In the REAP system, users are automatically provided with texts to read targeted to their individual reading levels. To find appropriate texts, the user's vocabulary knowledge must be assessed. We describe an approach to automatically generating questions for vocabulary assessment. Traditionally, these assessments have been hand-written. Using data from WordNet, we generate 6 types of vocabulary questions. They can have several forms, including wordbank and multiple-choice. We present experimental results that suggest that these automatically-generated questions give a measure of vocabulary skill that correlates well with subject performance on independently developed humanwritten questions. In addition, strong correlations with standardized vocabulary tests point to the validity of our approach to automatic assessment of word knowledge.
REF used an approach that tests the knowledge of students by automatically generating test items for vocabulary assessment.
Automatic Question Generation For Vocabulary Assessment
Human Language Technology Conference And Empirical Methods In Natural Language Processing
Computer Science