Automatic Recognition of Student Engagement Using Deep Learning and Facial Expression

Nezami, Omid Mohamad; Dras, Mark; Hamey, Len; Richards, Deborah; Wan, Stephen; Paris, Cécile

doi:10.1007/978-3-030-46133-1_17

Cited by 61 publications

(29 citation statements)

References 26 publications

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“…Automatic facial expression recognition can be used in patient care [ 26 ] and medical education [ 27 ], as well as in virtual reality [ 28 ] and augmented reality-based games [ 29 ]. Although some studies have explored the link between facial expression recognition technology and academic performance [ 18 , 19 , 30 ], there is limited research in the field of nursing education. The establishment of this information could serve as a supplementary screening tool for improving patient assessments [ 26 ].…”

Section: Discussionmentioning

confidence: 99%

“…He proposed that there are seven universal emotional expressions (i.e., happiness, sadness, surprise, fear, anger, disgust, and contempt) that can help identify emotions through these facial expressions. It is true that facial expressions can provide more detailed explanations to allow for a better understanding of emotions [ 17 , 18 , 19 ], such as anxiety or depression. In a recent study that used facial recognition technology, students who expressed more surprise and less disgust in a suicide advising session were more likely to be receptive to suicide prevention, which showed the relationship between facial expression and potential suicide prevention among students [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

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Relationships between Nursing Students’ Skill Mastery, Test Anxiety, Self-Efficacy, and Facial Expressions: A Preliminary Observational Study

et al. 2022

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Test anxiety and self-efficacy significantly influence the mastery of nursing skills. Facial expression recognition tools are central components to recognising these elements. This study investigated the frequent facial expressions conveyed by nursing students and examined the relationships between nursing skill mastery, test anxiety, self-efficacy, and facial expressions in a test-taking situation. Thirty-three second-year nursing students who were attending a university in a Korean metropolitan city participated. Test anxiety, self-efficacy, and facial expressions were collected while the students inserted indwelling catheters. Using Microsoft Azure software, the researchers examined the students’ facial expressions. Negative facial expressions, such as anger, disgust, sadness, and surprise, were more common during the test-taking situation than the practice trial. Fear was positively correlated with anxiety. None of the facial expressions had significant relationships with self-efficacy; however, disgust was positively associated with nursing skill mastery. The facial expressions during the practice and test-taking situations were similar; however, fear and disgust may have been indicators of test anxiety and skill mastery. To create a screening tool for detecting and caring for students’ emotions, further studies should explore students’ facial expressions that were not evaluated in this study.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Relationships between Nursing Students’ Skill Mastery, Test Anxiety, Self-Efficacy, and Facial Expressions: A Preliminary Observational Study

et al. 2022

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“…Nezami et al [36] in their paper proposed an automatic engagement recognition system using deep learning approach. They have prepared a new dataset named engagement recognition (ER) dataset for the purposeto facilitate research on engagement recognition from images.…”

Section: The Computer Vision-based Engagement Detection Techniquesmentioning

confidence: 99%

A survey on automatic engagement recognition methods: online and traditional classroom

sukumaran

Arun

2023

IJEECS

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Student engagement in a learning environment is directly related to students’ perception and involvement of the educational activities in the class, along with their physical and mental health. This paper presents an extensive survey of the various automatic engagement detection approaches and algorithms based on computer vision, physiological and neurological signals analysis-based methods. The computer vision-based techniques depend on the traits captured by image sensors such as facial expressions, gesture and posture analysis, and gaze direction. The physiological and neurological signal based approach depends on the sensor data, like heart rate (HR), electroencephalogram (EEG), blood pressure (BP), and galvanic skin response (GSR). A brief analysis of the available datasets for Engagement Recognition and its features are also summarized. This study highlights a few commercially available wearables which provides the physiological signals that helps in student’s attentivity recognition. Our study reveal that the accuracy of engagement recognition system will increase if we increase the number of modalities used. In this survey, we intend to support the upcoming researchers as well as tutors of smart education set up by providing an overview of existing or proposed approaches of automatic engagement detection techniques in different scenarios.

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“…The deep learning model is also used to enhance the engagement of students in e-learning through implementing pre-training data based on facial expression and then a model’s weights are used to initialize the base for the deep learning model using histograms of the oriented gradient to support vector machines ( Mohamad Nezami et al, 2020 ). Unfortunately, there is a lack of a dataset to be used for training systems in developing e-learning interactivity, most of the tested data are based on the physical behaviors of the face emotional states which are combined with students’ comprehension, and most of them are based on quantitative observation by recording the behaviors of students ( Sathik and Jonathan, 2013 ).…”

Section: Literature Reviewmentioning

confidence: 99%

The use of facial expressions in measuring students’ interaction with distance learning environments during the COVID-19 crisis

Maqableh¹,

Alzyoud

Zraqou

2023

Visual Informatics

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Automatic Recognition of Student Engagement Using Deep Learning and Facial Expression

Cited by 61 publications

References 26 publications

Relationships between Nursing Students’ Skill Mastery, Test Anxiety, Self-Efficacy, and Facial Expressions: A Preliminary Observational Study

Relationships between Nursing Students’ Skill Mastery, Test Anxiety, Self-Efficacy, and Facial Expressions: A Preliminary Observational Study

A survey on automatic engagement recognition methods: online and traditional classroom

The use of facial expressions in measuring students’ interaction with distance learning environments during the COVID-19 crisis

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