A Learning Attention Monitoring System via Photoplethysmogram Using Wearable Wrist Devices

Li, Qing; Ren, Yuan; Wei, Tianyu; Wang, Chengcheng; Liu, Zhi; Yue, Jie-Yu

doi:10.1007/978-3-030-41099-5_8

Cited by 11 publications

(2 citation statements)

References 39 publications

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“…The data of individual students in each band were normalized separately to avoid the influence of individual characteristics of students on the model. Compared with a traditional electroencephalograph, the portable EEG device has fewer channels, but it offers similar results (Li et al , 2020) and can be applied on a large scale in a real classroom environment, which a traditional electroencephalograph cannot do (Xu and Zhong, 2018). In this experiment, a type of brain wave monitoring device was used, the core component of which was the ThinkGear Asic Module.…”

Section: Methodsmentioning

confidence: 99%

Multimodal Fast–Slow Neural Network for learning engagement evaluation

Zhang

Hung

et al. 2023

DTA

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PurposeStudent engagement is a key factor that connects with student achievement and retention. This paper aims to identify individuals' engagement automatically in the classroom with multimodal data for supporting educational research.Design/methodology/approachThe video and electroencephalogram data of 36 undergraduates were collected to represent observable and internal information. Since different modal data have different granularity, this study proposed the Fast–Slow Neural Network (FSNN) to detect engagement through both observable and internal information, with an asynchrony structure to preserve the sequence information of data with different granularity.FindingsExperimental results show that the proposed algorithm can recognize engagement better than the traditional data fusion methods. The results are also analyzed to figure out the reasons for the better performance of the proposed FSNN.Originality/valueThis study combined multimodal data from observable and internal aspects to improve the accuracy of engagement detection in the classroom. The proposed FSNN used the asynchronous process to deal with the problem of remaining sequential information when facing multimodal data with different granularity.

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Section: Methodsmentioning

confidence: 99%

Multimodal Fast–Slow Neural Network for learning engagement evaluation

Zhang

Hung

et al. 2023

DTA

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“…Sci. 2024, 14, 548 2 of 12 signals [9]. Other researchers developed attention-monitoring algorithms based on multimodal data including facial expressions, eye movements, and behaviors [10,11].…”

Section: Introduction 1online Learning and Attention Assessmentmentioning

confidence: 99%

Using Synchronized Eye Movements to Predict Attention in Online Video Learning

Su,

Liu,

Gan

et al. 2024

Education Sciences

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Concerns persist about attentional engagement in online learning. The inter-subject correlation of eye movements (ISC) has shown promise as an accessible and effective method for attention assessment in online learning. This study extends previous studies investigating ISC of eye movements in online learning by addressing two research questions. Firstly, can ISC predict students’ attentional states at a finer level beyond a simple dichotomy of attention states (e.g., attending and distracted states)? Secondly, whether learners’ learning styles affect ISC’s prediction rate of attention assessment in video learning? Previous studies have shown that learners of different learning styles have different eye movement patterns when viewing static materials. However, limited research has explored the impact of learning styles on viewing patterns in video learning. An eye tracking experiment with participants watching lecture videos demonstrated a connection between ISC and self-reported attention states at a finer level. We also demonstrated that learning styles did not significantly affect ISC’s prediction rate of attention assessment in video learning, suggesting that ISC of eye movements can be effectively used without considering learners’ learning styles. These findings contribute to the ongoing discourse on optimizing attention assessment in the evolving landscape of online education.

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