Toward an Automatic Method for Generating Topical Vocabulary Test Forms for Specific Reading Passages

Abstract

Background knowledge is typically needed for successful comprehension of topical and domain-specific reading passages, such as in the STEM domains. However, there are few automated measures of student knowledge that can be readily deployed and scored in time to make predictions on whether a given student will likely be able to understand a specific content-area text. In this research report, we present our effort in developing the K-tool, an automated system for generating topical vocabulary tests that measure students’ background knowledge related to a specific text. The system automatically detects the topic of a given text and produces topical vocabulary items based on their relationship with the topic. This information is used to automatically generate background knowledge forms that contain words that are highly related to the topic and words that share similar features but do not share high associations to the topic. Prior research has indicated that performance on such tasks can help determine whether a student is likely to understand a particular text based on their knowledge state. The described system is intended for use with middle and high school student populations of native speakers of English. It is designed to handle single reading passages and is not dependent on any corpus or text collection. In this report, we describe the system architecture and present an initial evaluation of the system outputs.

Suggested citation: Flor, M., Wang, Z., Deane, P., & O’Reilly, T. (2025). Toward an automatic method for generating
topical vocabulary test forms for specific reading passages (Research Report No. RR-26-02). ETS.