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
- 3Citations
- 2Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85135894512&origin=inward; http://dx.doi.org/10.1007/978-3-031-11644-5_59; https://link.springer.com/10.1007/978-3-031-11644-5_59; https://dx.doi.org/10.1007/978-3-031-11644-5_59; https://link.springer.com/chapter/10.1007/978-3-031-11644-5_59
Springer Science and Business Media LLC
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