PlumX Metrics
Embed PlumX Metrics

Two-Stage Uniform Adaptive Testing to Balance Measurement Accuracy and Item Exposure

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 13355 LNCS, Page: 626-632
2022
  • 3
    Citations
  • 0
    Usage
  • 2
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

Conference Paper Description

Computerized adaptive testing (CAT) presents a tradeoff problem involving increasing measurement accuracy vs. decreasing item exposure in an item pool. To address this difficulty, we propose two-stage uniform adaptive testing. In the first stage, the proposed method partitions an item pool into numerous uniform item groups using a state-of-the-art uniform test assembly technique based on the Random Integer Programming Maximum Clique Problem. Then the method selects the optimum item from a uniform item group. In the second stage, when the standard error of an examinee’s ability estimate becomes less than a certain value, it switches to selecting and to presenting an optimum item from the whole item pool. Results of numerical experiments underscore the effectiveness of the proposed method.

Provide Feedback

Have ideas for a new metric? Would you like to see something else here?Let us know