This week as the final drawing for Block 3, I chose to carry out an experiment on a topic that has much relevance in my workplace (the higher education sector) – video watching behaviour. It involved me playing a 20-minute instructional video from YouTube on an unfamiliar topic, and then recording my screen as well as myself using a webcam. Afterwards, I watched the recording of myself to record the time points at which I faced away, closed my eyes, clicked fast forward or rewind, or stopped the video. In my data visualisation, I made colour-coded markings on the timeline to show the timing of these behaviours. This experiment is inspired by discussions about tracking students’ eye gazes and head movements in e-learning platforms, so as to obtain data about their engagement (Asteriadis et al., 2009).
Such data collection and visualisation empower new ways for governance in higher education settings. When visualised as a population, university management may see that students mostly managed to sustain their attention at the first 10 minutes of watching instructional videos. If it is shown to be common pattern, institutes may put forward guidelines that encourage videos to be shorter than 10 minutes. In a more hardline approach, institutes can use such data to officiate one form of instructional video while rejecting others. Video hosting platforms can also be hard coded to reject videos longer than 10 minutes if so wished by the institute.
On a day-to-day basis, institutes track their students’ progress in watching lecture recordings and instructional videos. Normally without eye gaze and head movement data, we can only look at how much of a video is played by each student’s account, and at best, whether the browser tab of the video remained onscreen or minimised. With a more intimate tracking, we can understand better how students sustain their attention while watching a video. However, as demonstrated in this experiment, it involved real-time video recording of myself and how such footage is used is entirely at the mercy of who holds that piece of data. If such data are employed for continuous assessment, the power imbalance between the assessor and the assessed may force students to yield and surrender their data, which would definitely be a concerning phenomenon.
Asteriadis, S., Tzouveli, P., Karpouzis, K., & Kollias, S. (2009). Estimation of behavioral user state based on eye gaze and head pose—application in an e-learning environment. Multimedia Tools and Applications, 41(3), 469-493.