This paper describes a new automated disengagement tracking system (DTS) that detects learners' maladaptive behaviors, e.g. mind-wandering and impetuous responding, in an intelligent tutoring system (ITS), called AutoTutor. AutoTutor is a conversation-based intelligent tutoring system designed to help adult literacy learners improve their reading…

Background: Pedagogical agents are computerized talking heads or embodied animated avatars that help students learn by performing actions and holding conversations with the students in natural language. Dialogues occur between a tutor agent and the student in the case of AutoTutor and other intelligent tutoring systems with natural language…

AutoTutor helps students learn by holding a conversation in natural language. AutoTutor is adaptive to the learners' actions, verbal contributions, and in some systems their emotions. Many of AutoTutor's conversation patterns simulate human tutoring, but other patterns implement ideal pedagogies that open the door to computer tutors eclipsing…

The current studies investigated the efficient use of dialogue in intelligent tutoring systems that use natural language interaction. Such dialogues can be relatively time-consuming. This work addresses the question of how much dialogue is needed to produce significant learning gains. In Experiment 1, a full dialogue condition and a read-only…

Relations between emotions (affect states) and learning have recently been explored in the context of AutoTutor. AutoTutor is a tutoring system on the Internet that helps learners construct answers to difficult questions by interacting with them in natural language. AutoTutor has an animated conversation agent and a dialog management facility that…

It is often assumed that engaging in a one-on-one dialogue with a tutor is more effective than listening to a lecture or reading a text. Although earlier experiments have not always supported this hypothesis, this may be due in part to allowing the tutors to cover different content than the noninteractive instruction. In 7 experiments, we tested…

Describes the seven main components of QUEST, a cognitive model of question answering that attempts to simulate the answers adults produce when they answer different types of questions, both closed class and open class. Illustrates how the model could be applied to different types of knowledge structures, including causal networks, goal…

Tests the QUEST model of question answering in naturalistic settings and in settings with complex pragmatic constraints: telephone surveys, business interactions, filmed interviews, and interviews on popular television programs. Finds that QUEST explains most of the answers in these contexts and virtually all of the answers that refer to the…

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…

The Human Use Regulatory Affairs Advisor (HURAA) is an Internet facility that provides help and training on the ethical use of human subjects in research, based on documents and regulations in United States Federal agencies. HURAA has a number of standard features of conventional web facilities and computer-based training, such as hypertext,…

Whereas existing learning environments on the Web lack high level interactivity, we have developed a human tutor-like tutorial conversation system for the Web that enhances educational courseware through mixed-initiative dialog with natural language processing. The conversational tutoring agent is composed of an animated tutor, a Latent Semantic…