Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A greedy genetic algorithm for the quadratic assignment problem
Computers and Operations Research
Mining learner profile utilizing association rule for web-based learning diagnosis
Expert Systems with Applications: An International Journal
Educational data mining: A survey from 1995 to 2005
Expert Systems with Applications: An International Journal
Discovery of maximum length frequent itemsets
Information Sciences: an International Journal
| Hi-index | 12.05 |
One of the essential goals of test designing is to select items with the most discriminative power. In the past, most research has assumed there is no dependent relationship among test items, so that test papers are often produced by selecting items with individual discriminations. However, in actuality, test items may relate to other items and the overall discrimination of a test paper cannot be simply aggregated. Therefore, this study proposes a two-step framework to design test papers by choosing discriminative item combinations from the item bank. The proposed approach (the process) first analyzing entails the archival tests to discover substitute items, as well as recognize discriminative test itemsets by using data mining technology. Then, test items can be recommended to compose a discriminative test paper. Finally, a real life case is used to test the proposed method. The test data is provided by the Chinese Enterprise Planning Association (CERP) in Taiwan. The experimental results indicate that: (1) the two-step method can complete the test design task efficiently; (2) the newly composed test paper presents highly discriminative; and (3) the discrimination power of our test paper is very close to the theoretic maximum value based on Item Response Theory.