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Pan and Wollack (PW) proposed a machine learning method to detect compromised items. We extend the work of PW to an approach detecting compromised items and examinees with item preknowledge simultaneously and draw on ideas in ensemble learning to relax several limitations in the work of PW. The suggested approach also provides a confidence score, which is based on an autoencoder to represent our confidence that the detection result truly corresponds to item preknowledge. Simulation studies indicate that the proposed approach performs well in the detection of item preknowledge, and the confidence score can provide helpful information for users.

In computerized adaptive testing, overexposure of items in the bank is a serious problem and might result in item compromise. We develop an item selection algorithm that utilizes the entire bank well and reduces the overexposure of items. The algorithm is based on collaborative filtering and selects an item in two stages. In the first stage, a set of candidate items whose expected performance matches the examinee's current performance is selected. In the second stage, an item that is approximately matched to the examinee's observed performance is selected from the candidate set. The expected performance of an examinee on an item is predicted by autoencoders. Experiment results show that the proposed algorithm outperforms existing item selection algorithms in terms of item exposure while incurring only a small loss in measurement precision.

As technologies have been improved, item preknowledge has become a common concern in the test security area. The present study proposes an unsupervised-learning-based approach to detect compromised items. The unsupervised-learning-based compromised item detection approach contains three steps: (1) classify responses of each examinee as either normal or aberrant based on both the item response and the response time; (2) use a recursive algorithm to cluster examinees into groups based on their response similarity; (3) identify the group with strongest preknowledge signal and report questionable items as compromised. Results show that under the conditions studied, provided the amount of preknowledge is not overwhelming and aberrance effect is at least moderate, the approach controls the false-negative rate at a relatively low level and the false-positive rate at an extremely low level.

Discrete-option multiple-choice (DOMC) items differ from traditional multiple-choice (MC) items in the sequential administration of response options (up to display of the correct option). DOMC can be appealing in computer-based test administrations due to its protection of item security and its potential to reduce testwiseness effects. A psychometric model for DOMC items that attends to the random positioning of key location across different administrations of the same item is proposed, a feature that has been shown to affect DOMC item difficulty. Using two empirical data sets having items administered in both DOMC and MC formats, the variability in key location effects across both items and persons is considered. The proposed model exploits the capacity of the DOMC format to isolate both (a) distinct sources of item difficulty (i.e., related to the identification of keyed responses versus the ruling out of distractor options) and (b) distinct person proficiencies related to the same two components. Practical implications in terms of the randomized process applied to schedule item key location in DOMC test administrations are considered. [ABSTRACT FROM AUTHOR]