When Does Disengagement Correlate with Performance in Spoken Dialog Computer Tutoring?

In this paper we investigate how student disengagement relates to two performance metrics in a spoken dialog computer tutoring corpus, both when disengagement is measured through manual annotation by a trained human judge, and also when disengagement is measured through automatic annotation by the system based on a machine learning model. First, we investigate whether manually labeled overall disengagement and six different disengagement types are predictive of learning and user satisfaction in the corpus. Our results show that although students’ percentage of overall disengaged turns negatively correlates both with the amount they learn and their user satisfaction, the individual types of disengagement correlate differently: some negatively correlate with learning and user satisfaction, while others don’t correlate with eithermetric at all. Moreover, these relationships change somewhat depending on student prerequisite knowledge level. Furthermore, using multiple disengagement types to predict learning improves predictive power. Overall, these manual label-based results suggest that although adapting to disengagement should improve both student learning and user satisfaction in computer tutoring, maximizing performance requires the system to detect and respond differently based on disengagement type. Next, we present an approach to automatically detecting and responding to user disengagement types based on their differing correlations with correctness. Investigation of ourmachine learningmodel of user disengagement shows that its automatic labels negatively correlate with both performance metrics in the same way as the manual labels. The similarity of the correlations across the manual and automatic labels suggests that the automatic labels are a reasonable substitute for the manual labels. Moreover, the significant negative correlations themselves suggest that redesigning ITSPOKE to automatically detect and respond to disengagement has the potential to remediate disengagement and thereby improve performance, even in the presence of noise introduced by the automatic detection process

Eliciting and annotating uncertainty in spoken language

A major challenge in the field of automatic recognition of emotion and affect in speech is the subjective nature of affect labels. The most common approach to acquiring affect labels is to ask a panel of listeners to rate a corpus of spoken utterances along one or more dimensions of interest. For applications ranging from educational technology to voice search to dictation, a speaker’s level of certainty is a primary dimension of interest. In such applications, we would like to know the speaker’s actual level of certainty, but past research has only revealed listeners’ perception of the speaker’s level of certainty. In this paper, we present a method for eliciting spoken utterances using stimuli that we design such that they have a quantitative, crowdsourced legibility score. While we cannot control a speaker’s actual internal level of certainty, the use of these stimuli provides a better estimate of internal certainty compared to existing speech corpora. The Harvard Uncertainty Speech Corpus, containing speech data, certainty annotations, and prosodic features, is made available to the research community.Engineering and Applied Science

A conceptual framework for an affective tutoring system using unobtrusive affect sensing for enhanced tutoring outcomes

PhD ThesisAffect plays a pivotal role in influencing the student’s motivation and learning achievements. The ability of expert human tutors to achieve enhanced learning outcomes is widely attributed to their ability to sense the affect of their tutees and to continually adapt their tutoring strategies in response to the dynamically changing affect throughout the tutoring session. In this thesis, I explore the feasibility of building an Affective Tutoring System (ATS) which senses the student’s affect on a moment-to-moment basis with the use of unobtrusive sensors in the context of computer programming tutoring. The novel use of keystrokes and mouse clicks for affect sensing is proposed here as they are ubiquitous and unobtrusive. I first establish the viability of using keystrokes and contextual logs for affect sensing first on a per exercise session level and then on a more granular basis of 30 seconds. Subsequently, I move on to investigate the use of multiple sensing channels e.g. facial, keystrokes, mouse clicks, contextual logs and head postures to enhance the availability and accuracy of sensing. The results indicated that it is viable to use keystrokes for affect sensing. In addition, the combination of multiple sensor modes enhances the accuracy of affect sensing. From the results, the sensor modes that are most significant for affect sensing are the head postures and facial modes. Nevertheless, keystrokes make up for the periods of unavailability of the former. With the affect sensing (both sensing of frustration and disengagement) in place, I moved on to architect and design the ATS and conducted an experimental study and a series of focus group discussions to evaluate the ATS. The results showed that the ATS is rated positively by the participants for usability and acceptance. The ATS is also effective in enhancing the learning of the studentsNanyang Polytechni

Designing Embodied Interactive Software Agents for E-Learning: Principles, Components, and Roles

Embodied interactive software agents are complex autonomous, adaptive, and social software systems with a digital embodiment that enables them to act on and react to other entities (users, objects, and other agents) in their environment through bodily actions, which include the use of verbal and non-verbal communicative behaviors in face-to-face interactions with the user. These agents have been developed for various roles in different application domains, in which they perform tasks that have been assigned to them by their developers or delegated to them by their users or by other agents. In computer-assisted learning, embodied interactive pedagogical software agents have the general task to promote human learning by working with students (and other agents) in computer-based learning environments, among them e-learning platforms based on Internet technologies, such as the Virtual Linguistics Campus (www.linguistics-online.com). In these environments, pedagogical agents provide contextualized, qualified, personalized, and timely assistance, cooperation, instruction, motivation, and services for both individual learners and groups of learners. This thesis develops a comprehensive, multidisciplinary, and user-oriented view of the design of embodied interactive pedagogical software agents, which integrates theoretical and practical insights from various academic and other fields. The research intends to contribute to the scientific understanding of issues, methods, theories, and technologies that are involved in the design, implementation, and evaluation of embodied interactive software agents for different roles in e-learning and other areas. For developers, the thesis provides sixteen basic principles (Added Value, Perceptible Qualities, Balanced Design, Coherence, Consistency, Completeness, Comprehensibility, Individuality, Variability, Communicative Ability, Modularity, Teamwork, Participatory Design, Role Awareness, Cultural Awareness, and Relationship Building) plus a large number of specific guidelines for the design of embodied interactive software agents and their components. Furthermore, it offers critical reviews of theories, concepts, approaches, and technologies from different areas and disciplines that are relevant to agent design. Finally, it discusses three pedagogical agent roles (virtual native speaker, coach, and peer) in the scenario of the linguistic fieldwork classes on the Virtual Linguistics Campus and presents detailed considerations for the design of an agent for one of these roles (the virtual native speaker)

Complexity Science in Human Change

This reprint encompasses fourteen contributions that offer avenues towards a better understanding of complex systems in human behavior. The phenomena studied here are generally pattern formation processes that originate in social interaction and psychotherapy. Several accounts are also given of the coordination in body movements and in physiological, neuronal and linguistic processes. A common denominator of such pattern formation is that complexity and entropy of the respective systems become reduced spontaneously, which is the hallmark of self-organization. The various methodological approaches of how to model such processes are presented in some detail. Results from the various methods are systematically compared and discussed. Among these approaches are algorithms for the quantification of synchrony by cross-correlational statistics, surrogate control procedures, recurrence mapping and network models.This volume offers an informative and sophisticated resource for scholars of human change, and as well for students at advanced levels, from graduate to post-doctoral. The reprint is multidisciplinary in nature, binding together the fields of medicine, psychology, physics, and neuroscience

Developing through relationships origins of communication, self, and culture

Journal ArticleI began to consider the study of relationships as an intellectual vocation in 1970, the result of two years of college teaching that was part of my work as a United States Peace Corps volunteer in Bogota, Colombia. After another year I began my doctoral training in the Department of Education at the University of Chicago, working on Kenneth Kaye's mother-infant communication studies and struggling to fill the gaps in my knowledge of developmental psychology left by undergraduate and master's degrees in physics and mathematics. I am still struggling, as I believe all professionals struggle, with incompleteness and ambiguity, wavering between conviction and uncertainty. The work that follows is part of an ongoing learning process. Apart from what I have said about these limitations in the body of the text I can also add that it feels finished enough for now, ready for public scrutiny, but open to revision in the future. This book is the product not only of the year over which the writing took place, but also of the past twenty years of my professional development and of my personal life history

Student Modeling in Intelligent Tutoring Systems

After decades of development, Intelligent Tutoring Systems (ITSs) have become a common learning environment for learners of various domains and academic levels. ITSs are computer systems designed to provide instruction and immediate feedback, which is customized to individual students, but without requiring the intervention of human instructors. All ITSs share the same goal: to provide tutorial services that support learning. Since learning is a very complex process, it is not surprising that a range of technologies and methodologies from different fields is employed. Student modeling is a pivotal technique used in ITSs. The model observes student behaviors in the tutor and creates a quantitative representation of student properties of interest necessary to customize instruction, to respond effectively, to engage students¡¯ interest and to promote learning. In this dissertation work, I focus on the following aspects of student modeling. Part I: Student Knowledge: Parameter Interpretation. Student modeling is widely used to obtain scientific insights about how people learn. Student models typically produce semantically meaningful parameter estimates, such as how quickly students learn a skill on average. Therefore, parameter estimates being interpretable and plausible is fundamental. My work includes automatically generating data-suggested Dirichlet priors for the Bayesian Knowledge Tracing model, in order to obtain more plausible parameter estimates. I also proposed, implemented, and evaluated an approach to generate multiple Dirichlet priors to improve parameter plausibility, accommodating the assumption that there are subsets of skills which students learn similarly. Part II: Student Performance: Student Performance Prediction. Accurately predicting student performance is one of the most desired features common evaluations for student modeling. for an ITS. The task, however, is very challenging, particularly in predicting a student¡¯s response on an individual problem in the tutor. I analyzed the components of two common student models to determine which aspects provide predictive power in classifying student performance. I found that modeling the student¡¯s overall knowledge led to improved predictive accuracy. I also presented an approach, which, rather than assuming students are drawn from a single distribution, modeled multiple distributions of student performances to improve the model¡¯s accuracy. Part III: Wheel-spinning: Student Future Failure in Mastery Learning. One drawback of the mastery learning framework is its possibility to leave a student stuck attempting to learn a skill he is unable to master. We refer to this phenomenon of students being given practice with no improvement as wheel-spinning. I analyzed student wheel-spinning across different tutoring systems and estimated the scope of the problem. To investigate the negative consequences of see what wheel-spinning could have done to students, I investigated the relationships between wheel-spinning and two other constructs of interest about students: efficiency of learning and ¡°gaming the system¡±. In addition, I designed a generic model of wheel-spinning, which uses features easily obtained by most ITSs. The model can be well generalized to unknown students with high accuracy classifying mastery and wheel-spinning problems. When used as a detector, the model can detect wheel-spinning in its early stage with satisfying satisfactory precision and recall

Identifying Social Signals from Human Body Movements for Intelligent Technologies

Numerous Human-Computer Interaction (HCI) contexts require the identification of human internal states such as emotions, intentions, and states such as confusion and task engagement. Recognition of these states allows for artificial agents and interactive systems to provide appropriate responses to their human interaction partner. Whilst numerous solutions have been developed, many of these have been designed to classify internal states in a binary fashion, i.e. stating whether or not an internal state is present. One of the potential drawbacks of these approaches is that they provide a restricted, reductionist view of the internal states being experienced by a human user. As a result, an interactive agent which makes response decisions based on such a binary recognition system would be restricted in terms of the flexibility and appropriateness of its responses. Thus, in many settings, internal state recognition systems would benefit from being able to recognize multiple different ‘intensities’ of an internal state. However, for most classical machine learning approaches, this requires that a recognition system be trained on examples from every intensity (e.g. high, medium and low intensity task engagement). Obtaining such a training data-set can be both time- and resource-intensive. This project set out to explore whether this data requirement could be reduced whilst still providing an artificial recognition system able to provide multiple classification labels. To this end, this project first identified a set of internal states that could be recognized from human behaviour information available in a pre-existing data set. These explorations revealed that states relating to task engagement could be identified, by human observers, from human movement and posture information. A second set of studies was then dedicated to developing and testing different approaches to classifying three intensities of task engagement (high, intermediate and low) after training only on examples from the high and low task engagement data sets. The result of these studies was the development of an approach which incorporated the recently developed Legendre Memory Units, and was shown to produce an output which could be used to distinguish between all three task engagement intensities after being trained on only examples of high and low intensity task engagement. Thus this project presents the foundation work for internal state recognition systems which require less data whilst providing more classification labels