The Affective Tutoring System

Ammar, Mohamed; Néji, Mahmoud; Alimi, Adel M.; Gouardères, Guy

doi:10.1016/j.eswa.2009.09.031

Cited by 140 publications

(78 citation statements)

References 18 publications

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“…And so, a computer application is able to recognize users' facial emotions and can improve its feedback to learners without involvement of a human teacher. There is a growing body of research on ATS which stresses the importance of our approach using facial expressions for deriving emotions (Ben Ammar, Neji, Alimi, & Gouardères, 2010;Sarrafzadeh et al, 2008). Sebe (2009) reports that the most informative channel for computer awareness of emotions is through facial expressions.…”

Section: Introductionmentioning

confidence: 99%

Towards multimodal emotion recognition in e-learning environments

Bahreini

Nadolski

Westera

2014

Interactive Learning Environments

106

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This paper presents a framework (FILTWAM (Framework for Improving Learning Through Webcams And Microphones)) for real-time emotion recognition in e-learning by using webcams. FILTWAM offers timely and relevant feedback based upon learner's facial expressions and verbalizations. FILTWAM's facial expression software module has been developed and tested in a proof-of-concept study. The main goal of this study was to validate the use of webcam data for a real-time and adequate interpretation of facial expressions into extracted emotional states. The software was calibrated with 10 test persons. They received the same computer-based tasks in which each of them were requested 100 times to mimic specific facial expressions. All sessions were recorded on video. For the validation of the face emotion recognition software, two experts annotated and rated participants' recorded behaviours. Expert findings were contrasted with the software results and showed an overall value of kappa of 0.77. An overall accuracy of our software based on the requested emotions and the recognized emotions is 72%. Whereas existing software only allows not-real time, discontinuous and obtrusive facial detection, our software allows to continuously and unobtrusively monitor learners' behaviours and converts these behaviours directly into emotional states. This paves the way for enhancing the quality and efficacy of e-learning by including the learner's emotional states.

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Section: Introductionmentioning

confidence: 99%

Towards multimodal emotion recognition in e-learning environments

Bahreini

Nadolski

Westera

2014

Interactive Learning Environments

106

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“…Most ITS employ curriculum sequencing based on student knowledge and also more recently user affect factors such as emotion [12], personality [13] and learning style [4]. Few ITS incorporate all three techniques as they are complex and time-consuming to develop, but the Oscar CITS presented in this paper will incorporate all three intelligent technologies by personalising learning material and discussing problems and solutions with students.…”

Section: Intelligent Tutoring Systemsmentioning

confidence: 97%

Oscar: An Intelligent Adaptive Conversational Agent Tutoring System

Latham

Crockett

McLean

et al. 2011

Agent and Multi-Agent Systems: Technologies and Applications

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Abstract. This paper presents an adaptive online intelligent tutoring system called Oscar which leads a tutoring conversation and dynamically predicts and adapts to a student's learning style. Oscar aims to mimic a human tutor by using knowledge of learning styles to adapt its tutoring style and improve the effectiveness of the learning experience. Learners can intuitively explore and discuss topics in natural language, helping to establish a deeper understanding of the topic and boost confidence. An initial study into the adaptation to learning styles is reported which produced encouraging results and positive test score improvements.

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“…They could benefit from 63 having knowledge of the user's emotion, especially with regards to 64 delivering more user-centric actions. Facial expression recognition 65 is a key point in the wave of research within HCI that aims to trans-66 late the user's emotional experience into a meaningful feedback, in 67 various areas, such as from a patient (e.g., suffering, annoyed, 68 depressed or comfortable) (Khosla et al, 2012;Lau, 2010), a student 69 (e.g., interested, puzzled or boring) (Bahreini,Nadolski,& Westera,70 2014; Ben Ammar, Neji, Alimi, & Gouardères, 2010;Whitehill, 71 Bartlett, & Movellan, 2008), or a customer (e.g., satisfied) 72 (Puccinelli, Motyka, & Grewal, 2010). 73 In a pioneer work, the psychologist Ekman and his colleagues 74 conducted extensive studies on how people from different races 75 express their emotions through facial expressions (Ekman, 1971; 76 Ekman & Friesen, 1976, 1977.…”

mentioning

confidence: 99%