autotutor

AutoTutor is an automated computer tutor that simulates human tutors and holds conversations with students in natural language. Using data collected from AutoTutor, the following determinations were sought: Can we automatically classify affect states from intelligent teaching systems to aid in the detection of a learner's emotional state? Using frequency patterns of AutoTutor feedback and assigned user emotion in a series of pairs, can the next pair of feedback/emotion series be predicted? Through a priori data mining approaches, we found dominant frequent item sets that predict the next set of responses...

The BRCA Gist Intelligent Tutoring System helps women understand and make decisions about genetic testing for breast cancer risk. BRCA Gist is guided by Fuzzy-Trace Theory, (FTT) and built using AutoTutor Lite. It responds differently to participants depending on what they say. Seven tutorial dialogues requiring explanation and argumentation are guided by three FTT concepts: forming gist explanations in one's own words, emphasizing decision-relevant information, and deliberating the consequences of decision alternatives...

The goal of intelligent tutoring systems (ITS) that interact in natural language is to emulate the benefits that a well-trained human tutor provides to students, by interpreting student answers and appropriately responding in order to encourage elaboration. BRCA Gist is an ITS developed using AutoTutor Lite, a Web-based version of AutoTutor. Fuzzy-trace theory theoretically motivated the development of BRCA Gist, which engages people in tutorial dialogues to teach them about genetic breast cancer risk. We describe an empirical method to create tutorial dialogues and fine-tune the calibration of BRCA Gist's semantic processing engine without a team of computer scientists...

This is an unusual moment in the history of psychology because of landmark advances in digital information technologies, computational linguistics, and other fields that use the computer to analyze language, discourse, and behavior. The technologies developed from this interdisciplinary fusion are helping students learn and think in ways that are sensitive to their cognitive and emotional states. Recent projects have developed computer technologies that help us understand the nature of conversational discourse and text comprehension in addition to improving learning...

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 the interaction hypothesis under the constraint that (a) all students covered the same content during instruction, (b) the task domain was qualitative physics, (c) the instruction was in natural language as opposed to mathematical or other formal languages, and (d) the instruction conformed with a widely observed pattern in human tutoring: Graesser, Person, and Magliano's 5-step frame...

AutoTutor is a learning environment that tutors students by holding a conversation in natural language. AutoTutor has been developed for Newtonian qualitative physics and computer literacy. Its design was inspired by explanation-based constructivist theories of learning, intelligent tutoring systems that adaptively respond to student knowledge, and empirical research on dialogue patterns in tutorial discourse. AutoTutor presents challenging problems (formulated as questions) from a curriculum script and then engages in mixed initiative dialogue that guides the student in building an answer...