Multimodal Affect Recognition in Intelligent Tutoring Systems

Banda, Ntombikayise; Robinson, Peter

doi:10.1007/978-3-642-24571-8_21

Cited by 13 publications

(7 citation statements)

References 16 publications

(14 reference statements)

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“…The model was designed with a standardised curriculum for this reason. They can be divided into four types; achievement emotions, topic emotions, social emotions and epistemic emotions (Pekrun, 2010;D'Mello, 2011). Currently, Open edX has no facility to integrate specification of learning objectives against learning objects.…”

Section: Resultsmentioning

confidence: 99%

User Modeling, Adaptation and Personalization

Houben¹,

McCalla²,

Pianesi³

et al. 2015

Lecture Notes in Computer Science

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Recommender systems face difficulty in cold-start scenarios where a new user has provided only few ratings. Improving cold-start performance is of great interest. At the same time, the growing number of adaptive systems makes it ever more likely that a new user in one system has already been a user in another system in related domains. To what extent can a user model built by one adaptive system help address a cold start problem in another system? We compare methods of cross-system user model transfer across two large real-life systems: we transfer user models built for information seeking of scientific articles in the SciNet exploratory search system, operating over tens of millions of articles, to perform cold-start recommendation of scientific talks in the CoMeT talk management system, operating over hundreds of talks. Our user study focuses on transfer of novel explicit open user models curated by the user during information seeking. Results show strong improvement in cold-start talk recommendation by transferring open user models, and also reveal why explicit open models work better in cross-domain context than traditional hidden implicit models.

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Section: Resultsmentioning

confidence: 99%

User Modeling, Adaptation and Personalization

Houben¹,

McCalla²,

Pianesi³

et al. 2015

Lecture Notes in Computer Science

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“…Banda and Robinson present a multimodal solution for affect analysis (FACS cognitive and affective mental states in 6 classes -agreeing, concentrating, disagreeing, interested, thinking and unsure) in the scope of an ITS, fusing automatic facial expression recognition, head gestures and audio analysis [42]. The aim of the system is to generate such affective response of the intelligent tutor that could keep the students' FaceReader software is used to analyze the emotional face expressions during an assessment session [43].…”

Section: Facial Expression Recognition In Tutoring Systemmentioning

confidence: 99%

Researching affective computing techniques for intelligent tutoring systems

Ivanova

2013

2013 International Conference on Interactive Collaborative Learning (ICL)

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Intelligent Tutoring is applied as a supportive tool in decision making, problem solving, knowledge receiving. Adding computer vision for affect recognition leads to the adaptation of tutor behavior not only to the cognitive level of a student but also to his emotional state that could improve quality of learning. In this paper the current research in the area of facial expression and gesture recognition in the context of intelligent tutoring is examined with the aim to facilitate educational society in building of affective intelligent tutoring systems.

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“…More precisely, the emergence of the affective computing domain has made a huge change in the design of such environments by enhancing their capabilities to understand the learners' needs and behaviors [1][2][3][4][5]. Research in the Intelligent Tutoring Systems (ITS) field is increasingly directed towards the integration of new techniques that can provide relevant indicators about the learners' internal and affective states.…”

Section: Introductionmentioning

confidence: 99%

MENTOR: A Physiologically Controlled Tutoring System

Chaouachi

Jraidi

Frasson

2015

Lecture Notes in Computer Science

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Abstract. In this paper we present a tutoring system that automatically sequences the learning content according to the learners' mental states. The system draws on techniques from Brain Computer Interface and educational psychology to automatically adapt to changes in the learners' mental states such as attention and workload using electroencephalogram (EEG) signals. The objective of this system is to maintain the learner in a positive mental state throughout the tutoring session by selecting the next pedagogical activity that fits the best to his current state. An experimental evaluation of our approach involving two groups of learners showed that the group who interacted with the mental state-based adaptive version of the system obtained higher learning outcomes and had a better learning experience than the group who interacted with a non-adaptive version.Keywords: Intelligent tutoring system · Engagement · Workload · Real-time adaptive system · EEG · Machine learning · Experience and affect IntroductionThe use of physio-cognitive sensing technologies in computer-based learning environments has grown continuously through these last years. More precisely, the emergence of the affective computing domain has made a huge change in the design of such environments by enhancing their capabilities to understand the learners' needs and behaviors [1][2][3][4][5]. Research in the Intelligent Tutoring Systems (ITS) field is increasingly directed towards the integration of new techniques that can provide relevant indicators about the learners' internal and affective states. In fact, one of the main objectives of ITS is to provide an adapted and individualized learning environment to the learner. This adaptation can be operated with regards to several considerations (cognitive, educational, emotional, social, etc.), and can be related to different aspects of the system's interaction strategy (selection of the next learning step, providing an individualized feedback, or help, etc.). The integration of physiological data sources can represent a genuine opportunity for such a system to extract valuable information about the user's state and to proactively adapt to this state. In this paper, we present a new ITS called MENTOR (MENtal tuTOR), which is entirely based on the analysis of the learner's engagement and workload, extracted from the EEG data, in order to sequence the learning activities. More precisely, the system relies on an adaptive logic that selects the next pedagogical activity which fits

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Multimodal Affect Recognition in Intelligent Tutoring Systems

Cited by 13 publications

References 16 publications

User Modeling, Adaptation and Personalization

User Modeling, Adaptation and Personalization

Researching affective computing techniques for intelligent tutoring systems

MENTOR: A Physiologically Controlled Tutoring System

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