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Using web log analysis to evaluate healthcare students’ engagement behaviours with multimedia lectures on YouTube

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The objective of this study was to utilise web log analysis to evaluate the relationship between University students’ engagement (e.g., watch time) and the characteristics of a catalogue of multimedia… Click to show full abstract

The objective of this study was to utilise web log analysis to evaluate the relationship between University students’ engagement (e.g., watch time) and the characteristics of a catalogue of multimedia lectures, including their duration, the speaking rate of the narrator and the extent to which they implemented certain principles from Mayer’s Cognitive Theory of Multimedia Learning (CTML). Fifty-six multimedia lectures covering topics related to healthcare (e.g., anatomy, physiology and clinical assessment) were developed to differentially employ the image/embodiment, redundancy, segmentation and signalling principles from the CTML. These lectures were delivered to multiple cohorts of students throughout an academic semester. Student watch time was evaluated using the meta-usage data provided by YouTube studio. The multimedia lectures were viewed 4338 times (mean = 35 views per lecture; 27 unique viewers per lecture). Generalised estimating equations revealed that videos that were segmented into shorter chunks, that incorporated signals to highlight important information for students and during which captions were toggled ‘off’ by students were associated with longer watch times (P < 0.05). Additionally, watch time diminished for videos placed later in a sequence based on the audience retention metric. When designing multimedia lectures, instructors should be encouraged to use on screen labels to highlight important information, segment learning material into shorter ‘chunks’ and incorporate a dynamic instructor on screen at regular intervals displaying high embodiment. If several videos are to be delivered to students as part of a learning ‘unit’, educators should consider placing the most important learning material earlier in the sequence.

Keywords: multimedia lectures; web log; analysis evaluate; students engagement; log analysis

Journal Title: PLOS ONE
Year Published: 2023

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