Towards automatic glossarization: automatically constructing and administering vocabulary assistance factoids and multiple-choice assessment

We address an important problem with a novel approach: helping children learn words during computer-assisted oral reading. We build on Project LISTEN's Reading Tutor, which is a computer program that adapts automatic speech recognition to listen to children read aloud, and helps them learn to read (http://www.cs.cmu.edu/~listen). In this paper, we focus on the problem of vocabulary acquisition. To learn a word from reading with the Reading Tutor, students must first encounter the word and then learn the meaning of the word from context. This paper describes how we modified the Reading Tutor to help students learn the meanings of new words by augmenting stories with WordNet-derived comparisons to other words – “factoids”. Furthermore, we report results from an embedded experiment designed to evaluate the effectiveness of including factoids in stories that children read with the Reading Tutor. Factoids helped – not for all students and all words, but for third graders seeing rare words, and for single-sense rare words tested one or two days later. We also discuss further steps towards automatic construction of explanations of words.