Detection of Subject Attention in an Active Environment Through Facial Expressions Using Deep Learning Techniques and Computer Vision

Gerard, Naqash; Yousuf, Talha; Johar, Ahmed Husnain; Asgher, Umer; Malik, Imran; Hasan, Adnan ul

doi:10.1007/978-3-030-51041-1_43

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Additionally, learners' facial expressions can also be used to represent concentration. Sharma et al [27] and Gerard et al [28] computed a learning concentration score by capturing learners' facial expression features, classifying expressions, and assigning different weights to them.…”

Section: Concentration Recognition Based On Interaction and Vision Datamentioning

confidence: 99%

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%

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

Zhong

et al. 2023

Sustainability

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“…Attention recognition has many variations of implementation, such as: to enhance dynamic teaching [39]; related to the correctness of quiz answers [40]; to find usability problems in the system [41]; to enhance virtual learning (interaction and feedback) [42]; encourage self-regulated learning [43]; to place the subtitles in the video that impact learning outcomes [44]; to develop smart and flexible systems by monitoring learner learning activities [45]; as reflection by observing the previous online learning strategy [46]; for learner feedback [47]; to monitor cognitive activity [48]; to comprehend the self-produced cognition complexity [49]; to identify user tasks in the online system [50]. In addition, attention detection can be utilized to assess teaching strategy [51] and the combination of attention, arousal, and valence are utilized to determine the affective state used to adjust to personal learner requirements [52]. The two indicates that emotion recognition can be used for attention identification.…”

Section: The Functions Of Prolmentioning

confidence: 99%

A Brief Study of The Use of Pattern Recognition in Online Learning: Recommendation for Assessing Teaching Skills Automatically Online Based

Utami

Hartanto²,

Soesanti³

2022

ELINVO

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Online learning has become a trend for the current generation of students who have been exposed to advanced information and communication technology. Smart education can use pattern recognition. Manual assessments are subjective and inconsistent. To overcome these problems, pattern recognition can be used in the non-verbal aspect assessment system. This study describes pattern recognition in online learning about the functions, modalities, and algorithms and specifically related to the recognition system of non-verbal aspects of teaching skills. The literature study was carried out through the stages of planning, selection, extraction, and selection. There are 86 articles reviewed. The first result is the functions of implementing pattern recognition in online learning are engagement recognition, attention detection, emotion recognition, learning behavior, learning activity recognition, authentication, teaching training, etc. using four classifications of modality: visual, audio, biosignal, behavioral, and CNN as the most widely used learning algorithm. Secondly, all modalities (except behavioral) and CNN algorithm can be used for assessing teaching skills. Early development of the non-verbal aspect assessment system can use Facial Expression Recognition (FER) and Hand Gesture Recognition (HGR). The future analysis needs to focus on technology characteristics, the meaningfulness of the content, and the proper teaching mode. In the end, hopefully, prospective teachers will acquire technology that can make it easier for them to practice teaching and get objective assessments.

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The Features of Students Paying and Not Paying Attention in Online Classes

Hafez,

Nosseir,

McKee

et al. 2023

2023 International Conference on Computer and Applications (ICCA)

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Detection of Subject Attention in an Active Environment Through Facial Expressions Using Deep Learning Techniques and Computer Vision

Cited by 3 publications

References 13 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

A Brief Study of The Use of Pattern Recognition in Online Learning: Recommendation for Assessing Teaching Skills Automatically Online Based

The Features of Students Paying and Not Paying Attention in Online Classes

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