Generating Instruction Automatically for the Reading Strategy of Self-Questioning

Abstract

Self-questioning is an important reading comprehension strategy, so it would be useful for an intelligent tutor to help students apply it to any given text. Our goal is to help children generate questions that make them think about the text in ways that improve their comprehension and retention. However, teaching and scaffolding self-questioning involve analyzing both the text and the students' responses. This requirement poses a tricky challenge to generating such instruction automatically, especially for children too young to respond by typing. This paper describes how to generate self-questioning instruction for an automated reading tutor. Following expert pedagogy, we decompose strategy instruction into describing, modeling, scaffolding, and prompting the strategy. We present a working example to illustrate how we generate each of these four phases of instruction for a given text. We identify some relevant criteria and use them to evaluate the generated instruction on a corpus of 513 children's stories.