This study fit a Naïve Bayesian classifier to the words of exam items to predict the Bloom's taxonomy level of the items. We addressed five research questions, showing that reasonably good prediction of Bloom's level was possible, but accuracy varies across levels. In our study, performance for Level 2 was poor (Level 2 items were misclassified…

Technology-Enhanced Items (TEIs) have been purported to be more motivating and engaging to test takers than traditional multiple-choice items. The claim of enhanced engagement, however, has thus far received limited research attention. This study examined the rates of rapid-guessing behavior received by three types of items (multiple-choice,…

In recent years, Automatic Item Generation (AIG) has increasingly shifted from theoretical research to operational implementation, a shift raising some unforeseen practical challenges. Specifically, generating high-quality answer choices presents several challenges such as ensuring that answer choices blend in nicely together for all possible item…

This article is a brief account of the use of Bloom's Taxonomy of Educational Objectives (Bloom, Engelhart, Furst, Hill, & Krathwohl, 1956) by staff of the Law School Admission Council in the 1990 development of redesigned specifications for the Reading Comprehension section of the Law School Admission Test. Summary item statistics for the…

This article investigates several questions regarding the impact of different item formats on measurement characteristics. Constructed response (CR) items and multiple choice (MC) items obviously differ in their formats and in the resources needed to score them. As such, they have been the subject of considerable discussion regarding the impact of…