Element matching in concept maps
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Moving digital libraries into the student learning space: The GetSmart experience
Journal on Educational Resources in Computing (JERIC)
Matching knowledge elements in concept maps using a similarity flooding algorithm
Decision Support Systems
Self-associated concept mapping for representation, elicitation and inference of knowledge
Knowledge-Based Systems
Use of Agent Prompts to Support Reflective Interaction in a Learning-by-Teaching Environment
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Reflection Prompts to Facilitate Students' Thinking about Teaching in Teachable Agent Environment
Proceedings of the 2007 conference on Supporting Learning Flow through Integrative Technologies
ICLS'08 Proceedings of the 8th international conference on International conference for the learning sciences - Volume 2
Fuzzy cognitive map based student progress indicators
ICWL'11 Proceedings of the 10th international conference on Advances in Web-Based Learning
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A concept map, typically depicted as a connected graph, is composed of a collection of propositions. Each proposition forming a semantic unit consists of a small set of concept nodes interconnected to one another with relation links. Concept maps possess a number of appealing features which make them a promising tool for teaching, learning, evaluation, and curriculum planning. We extend concept maps by associating their concept nodes and relation links with attribute values which indicate the relative significance of concepts and relationships in knowledge representation. The resulting maps are called attributed concept maps (ACM). Assessing students will be conducted by matching their ACMs with those prebuilt by experts. The associated techniques are referred to as map matching techniques. The building of an expert ACM has in the past been done by only one specialist. We integrate a number of maps developed by separate experts into a single map, called the master map (MM), which will serve as a prototypical map in map matching. Both map integration and map matching are conceptualized in terms of fuzzy set discipline. Experimental results have shown that the proposed ideas of ACM, MM, fuzzy map integration, and fuzzy map matching are well suited for students with high performances and difficult subject materials