A Multi-Agent Question-Answering System for E-Learning and Collaborative Learning Environment

A Multi-Agent Question-Answering System for E-Learning and Collaborative Learning Environment

Tannaz Alinaghi, Ardeshir Bahreininejad
Copyright: © 2011 |Pages: 17
DOI: 10.4018/jdet.2011040103
OnDemand:
(Individual Articles)
Available
$37.50
Current Special Offers
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

The increasing advances of new Internet technologies in all application domains have changed life styles and interactions. E-learning and collaborative learning environment systems are originated through such changes and aim at providing facilities for people in different times and geographical locations to cooperate, collaborate, learn and work together by using various educational services. One of the most important requirements of learners in online and virtual environments is the ability to ask questions and receive appropriate answers. The nature of such environments and the lack of physical existence of teachers make such issues critical and challenging problems. This paper presents a multi-agent system for building a question-answering system in learning management systems and collaborative learning environments. In the proposed system, after validating the content of questions, all available resources including course materials, frequently asked questions and responses from other learners will be gathered and finally using a recommender system, the most appropriate answer(s) with respect to several criteria such as learner’s knowledge, research background, history of previous questions, and the candidate answers relevant to the question will be suggested. A simplified version of the system has been implemented and integrated to a well known open source collaborative learning environment system in order to simulate and evaluate the applicability and appropriateness of the proposed system. The result shows that the proposed question-answering system may be used efficiently and expanded to accommodate further advanced capabilities.
Article Preview
Top

Background

With the rapid developments in e-learning domain, numerous technologies and tools have been used to facilitate communication, coordination, collaboration, cooperation, and production activities.

An agent-based QAS is presented in (Ishikawa, Wongvibulsin, & Yu, 2003) which, assists collaborative learning mechanisms. When a learner sends a question, an agent searches a FAQ document and also forwards the question to a selected learner(s). The agent in the system utilizes text mining techniques (word extraction, word weighting, word counting, and vector construction) to autonomously select an answer from FAQ or obtain a response from learners corresponding to the question. The system assists not only in offering answer(s) to the learner, but also provides the opportunities of collaboration and learning to learners by answering the questions of other learners.

Complete Article List

Search this Journal:
Reset
Volume 22: 1 Issue (2024)
Volume 21: 2 Issues (2023)
Volume 20: 4 Issues (2022): 1 Released, 3 Forthcoming
Volume 19: 4 Issues (2021)
Volume 18: 4 Issues (2020)
Volume 17: 4 Issues (2019)
Volume 16: 4 Issues (2018)
Volume 15: 4 Issues (2017)
Volume 14: 4 Issues (2016)
Volume 13: 4 Issues (2015)
Volume 12: 4 Issues (2014)
Volume 11: 4 Issues (2013)
Volume 10: 4 Issues (2012)
Volume 9: 4 Issues (2011)
Volume 8: 4 Issues (2010)
Volume 7: 4 Issues (2009)
Volume 6: 4 Issues (2008)
Volume 5: 4 Issues (2007)
Volume 4: 4 Issues (2006)
Volume 3: 4 Issues (2005)
Volume 2: 4 Issues (2004)
Volume 1: 4 Issues (2003)
View Complete Journal Contents Listing