, R. C., & Mayer, R. E. (2011). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning (3rd ed.). San Francisco: Pfeiffer.

Cojean, S., & Jamet, E. (2018). The role of scaffolding in improving information seeking in videos. Journal of Computer Assisted Learning, 34(6), 960–969. https://doi.org/10.1111/jcal.12303

Hong, J., Pi, Z., & Yang, J. (2018). Learning declarative and procedural knowledge via video lectures: cognitive load and learning effectiveness. Innovations in Education and Teaching International, 55(1), 74–81. https://doi.org/10.1080/14703297.2016.1237371

Kalyuga, S., & Singh, A.-M. (2016). Rethinking the boundaries of cognitive load theory in complex learning. Educational Psychology Review, 28(4), 831–852. https://doi.org/10.1007/s10648-015-9352-0

van Wermeskerken, M., & van Gog, T. (2017). Seeing the instructor’s face and gaze in demonstration video examples affects attention allocation but not learning. Computers & Education, 113, 98–107. https://doi.org/10.1016/J.COMPEDU.2017.05.013

Vural, O. F. (2013). The impact of a question-embedded video-based learning tool on e-learning. Retrieved April 7, 2019, from https://eric.ed.gov/?id=EJ1017292

Wang, J., & Antonenko, P. D. (2017). Instructor presence in instructional video: Effects on visual attention, recall, and perceived learning. Computers in Human Behavior, 71, 79–89. https://doi.org/10.1016/J.CHB.2017.01.049

Wong, A., Leahy, W., Marcus, N., & Sweller, J. (2012). Cognitive load theory, the transient information effect and e-learning. Learning and Instruction, 22(6), 449–457. https://doi.org/10.1016/J.LEARNINSTRUC.2012.05.004