This dissertation examines the effectiveness of machine learning algorithms and feature engineering techniques for analyzing process data and predicting test performance. The study compares three classification approaches and identifies item-specific process features that are highly predictive of student performance. The findings suggest that…

The multiple-choice (MC) item format has been widely used in educational assessments across diverse content domains. MC items purportedly allow for collecting richer diagnostic information. The effectiveness and economy of administering MC items may have further contributed to their popularity not just in educational assessment. The MC item format…

Large-scale assessments are supported by a large item pool. An important task in test development is to assign items into scales that measure different characteristics of individuals, and a popular approach is cluster analysis of items. Classical methods in cluster analysis, such as the hierarchical clustering, K-means method, and latent-class…

This study is a critical evaluation of the roles for coding and scoring of missing responses to multiple-choice items in educational tests. The focus is on tests in which the test-takers have little or no motivation; in such tests omitting and not reaching (as classified by the currently adopted operational rules) is quite frequent. Data from the…

This paper proposes a new Information-Time index as the basis for item selection in computerized classification testing (CCT) and investigates how this new item selection algorithm can help improve test efficiency for item pools with mixed item types. It also investigates how practical constraints such as item exposure rate control, test…

Studied the use of adaptive testing when examinees are classified into three categories. Established testing algorithms with two different statistical computation procedures and evaluated them through simulation using an operative item bank from Dutch basic adult education. Results suggest a reduction of at least 22% in the mean number of items…