Modeling Students' Attention in the Classroom using Eyetrackers

Veliyath, Narayanan; De, Pradipta; Allen, Andrew; Hodges, Charles B.; Mitra, Aniruddha

doi:10.1145/3299815.3314424

Cited by 21 publications

(10 citation statements)

References 12 publications

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“…In existing research, video-based methods extract learners' eye gaze and head pose data as indicators of attention. Veliyath et al [15] and Daniel et al [24] used eye gaze data to recognize concentration; they extracted gaze position, task location, gaze duration, gaze rate, gaze count, and other variables as effective evaluation indicators of concentration during the learning process. In video-based methods, head pose can also serve as an indicator of learners' concentration.…”

Section: Concentration Recognition Based On Interaction and Vision Datamentioning

confidence: 99%

“…Concentration is identified from the emotional dimension by extracting features such as facial expressions [11], text [12], and posture [13]. Behavioral aspects of concentration are evaluated through data collected from learners' clickstream [14], eye gaze [15], and other behaviors. The above approaches generally fall into two categories: computer interaction data and computer vision data.…”

Section: Introductionmentioning

confidence: 99%

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Research on Learning Concentration Recognition with Multi-Modal Features in Virtual Reality Environments

Zhong

et al. 2023

Sustainability

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Learning concentration, as a crucial factor influencing learning outcomes, provides the basis for learners’ self-regulation and teachers’ instructional adjustments and intervention decisions. However, the current research on learning concentration recognition lacks the integration of cognitive, emotional, and behavioral features, and the integration of interaction and vision data for recognition requires further exploration. The way data are collected in a head-mounted display differs from that in a traditional classroom or online learning. Therefore, it is vital to explore a recognition method for learning concentration based on multi-modal features in VR environments. This study proposes a multi-modal feature integration-based learning concentration recognition method in VR environments. It combines interaction and vision data, including measurements of interactive tests, text, clickstream, pupil facial expressions, and eye gaze data, to measure learners’ concentration in VR environments in terms of cognitive, emotional, and behavioral representation. The experimental results demonstrate that the proposed method, which integrates interaction and vision data to comprehensively represent the cognitive, emotional, and behavioral dimensions of learning concentration, outperforms single-dimensional and single-type recognition results in terms of accuracy. Additionally, it was found that learners with higher concentration levels achieve better learning outcomes, and learners’ perceived sense of immersion is an important factor influencing their concentration.

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Section: Concentration Recognition Based On Interaction and Vision Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Learning Concentration Recognition with Multi-Modal Features in Virtual Reality Environments

Zhong

et al. 2023

Sustainability

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“…Some prior studies have proposed interaction techniques centering on the context of participants' attention [16,39,51], as it has been pointed out that people often have difficulty maintaining their attention during video-based communication [29,30]. These techniques benefit from the significant effort that has been devoted to estimating participants' attentiveness based on visual cues, such as face movement [42], body postures [54], and gaze [7,24,45]. They then use the estimation results to enhance learners' performance, for instance in the case of video-based learning, as it is widely acknowledged that learners' attention and engagement are strongly related to their learning performance [4,16].…”

Section: Attention-related Interaction Techniques For Video-based Lea...mentioning

confidence: 99%

“…In addition, the accuracy of the machine learning-based sensing module in the second experiment can be improved using the latest techniques [24,42,45,54]. In this study, we used a naïve approach based on head pose to investigate the effect of the proposed approach with false-positive detection.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Mindless Attractor: A False-Positive Resistant Intervention for Drawing Attention Using Auditory Perturbation

Arakawa

Yakura

2021

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

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Explicitly alerting users is not always an optimal intervention, especially when they are not motivated to obey. For example, in video-based learning, learners who are distracted from the video would not follow an alert asking them to pay attention. Inspired by the concept of Mindless Computing, we propose a novel intervention approach, Mindless Attractor, that leverages the nature of human speech communication to help learners refocus their attention without relying on their motivation. Specifically, it perturbs the voice in the video to direct their attention without consuming their conscious awareness. Our experiments not only confirmed the validity of the proposed approach but also emphasized its advantages in combination with a machine learning-based sensing module. Namely, it would not frustrate users even though the intervention is activated by false-positive detection of their attentive state. Our intervention approach can be a reliable way to induce behavioral change in human-AI symbiosis. CCS CONCEPTS• Human-centered computing → Auditory feedback; Interaction techniques; • Applied computing → Interactive learning environments.

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“…Frasson, 2011a, 2011b;Abujayyab et al, 2017), and eyetracker (e.g. Veliyath et al, 2019), these types of researches can't be widely used for attention detection within asynchronous e-learning environments as it need special equipments. The second category focused on (ii) analyzing the data generated from the interaction between the learner and the learning system (e.g., de Vicente, 2003;Qu.…”

Section: Introductionmentioning

confidence: 99%

Towards Webcam-Based Face Direction Tracking to Detect Learners' Attention Within Asynchronous E-Learning Environment

Ramaha

Karaş²,

Gül

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Recently, as a consequence of COVID-19 pandemic, the delivery of education at most of the educational institutions depended mainly on e-learning. So, the researchers give more attention for both synchronous and asynchronous e-learning. Although from an economical perspective, asynchronous e-learning seems to be the best e-learning option for institutions, still one of the biggest challenges is how to keep learners motivated for the entire learning process. One of important motivational factors that drives the success of the learning process is the learner attention. Therefore, to retain the learners' attention during the asynchronous e-learning process, we need first to detect their loss of attention. Accordingly, more studies started to focus on detecting learners’ attention. However, those studies can't be widely used for attention detection within asynchronous e-learning environments, as the used approaches tend to be inaccurate, and complex for the design and maintain. In contrast, in this study, we explore the possibility to find a simple way that can be widely used to detect learners' attention within the asynchronous e-learning environments. Therefore, we used webcams which are available in almost every laptop, and computer vision tools to detect learners' attention by tracking their faces. Thereafter, we evaluated the accuracy of our suggested method, the result of this evaluation showed that our method is efficient.

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Modeling Students' Attention in the Classroom using Eyetrackers

Cited by 21 publications

References 12 publications

Research on Learning Concentration Recognition with Multi-Modal Features in Virtual Reality Environments

Research on Learning Concentration Recognition with Multi-Modal Features in Virtual Reality Environments

Mindless Attractor: A False-Positive Resistant Intervention for Drawing Attention Using Auditory Perturbation

Towards Webcam-Based Face Direction Tracking to Detect Learners' Attention Within Asynchronous E-Learning Environment

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