Learning is an iterative process which consists of knowledge acquisition, assessment of supposed knowledge, identification of misconceptions, and refinement of understanding. Within courses, teachers often employ textbook review questions as a method of assessing student knowledge retention. However, student attempts to resolve misconceptions or reinforce concepts following these questions may leave them searching large expanses of textbook content. This project aims to alleviate this problem by providing formative feedback in the form of textbook passages.

By utilizing question answering and reading comprehension methodologies, a search algorithm for selecting relevant material within textbooks is developed. The algorithm capitalizes on the inherent structure of textbooks which, intuitively, reduces the search space. Evaluation of question answering and information retrieval practices in the education domain is performed beyond simple factoid questions. Results are compared to data collected via subject matter experts, and limitations of these models, as they pertain to textbooks, are explored.