Alert:

Limited Availability of Full-Text Documents. Click here for more information, or here to request the return of a PDF online.

EJ801326 - Agent Technologies Designed to Facilitate Interactive Knowledge Construction

Help

Tutorial

Help |

Tutorial

Help |

Tutorial

Help With This Page

Back to Search Results Help Permalink Share this record

More Info:
Help 0 items in My Clipboard | Add record to My Clipboard

Record Details

Full-Text Availability Options:

More Info:
Help | Tutorial Help Finding Full Text | More Info:
Help Find in a Library | Publisher's website

Related Items: Show Related Items

Click on any of the links below to perform a new search
ERIC #:	EJ801326
Title:	Agent Technologies Designed to Facilitate Interactive Knowledge Construction
Authors:	Graesser, Arthur C.; Jeon, Moongee; Dufty, David
Descriptors:	Intelligent Tutoring Systems; Dialogs (Language); Interactive Video; Animation; Achievement Gains; Learning; Artificial Intelligence
Source:	Discourse Processes: A Multidisciplinary Journal, v45 n4-5 p298-322 Jul 2008
More Info: Help Peer Reviewed:	Yes
Publisher:	Routledge. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals/default.html
Publication Date:	2008-07-00
Pages:	25
Pub Types:	Journal Articles; Reports - Descriptive
Abstract:	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 during learning. This article describes computer technologies that have been developed during the last decade with tutors that attempt to facilitate deep comprehension (e.g., causal explanations, plans, logical justifications), reasoning in natural language, and inquiry (i.e., question asking, question answering, hypothesis testing). These tutors target high school and college students who learn about topics in science and technology. The primary example is AutoTutor, a system on the Internet that helps students compose answers to deep-reasoning questions and solutions to problems by holding a conversation. AutoTutor's dialogue moves include "feedback" (positive, neutral, and negative), "pumps" for more information ("Tell me more."), "hints", "prompts" to fill in missing words, "summaries", "corrections" of student misconceptions, and "answers" to student questions. Other learning technologies with agents include the Human Use Regulatory Affairs Advisor (HURAA); Source, Evidence, Explanation, and Knowledge (SEEK) Web Tutor; Interactive Strategy Trainer for Active Reading and Thinking (iSTART); Instruction with Deep-level Reasoning questions In Vicarious Environments (iDRIVE); and Acquiring Research Investigative and Evaluative Skills (ARIES). These systems have been tested on their effectiveness in facilitating knowledge construction. They also have uncovered insights on the prospects of designing agents to effectively communicate in language and discourse. (Contains 1 table and 1 figure.)
Abstractor:	As Provided
Reference Count:	77

Note:	N/A
Identifiers:	N/A
Record Type:	Journal
Level:	N/A
Institutions:	N/A
Sponsors:	N/A
ISBN:	N/A
ISSN:	ISSN-0163-853X
Audiences:	N/A
Languages:	English
Education Level:	Adult Education; Higher Education; Secondary Education
Direct Link:	http://www.informaworld.com/openurl?genre=article&id=doi:10.1080/01638530802145395

Back to Search Results