Customizing an Affective Tutoring System Based on Facial Expression and Head Pose Estimation (original) (raw)
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affective tutoring system for Better Learning
2009
Intelligent tutoring systems (ITS) are still not as effective as one-on-one human tutoring. The next generation of intelligent tutors are expected to be able to take into account the emotional state of students. This paper presents research on the development of an Affective Tutoring System (ATS). The system called "Easy with Eve" adapts to students via a lifelike animated agent who is able to detect student emotion through facial expression analysis, and can display emotion herself. Eve's adaptations are guided by a case-based method for adapting to student states; this method uses data that was generated by an observational study of human tutors. This paper presents an analysis of facial expressions of students engaged in learning with human tutors and how a facial expression recognition system, a life like agent and a case based system based on this analysis have been integrated to develop an ATS for mathematics.
The integration of an emotional system in the Intelligent tutoring system
The 3rd ACS/IEEE International Conference onComputer Systems and Applications, 2005., 2005
In this article, we present a new architecture of the Intelligent Tutoring System (ITS) and we suggest an original method, which allows recognizing the expression of the learner's face during exercise. It helps evaluate his affective state in an emotional system in order to distinguish his influence on his responses. Accordingly, we should first be able to detect his face, extract his important features translating the state of his expression (characteristical features: eyes and mouth,), then we should analyse their configuration and characteristics in order to recognize the expression, which describe and interpret it. Our architecture is based on the observation of the behaviour of the learner; detect engaging signs so as to detect affective responses, which can be the manifestation of feelings of interest, excitement and confusion. From the observation and the identification of the emotional state of the learner, the tutor can undertake actions which will influence the quality of learning and its execution (important remarks may reduce the feeling of failure of the learner or avoid the risk of interrupting his work as soon as he feels bored), A Scientific and especially emotional analyse will be necessary to evaluate and help the learner during exercise.
A Facial Expression Analysis Component for Affective Tutoring Systems
ICWI, 2003
Intelligent tutoring systems (ITS) provide individualised instruction. They offer many advantages over the traditional classroom scenario: they are always available, non-judgemental and provide tailored feedback resulting in increased and effective learning. However, they are still not as effective as one-on-one human tutoring. The next generation of intelligent tutors are expected to be able to take into account the cognitive and emotional state of students. This paper reports on the progress made in the development of a facial expression analysis component for intelligent tutoring systems. A digital camera is used to grab the image of the learner and signal processing techniques such as Wavelet and ANN are employed to extract facial expressions. We have developed algorithms for detecting learner's face and locating permanent facial features such as eyebrow, eyes, and mouth. Facial expression analysis is performed based on both permanent and transient facial features in a nearly frontal-view face image sequence. This information will be added to the student's knowledge state model. Other non-verbal interactions like heartbeat and eye and body movement will be used in the future to enhance the performance of the system. This will enable intelligent tutors to react to changes in student's state reflected in student models. We have called this new generation of intelligent tutors, "affective tutoring systems".
2005
At the dawn of approaching information society, there is an ever-growing research interest on the field of elearning which drive attention of researchers from diverse area of disciplines, such as computer science, signal processing, psychology, education, etc. This is a reflection of a concrete consensus that Human Computer Interaction (HCI) is going to have a great impact on education in the future [1]. However, even though there are large numbers of studies on HCI, the affective aspects in HCI studies are still neglected [2]. It is argued that the current state of e-learning can be improved via inserting the adaptability and interactivity, which will be obtained by enhanced human-computer interaction, into student model [3,4]. This will facilitate the acceptance and credibility of the new generation of educational environments. In this paper, it is postulated that the possibility of integration of recent development in affective and cognitive computations will have a great impact in e-learning processes on education in the future.
The Transition From Intelligent to Affective Tutoring System: A Review and Open Issues
IEEE Access, 2020
The swelling use of computerized learning, accompanied by the rapid growth of information technology has become a surge of interest in the research community. Consequently, several technologies have been developed to maintain and promote computerized learning. In this study, we provided an indepth analysis of two of the prominent computerized learning systems i.e., Intelligent Tutoring System (ITS) and Affective Tutoring System (ATS). An ITS is one of the training software systems, which use intelligent technologies to provide personalized learning content to students based on their learning needs with the aim of enhancing the individualized learning experience. Recently, researchers have demonstrated that the affect or emotional states of a student have an impact on the overall performance of his/her learning, which introduces a new trend of ITS development termed as ATS, which is the extended research of the previous one. Although there have been several studies on these tutoring systems, however, none of them has comprehensively analyzed both systems, particularly the transition from ITS to ATS. Therefore, this study examines these two tutoring systems more inclusively with regards to their architectures, models, and techniques and approaches used by taking into consideration the related researches conducted between 2014 to 2019. A crucial finding from the study is that ATS can be a promising tutoring system for the next generation learning environment by affiliating proper emotion recognition channels, along with computational intelligence approaches. Finally, this study concludes with research challenges and possible future directions and trends. INDEX TERMS Intelligent tutoring system, affective tutoring system, tutoring system in education, affective computing in education, artificial intelligence in education, educational technology.
This chapter describes how a machine vision approach could be utilized for tracking learning feedback information on emotions for enhanced teaching and learning with Intelligent Tutoring Systems (ITS). The chapter focuses on analyzing learners’ emotions to show how affective states account for personalization or traceability for learning feedback. The chapter achieves this goal in three ways: (1) by presenting a comprehensive review of adaptive educational learning systems, particularly inspired by machine vision approaches; (2) by proposing an affective model for monitoring learners’ emotions and engagement with educational learning systems; (3) by presenting a case-based technique as an experimental prototype for the proposed affective model, where students’ facial expressions are tracked in the course of studying a composite video lecture. Results of the experiments indicate the superiority of such emotion-aware systems over emotion-unaware ones, achieving a significant performance increment of 71.4%.
Emotions during learning: The first steps toward an affect sensitive intelligent tutoring system.In
2004
Abstract. In an attempt to discover links between learning and emotions, this study adopted an emote-aloud procedure where participants were recorded as they verbalized their affective states while interacting with an intelligent tutoring system, AutoTutor. Participants ’ facial expressions were coding using the Facial Action Coding System and analyzed using association rule mining techniques. The resulting rules are discussed along with implications to the larger project of improving the AutoTutor system into a nonintrusive affect sensitive intelligent tutoring system. While the 20 th century has been ripe with learning theory, these theories have mostly ignored the importance of the link between a persons emotions or affective states and learning (Meyer, & Turner, 2002). However, toward the end of the twentieth century, emotions started to get more attention. Some seminal contributions to the literature include the facial action coding system by Ekman & Friesen (1978), Stein and L...
Emotions during learning: The first steps toward an affect sensitive intelligent tutoring system
Proceedings of E- …, 2004
In an attempt to discover links between learning and emotions, this study adopted an emote-aloud procedure where participants were recorded as they verbalized their affective states while interacting with an intelligent tutoring system, AutoTutor. Participants' facial expressions were coding using the Facial Action Coding System and analyzed using association rule mining techniques. The resulting rules are discussed along with implications to the larger project of improving the AutoTutor system into a nonintrusive affect sensitive intelligent tutoring system.
Integrating affect sensors in an intelligent tutoring system
… : The Computer in …, 2005
This project augments an existing intelligent tutoring system (AutoTutor) that helps learners construct explanations by interacting with them in natural language and helping them use simulation environments. The research aims to develop an agile learning environment that is sensitive to a learner's affective state, presuming that this will promote learning. We integrate state-of-the-art, nonintrusive, affect-sensing technology with AutoTutor in an endeavor to classify emotions on the bases of facial expressions, gross body movements, and conversational cues. This paper sketches our broad theoretical approach, our methods for data collection and evaluation, and our emotion classification techniques.
E-Learner's Academic Emotions Based on Facial Expressions : A Survey
International Journal of Scientific Research in Science, Engineering and Technology, 2020
This paper presents different technologies and framework used for academic emotion detection using facial recognition in E-Learning. E-Learning is growing day by day for various reasons like distance learning and user is able to do it at anytime and anywhere. But E-Learning lacks in real time feedback from the students to teachers and vice-versa. Academic Emotion plays an important role on detecting whether the students have understood the topic or not. In face to face learning, a skilled teacher achieves affective domain goals by interacting with the students and asking them questions. But in online learning student and teacher are apart so if system itself finds the emotion and take the action accordingly, is really very helpful to teacher and student both. There are various ways like sensors, facial expressions, log usage are used by many scientists to achieve this. We have researched and read many papers about various frameworks used and found that academic emotions play a vital role and also makes big difference in learning if it is properly analyzed and suitable action is taken. A model for the same purpose has been proposed here which will detect emotion and generate feedback accordingly.