Multimodal Interaction in a Haptic Environment

In this paper we investigate the introduction of haptics in a multimodal tutoring environment. In this environment a haptic device is used to control a virtual piece of sterile cotton and a virtual injection needle. Speech input and output is provided to interact with a virtual tutor, available as a talking head, and a virtual patient. We introduce the haptic tasks and how different agents in the multi-agent system are made responsible for them. Notes are provided about the way we introduce an affective model in the tutor agent

Robust Modeling of Epistemic Mental States

This work identifies and advances some research challenges in the analysis of facial features and their temporal dynamics with epistemic mental states in dyadic conversations. Epistemic states are: Agreement, Concentration, Thoughtful, Certain, and Interest. In this paper, we perform a number of statistical analyses and simulations to identify the relationship between facial features and epistemic states. Non-linear relations are found to be more prevalent, while temporal features derived from original facial features have demonstrated a strong correlation with intensity changes. Then, we propose a novel prediction framework that takes facial features and their nonlinear relation scores as input and predict different epistemic states in videos. The prediction of epistemic states is boosted when the classification of emotion changing regions such as rising, falling, or steady-state are incorporated with the temporal features. The proposed predictive models can predict the epistemic states with significantly improved accuracy: correlation coefficient (CoERR) for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for Certain 0.854, and for Interest 0.913.Comment: Accepted for Publication in Multimedia Tools and Application, Special Issue: Socio-Affective Technologie

Determining what people feel and think when interacting with humans and machines

Any interactive software program must interpret the users’ actions and come up with an appropriate response that is intelligable and meaningful to the user. In most situations, the options of the user are determined by the software and hardware and the actions that can be carried out are unambiguous. The machine knows what it should do when the user carries out an action. In most cases, the user knows what he has to do by relying on conventions which he may have learned by having had a look at the instruction manual, having them seen performed by somebody else, or which he learned by modifying a previously learned convention. Some, or most, of the times he just finds out by trial and error. In user-friendly interfaces, the user knows, without having to read extensive manuals, what is expected from him and how he can get the machine to do what he wants. An intelligent interface is so-called, because it does not assume the same kind of programming of the user by the machine, but the machine itself can figure out what the user wants and how he wants it without the user having to take all the trouble of telling it to the machine in the way the machine dictates but being able to do it in his own words. Or perhaps by not using any words at all, as the machine is able to read off the intentions of the user by observing his actions and expressions. Ideally, the machine should be able to determine what the user wants, what he expects, what he hopes will happen, and how he feels

Towards the Use of Dialog Systems to Facilitate Inclusive Education

Continuous advances in the development of information technologies have currently led to the possibility of accessing learning contents from anywhere, at anytime, and almost instantaneously. However, accessibility is not always the main objective in the design of educative applications, specifically to facilitate their adoption by disabled people. Different technologies have recently emerged to foster the accessibility of computers and new mobile devices, favoring a more natural communication between the student and the developed educative systems. This chapter describes innovative uses of multimodal dialog systems in education, with special emphasis in the advantages that they provide for creating inclusive applications and learning activities

Affective learning: improving engagement and enhancing learning with affect-aware feedback

This paper describes the design and ecologically valid evaluation of a learner model that lies at the heart of an intelligent learning environment called iTalk2Learn. A core objective of the learner model is to adapt formative feedback based on students’ affective states. Types of adaptation include what type of formative feedback should be provided and how it should be presented. Two Bayesian networks trained with data gathered in a series of Wizard-of-Oz studies are used for the adaptation process. This paper reports results from a quasi-experimental evaluation, in authentic classroom settings, which compared a version of iTalk2Learn that adapted feedback based on students’ affective states as they were talking aloud with the system (the affect condition) with one that provided feedback based only on the students’ performance (the non-affect condition). Our results suggest that affect-aware support contributes to reducing boredom and off-task behavior, and may have an effect on learning. We discuss the internal and ecological validity of the study, in light of pedagogical considerations that informed the design of the two conditions. Overall, the results of the study have implications both for the design of educational technology and for classroom approaches to teaching, because they highlight the important role that affect-aware modelling plays in the adaptive delivery of formative feedback to support learning

A Talk on the Wild Side: The Direct and Indirect Impact of Speech Recognition on Learning Gains

Research in the learning sciences and mathematics education has suggested that ‘thinking aloud’ (verbalization) can be important for learning. In a technology-mediated learning environment, speech might also help to promote learning by enabling the system to infer the students’ cognitive and affective state so that they can be provided a sequence of tasks and formative feedback, both of which are adapted to their needs. For these and associated reasons, we developed the iTalk2Learn platform that includes speech production and speech recognition for children learning about fractions. We investigated the impact of iTalk2Learn’s speech functionality in classrooms in the UK and Germany, with our results indicating that a speech-enabled learning environment has the potential to enhance student learning gains and engagement, both directly and indirectly

Personalized face and gesture analysis using hierarchical neural networks

The video-based computational analyses of human face and gesture signals encompass a myriad of challenging research problems involving computer vision, machine learning and human computer interaction. In this thesis, we focus on the following challenges: a) the classification of hand and body gestures along with the temporal localization of their occurrence in a continuous stream, b) the recognition of facial expressivity levels in people with Parkinson's Disease using multimodal feature representations, c) the prediction of student learning outcomes in intelligent tutoring systems using affect signals, and d) the personalization of machine learning models, which can adapt to subject and group-specific nuances in facial and gestural behavior. Specifically, we first conduct a quantitative comparison of two approaches to the problem of segmenting and classifying gestures on two benchmark gesture datasets: a method that simultaneously segments and classifies gestures versus a cascaded method that performs the tasks sequentially. Second, we introduce a framework that computationally predicts an accurate score for facial expressivity and validate it on a dataset of interview videos of people with Parkinson's disease. Third, based on a unique dataset of videos of students interacting with MathSpring, an intelligent tutoring system, collected by our collaborative research team, we build models to predict learning outcomes from their facial affect signals. Finally, we propose a novel solution to a relatively unexplored area in automatic face and gesture analysis research: personalization of models to individuals and groups. We develop hierarchical Bayesian neural networks to overcome the challenges posed by group or subject-specific variations in face and gesture signals. We successfully validate our formulation on the problems of personalized subject-specific gesture classification, context-specific facial expressivity recognition and student-specific learning outcome prediction. We demonstrate the flexibility of our hierarchical framework by validating the utility of both fully connected and recurrent neural architectures