I Do Not Understand What I Cannot Define: Automatic Question Generation With Pedagogically-Driven Content Selection
This addresses the scarcity of self-assessment questions in textbooks for learners, though it is incremental in automating question generation with pedagogical grounding.
The paper tackles the problem of generating pedagogically sound questions from textbooks to improve text comprehension, introducing a content selection mechanism that achieved high linguistic quality and relevance in expert evaluations across six domains.
Most learners fail to develop deep text comprehension when reading textbooks passively. Posing questions about what learners have read is a well-established way of fostering their text comprehension. However, many textbooks lack self-assessment questions because authoring them is timeconsuming and expensive. Automatic question generators may alleviate this scarcity by generating sound pedagogical questions. However, generating questions automatically poses linguistic and pedagogical challenges. What should we ask? And, how do we phrase the question automatically? We address those challenges with an automatic question generator grounded in learning theory. The paper introduces a novel pedagogically meaningful content selection mechanism to find question-worthy sentences and answers in arbitrary textbook contents. We conducted an empirical evaluation study with educational experts, annotating 150 generated questions in six different domains. Results indicate a high linguistic quality of the generated questions. Furthermore, the evaluation results imply that the majority of the generated questions inquire central information related to the given text and may foster text comprehension in specific learning scenarios.