Emotion Recognition in E-learning Systems

Hammoumi, Oussama El; Benmarrakchi, Fatima Ezzahra; Ouherrou, Nihal; Kafi, Jamal El; Hore, Ali El

doi:10.1109/icmcs.2018.8525872

Cited by 44 publications

(18 citation statements)

References 20 publications

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“…e feedback was critical in creating a more immersive learning environment. Hammoumi et al [41] devised a method for determining how a learner feels in an e-learning environment. e framework was created using instructive game software.…”

Section: Facial Emotions Sathik and Jonathanmentioning

confidence: 99%

Estimation of Learning Affects Experienced by Learners: An Approach Using Relational Reasoning and Adaptive Mapping

Pise

Vadapalli

Sanders

2022

Wireless Communications and Mobile Computing

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Various studies have measured and analyzed learners’ emotions in both traditional classroom and e-learning settings. Learners’ emotions can be estimated using their text input, speech, body language, or facial expressions. The presence of certain facial expressions has shown to indicate a learner’s levels of concentration in both traditional and e-learning environments. Many studies have focused on the use of facial expressions in estimating the emotions experienced by learners. However, little research has been conducted on the use of analyzed emotions in estimating the learning affect experienced. Previous studies have shown that online learning can enhance students’ motivation, interest, attention, and performance as well as counteract negative emotions, such as boredom and anxiety, that students may experience. Thus, it is crucial to integrate modules into an existing e-learning platform to effectively estimate learners’ learning affect (LLA), provide appropriate feedback to both learner and lecturers, and potentially change the overall online learning experience. This paper proposes a learning affect estimation framework that employs relational reasoning for facial expression recognition and adaptive mapping between recognized emotions and learning affect. Relational reasoning and deep learning, when used for autoanalysis of facial expressions, have shown promising results. The proposed methodology includes estimating a learner’s facial expressions using relational reasoning; mapping the estimated expressions to the learner’s learning affect using the adaptive LLA transfer model; and analyzing the effectiveness of LLA within an online learning environment. The proposed research thus contributes to the field of facial expression recognition enhancing online learning experience and adaptive learning.

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Section: Facial Emotions Sathik and Jonathanmentioning

confidence: 99%

Estimation of Learning Affects Experienced by Learners: An Approach Using Relational Reasoning and Adaptive Mapping

Pise

Vadapalli

Sanders

2022

Wireless Communications and Mobile Computing

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“…ERS has been implemented for e-learning to improve learner's experience and to improve the delivery. In [30], a facial based ERS developed using open database is used to detect emotion of students participated in educational game. In their experiment 4 young students including 2 of students with learning difficulties participated.…”

Section: Emotion Recognition System In Educationmentioning

confidence: 99%

Awareness and Readiness of Malaysian University Students for Emotion Recognition System

Aziz

Ismail

et al. 2021

IJIE

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Emotion Recognition System (ERS) identifies human emotion like happiness, sadness, anger, disgust and fear. These emotions can be detected via various modalities such as facial expression analysis, voice intonation, and physiological signals like the brain’s electroencephalogram (EEG) and heart’s electrocardiogram (ECG). The emotion recognition system allows machines to recognized human emotions and reacts to it. It offers broad areas of application, from smart home automation to entertainment recommendation system to driving assistance and to automated security system. It is a promising and interesting field to be explored especially as we are moving towards industrial revolution 5.0. Therefore, a survey was conducted on the awareness and readiness of the usage of emotion recognition system among Malaysian youths, specifically among university students. The findings are presented here. Overall, positive orientation towards the technology is observed among the participants and they are ready for its adoption

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“…Following the line of works in the deep learning paradigm, the use of CNN for image feature extraction has become the standard choice. Emotion Recognition studies conducted in [10], [11], [12], [13], [14] achieve State of the art accuracy on very popular datasets using CNN using both classification and regression targets.…”

Section: Related Workmentioning

confidence: 99%

Affective State-Based Framework for e-Learning Systems

Rodríguez

Comas

Binefa

2021

Frontiers in Artificial Intelligence and Applications

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Virtual learning and education have become crucial during the COVID-19 pandemic, which has forced a rethink by teachers and educators into designing online content and the indirect interaction with students. In an face-to-face class, some visual cues help the teacher recognize the engagement level of students, while the main weakness of the online approach is the lack of feedback that the teacher has about the learning process of the students. In this paper, we introduce a novel framework able to track the learning states, or LS, of the students while they are watching a piece of knowledge-based content. Specifically, we extract four learning states: Interested, Bored, Confused or Distracted. Finally, to demonstrate the system’s capability, we collected a reduced database to analyze the affective state of the subjects. From these preliminary results, we observe abrupt changes in the LS of the audience when there are abrupt changes in the narrative of the video, indicating that well-structured and bounded information is strongly related with the learning behaviour of the students.

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Emotion Recognition in E-learning Systems

Cited by 44 publications

References 20 publications

Estimation of Learning Affects Experienced by Learners: An Approach Using Relational Reasoning and Adaptive Mapping

Estimation of Learning Affects Experienced by Learners: An Approach Using Relational Reasoning and Adaptive Mapping

Awareness and Readiness of Malaysian University Students for Emotion Recognition System

Affective State-Based Framework for e-Learning Systems

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