A Deep Learning Approach for Online Learning Emotion Recognition

Ma, Chao; Cheng, Sun; Song, Donglei; Li, Xuan; Xu, Hao

doi:10.1109/iccse.2018.8468741

Cited by 11 publications

(18 citation statements)

References 5 publications

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“…They implemented it on a robotic platform to enable the robot to mimic its user"s emotional states when this robot looks into the user"s face. Moreover, a study [28] developed a CNNbased model for real-time emotion-score recognition. This model captures the learners" learning image via a webcam, judges their learning emotions in real time and sends feedback to the lecturer, enabling real-time emotion feedback in distant education [28].…”

Section: Face-basedmentioning

confidence: 99%

“…Moreover, a study [28] developed a CNNbased model for real-time emotion-score recognition. This model captures the learners" learning image via a webcam, judges their learning emotions in real time and sends feedback to the lecturer, enabling real-time emotion feedback in distant education [28]. The model was aimed at improving teacher-learner interaction, thereby improving learning efficiency and helping personalise education.…”

Section: Face-basedmentioning

confidence: 99%

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Robotics Utilization in Automatic Vision-Based Assessment Systems From Artificial Intelligence Perspective: A Systematic Review

et al. 2022

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This study presents a systematic review of the automatic vision-based assessment systems and models from related fields over the last 6 years. Many studies focus on automatic vision-based assessment in the area of robotics, artificial intelligence and virtual reality or augmented reality (AR) to enhance the communication between humans and machines for general purposes. Three reliable databases, IEEEXplore, Science Direct and Web of Science, were used to obtain relevant studies on the given topic. Several stages of filtering and scanning were applied according to the inclusion/exclusion criteria to filter the obtained 3505 papers from 2015 to 2020. Finally, 97 papers met the criteria. They were classified into four main categories based on their field by following the scientific taxonomy: computer-vision-based, robotics-based, AR-based and hybrid-based categories, accounting for 42.26% (n = 41/97 papers), 48.45% (n = 47/97 papers), 6.18% (n = 6/97 papers) and 4.12% (n = 4/97 papers), respectively. Subsequently, a deep and critical analysis of this multifield systematic review highlighted new research opportunities, motivations, challenges and recommendations that need attention to integrate interdisciplinary studies. Thus, automatic vision-based assessment, which is a field requiring automated solutions, tools and methods, enhances the ability of the assistive technology and facilitates the interaction of individuals with machines. Many studies have been conducted on the automatic vision-based assessment systems and their subtypes to promote accurate communication and performance evaluation in human-machine interaction. This study can provide researchers with useful guides and valuable information for future research. This study also addresses the ambiguity of automatic vison-based assessment models in interdisciplinary fields.

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Section: Face-basedmentioning

confidence: 99%

Section: Face-basedmentioning

confidence: 99%

Robotics Utilization in Automatic Vision-Based Assessment Systems From Artificial Intelligence Perspective: A Systematic Review

et al. 2022

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“…Además de la importancia de efectuar este tipo de reconocimientos de persona a persona, ya en el contexto de los entornos virtuales surgen enfoques avanzados de inteligencia artificial para identificar, reconocer y acompañar el proceso de aprendizaje de cada estudiantes teniendo en cuenta sus emociones a lo largo de todo el proceso (Ma, Sun, Song, Li, & Xu, 2018;O'Reilly, 2015;Yang, Alsadoon, Prasad, Singh, & Elchouemi, 2018). Surgen también desafíos éticos para responder correctamente a este tipo de nuevos reconocimientos y acompañamientos mediados por tecnologías digitales.…”

Section: éTica Educación Y Tecnologíaunclassified

Pedagogías emergentes en ambientes virtuales de aprendizaje

Aparicio-Gómez¹,

Ostos-Ortiz²

2021

RIPIE

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Las pedagogías emergentes hacen su aparición en el escenario de los ambientes virtuales de aprendizaje en tiempos de gran incertidumbre. Las pedagogías emergentes apoyan el proceso educativo de las personas principalmente por medio de la promoción del aprendizaje continuo con tecnología y recursos educativos abiertos ante de grandes volúmenes de información y en contextos de redes de comunicación. Las metodologías activas para el aprendizaje coadyuvan en la gran labor de educar a partir de iniciativas provenientes de diversos lugares del mundo; así, el design thinking para resolver problemas, el llamado teachback para aprender a través de la conversación, el flipped learning para el acompañamiento y la independencia, la gamification para aprender a través de juegos y las social media para el aprendizaje en red, se hacen cada vez más recurrentes en las prácticas pedagógicas. Por otra parte, la evaluación de los aprendizajes comienza a considerar cada vez más la relevancia de las analíticas de aprendizaje, el blockchain, así como las insignias para acreditar el aprendizaje, en un contexto de evaluación discreta y formativa. Finalmente, las consideraciones éticas en torno al uso y la apropiación de las TIC en educación se hacen cada vez más necesarias para establecer los nexos entre pedagogía y tecnología; de esta manera, el aprendizaje basado en el contexto y, por lo tanto, también situado en el contexto de las historias personales, otorgando especial prevalencia al aprendizaje desde las emociones y los valores, requiere de todos los actores del proceso educativo que se acompañe el proceso de los niños y jóvenes desde una adecuada construcción del conocimiento desde una correcta argumentación en torno a sus intereses y necesidades.

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“…Chao ma et.al [5] proposed a facial emotion recognition system in an online learning environment. This study captured a student facial image through webcam, judges the student learning emotions and provide feedback to the lecturer.…”

Section: Related Workmentioning

confidence: 99%

Classroom Student Emotions Classification from Facial Expressions and Speech Signals using Deep Learning

Sharma*¹,

Mansotra²

2019

IJRTE

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Classroom environment is a competent platform for the students to learn and improve their understanding of the subject. An instructor’s primary responsibility lies in managing the students in a way they feel interested and focused during the class. With the aid of automated systems based on artificial intelligence, an instructor can get feedback on the students’ attention span in the class by monitoring their emotions using learning algorithms that can prove to be effective to improve the teaching style of the instructor that can in turn have positive effects on the class. In this paper, we propose an LSTM recurrent neural network trained on an emotional corpus database to extract the speech features and convolutional neural networks trained on the FER2013 facial emotion recognition database were used to predict the speech and facial emotions of the students respectively, in real-time. The live video and audio sequence of the students captured is fed to the learned model to classify the emotions individually. Once the emotions such as anger, sadness, happiness, surprise, fear, disgust and neutral were identified, a decision-making mechanism was used to analyze the predicted emotions and choose the overall group emotion by virtue of the highest peak value achieved. This research approach has the potential to be deployed in video conferences, online classes etc. This implementation proposal should effectively improve the classification accuracy and the relatability of the detected student emotions and facilitate in the design of sophisticated automated learning systems that can be a valuable tool in evaluating both the students and the instructors. The adapted research methodologies and their results are discussed and found to perform suggestively better than the other research works used in the comparison

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A Deep Learning Approach for Online Learning Emotion Recognition

Cited by 11 publications

References 5 publications

Robotics Utilization in Automatic Vision-Based Assessment Systems From Artificial Intelligence Perspective: A Systematic Review

Robotics Utilization in Automatic Vision-Based Assessment Systems From Artificial Intelligence Perspective: A Systematic Review

Pedagogías emergentes en ambientes virtuales de aprendizaje

Classroom Student Emotions Classification from Facial Expressions and Speech Signals using Deep Learning

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