Automatic generation of personalized review materials based on across-learning-system analysis

In this paper, we propose a novel method to make a summary set of lecture slides for supporting students' review study. Quizzes are often conducted in a lecture to check students' understanding level. The aim of our study is to support a student who wrongly answers the quiz. The quiz statement is analyzed to extract nouns in the statement. Then, text mining is performed to find the pages related to the quiz statement in the relevant lecture materials. The proposed SummaryRank algorithm evaluates the topic similarity among pages in material with emphasizing the related page to the quiz statement. In addition, our proposed method considers the preview status of each student, resulting in the generation of adaptive review materials tailored for each student. Through experiments, we confirmed that the proposed method could find appropriate pages with respect to the quiz statements.