Towards an automatic method for generating topical vocabulary test forms for specific reading passages
Michael Flor, Zuowei Wang, Paul Deane, Tenaha O'Reilly

TL;DR
This paper introduces K-tool, an automated system that generates topical vocabulary tests to assess students' background knowledge for specific texts, aiding predictions of comprehension ability.
Contribution
The paper presents a novel automated system for creating topical vocabulary tests tailored to individual texts without relying on external corpora.
Findings
System successfully detects text topics and generates relevant vocabulary items.
Initial evaluation shows promising accuracy in vocabulary selection.
Potential to improve reading comprehension assessments for students.
Abstract
Background knowledge is typically needed for successful comprehension of topical and domain specific reading passages, such as in the STEM domain. 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 paper, we present our effort in developing 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…
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Taxonomy
TopicsNatural Language Processing Techniques · Mathematics, Computing, and Information Processing
