Conversational Intelligent Tutoring Systems (ITSs) are expensive to develop. While simple online courseware could be easily authored by teachers, the authoring of conversational ITSs usually involves a team of experts with different expertise, including domain experts, linguists, instruction designers, programmers, artists, computer scientists,…

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…

In this paper, we consider a minimalistic and behavioristic view of AIS to enable a standardizable mapping of both the behavior of the system and of the learner. In this model, the "learners" interact with the learning "resources" in a given learning "environment" following preset steps of learning…

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…

This work is a step towards full automation of auto-mentoring processes in multi-player online environments such as virtual internships. We focus on automatically identifying speaker's intentions, i.e. the speech acts of chat utterances, in such virtual internships. Particularly, we explore several machine learning methods to categorize speech…

Engineering virtual internships are simulations where students role play as interns at fictional companies, working to create engineering designs. To improve the scalability of these virtual internships, a reliable automated assessment system for tasks submitted by students is necessary. Therefore, we propose a machine learning approach to…

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…

During the last decade, interdisciplinary researchers have developed technologies with animated pedagogical agents that interact with the student in language and other communication channels (such as facial expressions and gestures). These pedagogical agents model good learning strategies and coach the students in actively constructing knowledge…

Evaluated the PREG conceptual model of human question asking. Found the model was sufficient as it accounted for nearly all of the questions produced by students, and was discriminating in that it could identify the conditions in which particular classes of questions are or are not generated. (Author/SD)