The role that affective states play in learning was investigated from the perspective of a constructivist learning framework. We observed six different affect states (frustration, boredom, flow, confusion, eureka and neutral) that potentially occur during the process of learning introductory computer literacy with AutoTutor, an intelligent tutoring system with tutorial dialogue in natural language. Observational analyses revealed significant relationships between learning and the affective states of boredom, flow and confusion. The positive correlation between confusion and learning is consistent with a model that assumes that cognitive disequilibrium is one precursor to deep learning. The findings that learning correlates negatively with boredom and positively with flow are consistent with predictions from Csikszentmihalyi’s analysis of flow experiences.

We examine the utility of speech and lexical features for predicting student emotions in computerhuman spoken tutoring dialogues. We first annotate student turns for negative, neutral, positive and mixed emotions. We then extract acoustic-prosodic features from the speech signal, and lexical items from the transcribed or recognized speech. We compare the results of machine learning experiments using these features alone or in combination to predict various categorizations of the annotated student emotions. Our best results yield a 19-36 % relative improvement in error reduction over a baseline. Finally, we compare our results with emotion prediction in human-human tutoring dialogues. 1

This paper reports work in progress to build a Learning Companion, a computerized system sensitive to the affective aspects of learning, which facilitates the child’s own efforts at learning. Learning related to science, math, engineering, and technology naturally involves failure and a host of associated affective responses. This article describes techniques and tools being developed to recognize affective states important in the interplay between emotions and learning.

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.

Abstract. This paper presents the first statistically reliable empirical evidence from a controlled study for the effect of human-provided emotional scaffolding on student persistence in an intelligent tutoring system. We describe an experiment that added human-provided emotional scaffolding to an automated Reading Tutor that listens, and discuss the methodology we developed to conduct this experiment. Each student participated in one (experimental) session with emotional scaffolding, and in one (control) session without emotional scaffolding, counterbalanced by order of session. Each session was divided into several portions. After each portion of the session was completed, the Reading Tutor gave the student a choice: continue, or quit. We measured persistence as the number of portions the student completed. Human-provided emotional scaffolding added to the automated Reading Tutor resulted in increased student persistence, compared to the Reading Tutor alone. Increased persistence means increased time on task, which ought lead to improved learning. If these results for reading turn out to hold for other domains too, the implication for intelligent tutoring systems is that they should respond with not just cognitive support – but emotional scaffolding as well. Furthermore, the general technique of adding human-supplied capabilities to an existing intelligent tutoring system should prove useful for studying other ITSs too.

This paper investigates the reliability of detecting a learner’s affective states in an attempt to augment an Intelligent Tutoring System (AutoTutor) with the ability to incorporate such states into its pedagogical strategies to improve learning. We describe two studies that used observational and emote-aloud protocols in order to identify the affective states that learners experience while interacting with AutoTutor. In a third study, training and validation data were collected from three sensors in a learning session with AutoTutor, after which the affective states of the learner were identified by the learner, a peer, and two trained judges. The third study assessed the reliability of automatic detection of boredom, confusion, delight, flow, and frustration (versus the neutral baseline) from sensors that monitored the manner in which learners communicate affect through conversational cues, gross body language, and facial expressions. Although the primary focus of this article is on the classification of learner affect, we also explore how an affect-sensitive AutoTutor can adapt its instructional strategies to promote learning. 1.

Recent research shows that instructors commonly use politeness strategies to achieve affective scaffolding in educational contexts. The importance of affective factors such as self-confidence and interest that contribute to learner motivation is well recognized. In this paper, we describe the results of a Wizard-of-Oz experiment to study the effect of politeness strategies on both cognitive and motivational factors. We compare the results of two different politeness strategies, direct and polite, in assisting seventeen students in a computer-based learning task. We find that politeness can affect students ’ motivational state and help students learn difficult concepts. The results of the experiment provide a basis for the design of a polite pedagogical agent and its tutorial intervention strategies. Categories and Subject Descriptors H.5.2 [Information Interfaces and Presentation]: User Interfaces – evaluation/methodology, graphical user interfaces,

This paper evaluates dialogue-based student performance in a controlled experiment using versions of a tutoring system with and without automatic adaptation to the student affective state of uncertainty. Our performance metrics include correctness, uncertainty, and learning impasse severities, which are measured in a “test ” dialogue after the tutoring treatment. Although these metrics did not significantly differ across conditions when considering all student answers in our test dialogue, we found significant differences in specific types of student answers, and these differences suggest that our uncertainty adaptation does have a positive benefit on student performance.

Abstract. In the first of two experimental studies, 312 students were randomly assigned to one of 8 conditions, where agents differed by ethnicity (Black, White), gender (male, female), and image (realistic, cartoon), yet had identical messages and computer-generated voice. In the second study, 229 students were randomly assigned to one of 12 conditions where agents represented different instructional roles (expert, motivator, and mentor), also differing by ethnicity (Black, White), and gender (male, female). Overall, it was found that students had greater transfer of learning when the agents had more realistic images and when agents in the “expert ” role were represented non-traditionally (as Black versus White). Results also generally confirmed prior research where agents perceived as less intelligent lead to significantly improved self-efficacy. The presence of motivational messages, as employed through the motivator and mentor agent roles, led to enhanced learner self-regulation and self-efficacy. Results are discussed with respect to social cognitive theory. 1

The use of the computer as a model, metaphor, and modelling tool has tended to privilege the ‘cognitive ’ over the ‘affective ’ by engendering theories in which thinking and learning are viewed as information processing and affect is ignored or marginalised. In the last decade there has been an accelerated flow of findings in multiple disciplines supporting a view of affect as complexly intertwined with cognition in guiding rational behaviour, memory retrieval, decision-making, creativity, and more. It is time to redress the imbalance by developing theories and technologies in which affect and cognition are appropriately integrated with one another. This paper describes work in that direction at the MIT Media Lab and projects a large perspective of new research in which computer technology is used to redress the imbalance that was caused (or, at least, accentuated) by the computer itself. 1. Vision The last half-century of technological acceleration has yielded a massive incursion of digital technology into the learning environment, making dramatic differences, and promising even greater changes, to the practice of learning. Computers have served as tools to aid in learning at all levels from simple classroom activities to the way theorists think about thinking. The field of artificial intelligence, with emphasis on ideas such