Search

This chapter describes the automatic recognition of and response to human emotion within intelligent tutors. Tutors can recognize student emotion with more than 80%accuracy compared to student self-reports, using wireless sensors that provide data about posture, movement, grip tension, facially expressed mental states and arousal. Pedagogical agents have been used that provide emotional or motivational feedback. Students using such agents increased their math value, self-concept and mastery orientation, with females reporting more confidence and less frustration. Low-achieving students–one third of whom have learning disabilities–report higher affective needs than their higher-achieving peers. After interacting with affective pedagogical agents, low-achieving students improved their affective outcomes and reported reduced frustration and anxiety. [ABSTRACT FROM AUTHOR]

A long standing challenge for intelligent tutoring system (ITS) designers and educators alike is how to encourage students to take pleasure and interest in learning activities. In this paper, we present findings from a user study involving students interacting with an ITS, focusing on when students express excitement, what we dub ˵yes!″ moments. These findings include an empirically-based user model that relies on both interaction and physiological sensor features to predict ˵yes!″ events; here we describe this model, its validation, and initial indicators of its importance for understanding and fostering student interest. [ABSTRACT FROM AUTHOR]

Recent progress has been made by using sensors with Intelligent Tutoring Systems in classrooms in order to predict the affective state of students users. If tutors are able to interpret sensor data with new students based on past experience, rather than having to be individually trained, then this will enable tutor developers to evaluate various methods of adapting to each student΄s affective state using consistent predictions. In the past, our classifiers have predicted student emotions with an accuracy between 78% and 87%. However, it is still unclear which sensors are best, and the educational technology community needs to know this to develop better than baseline classifiers, e.g. ones that use only frequency of emotional occurrence to predict affective state. This paper suggests a method to clarify classifier ranking for the purpose of affective models. The method begins with a careful collection of a training and testing set, each from a separate population, and concludes with a non-parametric ranking of the trained classifiers on the testing set. We illustrate this method with classifiers trained on data collected in the Fall of 2008 and tested on data collected in the Spring of 2009. Our results show that the classifiers for some affective states are significantly better than the baseline model; a validation analysis showed that some but not all classifier rankings generalize to new settings. Overall, our analysis suggests that though there is some benefit gained from simple linear classifiers, more advanced methods or better features may be needed for better classification performance. [ABSTRACT FROM AUTHOR]

We present results from an analysis of students΄ shallow behaviors, i.e., gaming, during their interaction with an Intelligent Tutoring System (ITS). The analysis is based on six college classes using the Andes ITS for homework and test preparation. Our findings show that student features are a better predictor of gaming than problem features, and that individual differences between students impact where and how students game. [ABSTRACT FROM AUTHOR]

We report the results of a randomized controlled evaluation of the effectiveness of pedagogical agents as providers of affective feedback. These digital learning companions were embedded in an intelligent tutoring system for mathematics, and were used by approximately one hundred students in two public high schools. Students in the control group did not receive the learning companions. Results indicate that low-achieving students–one third of whom have learning disabilities–had higher affective needs than their higher-achieving peers; they initially considered math problem-solving more frustrating, less exciting, and felt more anxious when solving math problems. However, after they interacted with affective pedagogical agents, low-achieving students improved their affective outcomes, e.g., reported reduced frustration and anxiety. [ABSTRACT FROM AUTHOR]

This paper describes the use of wireless sensors to recognize student emotion and the use of pedagogical agents to respond to students with these emotions. Minimally invasive sensor technology has reached such a maturity level that students engaged in classroom work can us sensors while using a computer-based tutor. The sensors, located on each of 25 student΄s chair, mouse, monitor, and wrist, provide data about posture, movement, grip tension, facially expressed mental states and arousal. This data has demonstrated that intelligent tutoring systems can provide adaptive feedback based on an individual student΄s affective state. We also describe the evaluation of emotional embodied animated pedagogical agents and their impact on student motivation and achievement. Empirical studies show that students using the agents increased their math value, self-concept and mastery orientation. [ABSTRACT FROM AUTHOR]

We investigate the utility of an eye tracker for providing information on users΄ affect and reasoning. To do so, we conducted a user study, results from which show that users΄ pupillary responses differ significantly between positive and negative affective states. As far as reasoning is concerned, while our analysis shows that larger pupil size is associated with more constructive reasoning events, it also suggests that to disambiguate between different kinds of reasoning, additional information may be needed. Our results show that pupillary response is a promising non-invasive avenue for increasing user model bandwidth. [ABSTRACT FROM AUTHOR]

In this paper we explore findings from three experiments that use minimally invasive sensors with a web based geometry tutor to create a user model. Minimally invasive sensor technology is mature enough to equip classrooms of up to 25 students with four sensors at the same time while using a computer based intelligent tutoring system. The sensors, which are on each student΄s chair, mouse, monitor, and wrist, provide data about posture, movement, grip tension, arousal, and facially expressed mental states. This data may provide adaptive feedback to an intelligent tutoring system based on an individual student΄s affective states. The experiments show that when sensor data supplements a user model based on tutor logs, the model reflects a larger percentage of the students΄ self-concept than a user model based on the tutor logs alone. The models are further expanded to classify four ranges of emotional self-concept including frustration, interest, confidence, and excitement with over 78% accuracy. The emotional predictions are a first step for intelligent tutor systems to create sensor based personalized feedback for each student in a classroom environment. Bringing sensors to our children΄s schools addresses real problems of students΄ relationship to mathematics as they are learning the subject. [ABSTRACT FROM AUTHOR]

The accuracy of a user model usually depends on the amount and quality of information available on the user's states of interest. An eye-tracker provides data detailing where a user is looking during interaction with the system. In this paper we present a study to explore how this information can improve the performance of a model designed to assess the user's tendency to engage in a meta-cognitive behavior known as self-explanation. [ABSTRACT FROM AUTHOR]

We present a computational framework designed to provide adaptive support aimed at triggering learning from problem-solving activities in the presence of worked-out examples. The key to the framework's ability to provide this support is a user model that exploits a novel classification of similarity to infer the impact of a particular example on a given student's metacognitive behaviors and subsequent learning. [ABSTRACT FROM AUTHOR]