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. © 2008 Springer-Verlag Berlin Heidelberg.