AutoTutor

2012 ◽  
pp. 169-187 ◽  
Author(s):  
Arthur C. Graesser ◽  
Sidney D’Mello ◽  
Xiangen Hu ◽  
Zhiqiang Cai ◽  
Andrew Olney ◽  
...  
Keyword(s):  
Learning Environments ◽  
Speech Act ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Conversational Agent ◽  
Learning Gains ◽  
Tutoring System ◽  
Agent Based ◽  
Electronic Documents ◽  
Dialogue Management

AutoTutor is an intelligent tutoring system that helps students learn science, technology, and other technical subject matters by holding conversations with the student in natural language. AutoTutor’s dialogues are organized around difficult questions and problems that require reasoning and explanations in the answers. The major components of AutoTutor include an animated conversational agent, dialogue management, speech act classification, a curriculum script, semantic evaluation of student contributions, and electronic documents (e.g., textbook and glossary). This chapter describes the computational components of AutoTutor, the similarity of these components to human tutors, and some challenges in handling smooth dialogue. We describe some ways that AutoTutor has been evaluated with respect to learning gains, conversation quality, and learner impressions. AutoTutor is sufficiently modular that the content and dialogue mechanisms can be modified with authoring tools. AutoTutor has spawned a number of other agent-based learning environments, such as AutoTutor-lite, Operation Aries!, and Guru.

Text-Based Affect Detection in Intelligent Tutors

2012 ◽  
pp. 284-304
Author(s):  
Sidney D’Mello ◽  
Arthur C. Graesser
Keyword(s):  
Learning Environments ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Affective States ◽  
Learning Gains ◽  
Tutoring Systems ◽  
Detection Systems ◽  
Intelligent Tutors ◽  
Cognitive States ◽  
Affect Detection

Affect-sensitive Intelligent Tutoring Systems are an exciting new educational technology that aspire to heighten motivation and enhance learning gains in interventions that are dynamically adaptive to learners’ affective and cognitive states. Although state of the art affect detection systems rely on behavioral and physiological measures for affect detection, we show that a textual analysis of the tutorial discourse provides important cues into learners’ affective states. This chapter surveys the existing literature on text-based affect sensing and focuses on how learners’ affective states (boredom, flow/engagement, confusion, and frustration) can be automatically predicted by variations in the cohesiveness of tutorial dialogues during interactions with AutoTutor, an intelligent tutoring system with conversational dialogues. The authors discuss the generalizability of findings to other domains and tutoring systems, the possibility of constructing real-time cohesion-based affect detectors, and implications for text-based affect detection for the next generation affect-sensitive learning environments.

scholarly journals Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Hsuan-Ta Lin ◽  
Po-Ming Lee ◽  
Tzu-Chien Hsiao
Keyword(s):  
Online Learning ◽  
Reinforcement Learning ◽  
Real World ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Rule Discovery ◽  
Learning Gains ◽  
Tutoring System

Tutorial tactics are policies for an Intelligent Tutoring System (ITS) to decide the next action when there are multiple actions available. Recent research has demonstrated that when the learning contents were controlled so as to be the same, different tutorial tactics would make difference in students’ learning gains. However, the Reinforcement Learning (RL) techniques that were used in previous studies to induce tutorial tactics are insufficient when encountering large problems and hence were used in offline manners. Therefore, we introduced a Genetic-Based Reinforcement Learning (GBML) approach to induce tutorial tactics in an online-learning manner without basing on any preexisting dataset. The introduced method can learn a set of rules from the environment in a manner similar to RL. It includes a genetic-based optimizer for rule discovery task by generating new rules from the old ones. This increases the scalability of a RL learner for larger problems. The results support our hypothesis about the capability of the GBML method to induce tutorial tactics. This suggests that the GBML method should be favorable in developing real-world ITS applications in the domain of tutorial tactics induction.

Sign in / Sign up

Export Citation Format

Share Document