Exploring the usage of thermal imaging for understanding video lecture designs and students' experiences

Abstract

Video is becoming a dominant medium for the delivery of educational material. Despite the widespread use of video for learning, there is still a lack of understanding about how best to help people learn in this medium. This study demonstrates the use of thermal camera as compared to traditional self-reported methods for assessing learners' cognitive load while watching video lectures of different styles. We evaluated our approach in a study with 78 university students viewing two variants of short video lectures on two different topics. To incorporate subjective measures, the students reported on mental effort, interest, prior knowledge, confidence, and challenge. Moreover, through a physical slider device, the students could continuously report on their perceived level of difficulty. Lastly, we used thermal sensor as an additional indicator of students' level of difficulty and associated cognitive load. This was achieved through, continuous real-time monitoring of students by using a thermal imaging camera. This study aims to address the following: firstly, to analyze if video styles differ in terms of the associated cognitive load. Secondly, to assess the effects of cognitive load on learning outcomes; could an increase in the cognitive load be associated with poorer learning outcomes? Third, to see if there is a match between students' perceived difficulty levels and a biological indicator. The results suggest that thermal imaging could be an effective tool to assess learners' cognitive load, and an increased cognitive load could lead to poorer performance. Moreover, in terms of the lecture styles, the animated video lectures appear to be a better tool than the text-only lectures (in the content areas tested here). The results of this study may guide future works on effective video designs, especially those that consider the cognitive load.

A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness

“…Pace-related: automatic pauses, pauses controlled by the learner, type of pauses, fast pace. Interactive features [11, 15, 20, 33, 34, 41, 46, 47, 51, 53, 56, 62, 82, 84, 91, 100, 106, 110, 113, 114, 118-120, 125, 129, 130, 132, 142, 149-151, 154, 157, 162, 172, 175, 178, 188, 199, 208, 210, 217, 222, 234, 238, 240, 247-250, 259, 277] Quizzes embedded in the video, virtual reality and 360-degree characteristics, in-video annotations, feedback to the learner, games, hyperlinks, interface elements for navigation Production style [6,30,42,45,49,55,56,87,88,93,94,136,140,180,181,190,191,197,207,212,214,223,224,229,230,236] Khan-style, with a talking head, dialogue-style or monologue-style, slide-based, with animations, paper-based explanations, classroom-style videos, Instructional design and principles [5,58,60,75,95,101,135,140,141,169,185,212,226,241,246,...…”

“…Production style: Most of the experiments reported the production style of the videos (69% of controlled experiments). The results showed that most of them used slide-based videos (39% of the experiments) [30,46,93,96,98,116,136,142,172,181,192,193,209,223,224,251,254]. The presence of an instructor was also very common among the experiments (36%) [18,19,30,46,55,93,96,98,116,180,192,193,223,229,230,254].…”

“…The results showed that most of them used slide-based videos (39% of the experiments) [30,46,93,96,98,116,136,142,172,181,192,193,209,223,224,251,254]. The presence of an instructor was also very common among the experiments (36%) [18,19,30,46,55,93,96,98,116,180,192,193,223,229,230,254]. Less frequent production styles included handwriting (e.g., Khan-style, classroom-style videos) (16%), [93,136,190,204,209,223,230], talking-head (16%) [30,49,93,145,[190][191][192], screencasts (14%) [73,…”

“…The presence of an instructor was also very common among the experiments (36%) [18,19,30,46,55,93,96,98,116,180,192,193,223,229,230,254]. Less frequent production styles included handwriting (e.g., Khan-style, classroom-style videos) (16%), [93,136,190,204,209,223,230], talking-head (16%) [30,49,93,145,[190][191][192], screencasts (14%) [73,74,113,186,241,242], productions with virtual reality and 360-degree characteristics (14%) [11,33,47,84,100,175], and animations in videos (content created entirely with drawings, illustrations, or computer-generated effects) (14%) [6,42,145,170,205,…”

Data-driven analysis of facial thermal responses and multimodal physiological consistency among subjects

“…IRI quantifies the temperature fluctuations of the face, predominantly resulting from blood flow changes 4 although it has also been shown to capture the temperature effect of perspiration 5 . As a contact-free technique, it allows ecologically valid experimental conditions which recent studies have exploited 6 . Although it has not yet been as widely used as GSR and HR measures, thermal imaging has shown promising results in affective research [7][8][9] .…”