Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.eswa.2011.08.026 | Expert Syst. Appl. |
Keywords | Field | DocType |
discriminative test itemsets,discriminative test paper,discriminative power,test item,test paper,test data,data mining technology,discriminative item combination,archival test,achievement test,test design task,data mining,association rule | Data mining,Test score,Computer science,Achievement test,Computerized classification test,Test design,Artificial intelligence,Test data,Item bank,Discriminative model,Item response theory,Machine learning | Journal |
Volume | Issue | ISSN |
39 | 1 | 0957-4174 |
Citations | PageRank | References |
1 | 0.36 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu-Chin Liu | 1 | 12 | 3.96 |
| Po-Jung Chen | 2 | 1 | 0.36 |