Applying the genetic encoded conceptual graph to grouping learning

Authors:
Teyi Chan;Chien-Ming Chen;Yu-Lung Wu;Bin-Shyan Jong;Yen-Teh Hsia;Tsong-Wuu Lin
Affiliations:
Science and Technology Policy Research and Information Center, Trend Analysis Division 14F, No. 106, Heping E. Rd., Sec. 2, Taipei 10636, Taiwan;Department of Electronic Engineering, Chung Yuan Christian University, Taiwan;Department of Information and Communication, Kun Shan University, Taiwan;Department of Information and Computer Engineering, Chung Yuan Christian University, Taiwan;Department of Information and Computer Engineering, Chung Yuan Christian University, Taiwan;Department of Computer Science and Information Management, SooChow University, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 11
Cited 2

Adaptation in natural and artificial systems

Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
E-Learning: Strategies for Delivering Knowledge in the Digital Age

E-Learning: Strategies for Delivering Knowledge in the Digital Age
Interactive Distance Learning Over Intranets

IEEE Internet Computing
A conceptual map model for developing intelligent tutoring systems

Computers & Education
Dynamic Grouping Strategies Based on a Conceptual Graph for Cooperative Learning

IEEE Transactions on Knowledge and Data Engineering
DIANA: A computer-supported heterogeneous grouping system for teachers to conduct successful small learning groups

Computers in Human Behavior
Conceptual graphs for a data base interface

IBM Journal of Research and Development
A controlled genetic algorithm by fuzzy logic and belief functionsfor job-shop scheduling

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Engineers turn to e-learning

IEEE Spectrum
An educational genetic algorithms learning tool

IEEE Transactions on Education

Creating effective student groups: an introduction to groupformation.org

Proceeding of the 44th ACM technical symposium on Computer science education
Improving e-learning communities through optimal composition of multidisciplinary learning groups

Computers in Human Behavior

Quantified Score

Hi-index	12.05

Visualization

Abstract

The continual development of information technology in recent years has ensured its increasingly widespread use in many domains. Recent developments pertaining to the Internet and in computer technology have resulted in e-learning-a pedagogy that is free from time and space constraints. The issues of helping learners to study efficiently through lecturing procedures and using learning systems are now becoming increasingly important. Learning strategy based on a combination of the concept graphs learning system and cooperative learning is an important trend in computer and network aided instructions. The first step in cooperative learning activities is to divide learners into groups. This study proposes a grouping strategy to divide learning activities into several phases. The concept graph diagnostic system is adopted to assess learners' learned concept nodes after each learning phase. The results of the assessment are encoded to learning genes and used to calculate the group complementary score. The genetic algorithm is used to group the learners into learning groups according to the group complementary score.