An Item-Driven Adaptive Design for Calibrating Pretest Items

Item pool design for computerized adaptive tests

Although item pools are critical to the proper functioning of computerized adaptive tests (CATs), there is very little in the research literature that indicates the desired features of an item pool. Most research articles use an existing item pool that was developed for other purposes. For example, Pastor, Dodd and Chang (2002) used items from NAEP; Wang and Kolen (2001) used an item pool from ...

An item gains and losses analysis of false memories suggests critical items receive more item-specific processing than list items.

In a repeated testing paradigm, list items receiving item-specific processing are more likely to be recovered across successive tests (item gains), whereas items receiving relational processing are likely to be forgotten progressively less on successive tests. Moreover, analysis of cumulative-recall curves has shown that item-specific processing produces a slower, but steadier rate of recall th...

Multidimensional Adaptive Testing with Optimal Design Criteria for Item Selection

Several criteria from the optimal design literature are examined for use with item selection in multidimensional adaptive testing. In particular, it is examined what criteria are appropriate for adaptive testing in which all abilities are intentional, some should be considered as a nuisance, or the interest is in the testing of a composite of the abilities. Both the theoretical analyses and the...

Auto-calibrating adaptive array for mobile telecommunications

Frequent Item-set Mining without Ubiquitous Items

Frequent Item-set Mining (FIM), sometimes called Market Basket Analysis (MBA) or Association Rule Learning (ARL), are Machine Learning (ML) methods for creating rules from datasets of transactions of items. Most methods identify items likely to appear together in a transaction based on the support (i.e. a minimum number of relative co-occurrence of the items) for that hypothesis. Although this ...