Measuring real-time cognitive engagement in remote audiences

Levordashka, Ana; Fraser, Danaë Stanton; Gilchrist, Iain D.

doi:10.21203/rs.3.rs-2377850/v1

Cited by 2 publications

(1 citation statement)

References 41 publications

(37 reference statements)

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“…Among these automatic detection methods, computer vision-based engagement detection is the most popular one, which can offer many ways to detect engagement, such as body behavior, facial expression, head posture, etc [5,6]. For example, Levordashka et al [7] used head posture features (i.e., head yaw, pitch, and roll) to detect cognitive engagement. The advantages of the computer vision-based method, such as real-time, non-invasive, and low-cost, make it suitable for cognitive engagement detection in the classroom [4].…”

Section: Introductionmentioning

confidence: 99%

ICAPD Framework and simAM-YOLOv8n for Student Cognitive Engagement Detection in Classroom

Xu,

Wei,

Gao

et al. 2023

IEEE Access

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Research has shown that cognitive engagement plays a key role in effective learning, resulting in extensive efforts have been devoted to measuring it. Whereas most of the literature explores manual methods to measure cognitive engagement, the research on automatic detection of cognitive engagement levels in real classrooms is very limited. Automatic detection of cognitive engagement has been a problem for a long time due to the lack of behavior annotation guidance and effective detection algorithms. For the first challenge, a theory of cognitive engagement called Interactive-Constructive-Active-Passive-Disengage (ICAPD) was proposed in this paper. ICAPD links visual behaviors with cognitive engagement in the classroom. According to the ICAPD framework, a cognitive engagement dataset was constructed to train the detection model. To tackle the second challenge, the simAM-based You Only Look Once version 8 Nano (simAM-YOLOv8n) model, which uses simAM attention module to strengthen feature extraction, was designed to detect different levels of cognitive engagement precisely and efficiently. The experimental results on the self-build dataset have demonstrated the effectiveness of the proposed theory framework and detection algorithm, indicating that the proposed methodology could be used to detect real-time cognitive engagement in the classroom scenario. This work has the potential to help teachers to carry out learning analysis and instructional adjustments.

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Section: Introductionmentioning

confidence: 99%

ICAPD Framework and simAM-YOLOv8n for Student Cognitive Engagement Detection in Classroom

Xu,

Wei,

Gao

et al. 2023

IEEE Access

View full text Add to dashboard Cite

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Narrative predicts cardiac synchrony in audiences

Hammond,

Armstrong,

Thomas

et al. 2024

Sci Rep

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Audio-visual media possesses a remarkable ability to synchronise audiences’ neural, behavioural, and physiological responses. This synchronisation is considered to reflect some dimension of collective attention or engagement with the stimulus. But what is it about these stimuli that drives such strong engagement? There are several properties of media stimuli which may lead to synchronous audience response: from low-level audio-visual features, to the story itself. Here, we present a study which separates low-level features from narrative by presenting participants with the same content but in separate modalities. In this way, the presentations shared no low-level features, but participants experienced the same narrative. We show that synchrony in participants’ heart rate can be driven by the narrative information alone. We computed both visual and auditory perceptual saliency for the content and found that narrative was approximately 10 times as predictive of heart rate as low-level saliency, but that low-level audio-visual saliency has a small additive effect towards heart rate. Further, heart rate synchrony was related to a separate cohorts’ continuous ratings of immersion, and that synchrony is likely to be higher at moments of increased narrative importance. Our findings demonstrate that high-level narrative dominates in the alignment of physiology across viewers.

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Measuring real-time cognitive engagement in remote audiences

Cited by 2 publications

References 41 publications

ICAPD Framework and simAM-YOLOv8n for Student Cognitive Engagement Detection in Classroom

ICAPD Framework and simAM-YOLOv8n for Student Cognitive Engagement Detection in Classroom

Narrative predicts cardiac synchrony in audiences

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