Paper Info
Title
Computer-Aided Generation of Item Banks Based on Ontology and Bloom's Taxonomy
Abstract
Online learning and testing are important topics in information education. Students can take online tests to assess their achievement of learning goals. However, the test results should assign student scores and assess their achievement of knowledge and cognition levels. Teachers currently need to spend considerable time on producing and maintaining on-line testing items. This study applied ontology, Chinese semantic database, artificial intelligence and Bloom's taxonomy to propose a CAGIS E-learning system architecture to assist teachers in creating test items. As the result, the computer assisted teachers in producing a large number of test items quickly. These test items covered three types of knowledge and five dimensions of cognitive skills. The test items could meaningfully assess learning level meaningfully.
Year
DOI
Venue
2008
10.1007/978-3-540-85033-5_16
ICWL
Keywords
Field
DocType
item banks,computer-aided generation,artificial intelligence,test result,online test,test item,cognitive skill,chinese semantic database,online learning,cognition level,cagis e-learning system architecture,on-line testing item,artificial intelligent,cognitive skills,ontology,semantic web
Data science,Ontology,Descriptive knowledge,Computer science,Semantic Web,Knowledge management,Bloom's taxonomy,Cognitive skill,Applied ontology,Systems architecture,Cognition,Multimedia
Conference
Volume
ISSN
Citations 
5145
0302-9743
3
PageRank 
References 
Authors
0.54
4
2
Name
Order
Citations
PageRank
Ming-Hsiung Ying182.69
hengli yang234427.53