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

Atsushi Shimada, Fumiya Okubo, Chengjiu Yin, Hiroaki Ogata

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)22-27
Number of pages6
JournalCEUR Workshop Proceedings
Publication statusPublished - 2016
Event1st International Workshop on Learning Analytics Across Physical and Digital Spaces, CrossLAK 2016 - Edinburgh, Scotland, United Kingdom
Duration: Apr 25 2016Apr 29 2016

All Science Journal Classification (ASJC) codes

  • Computer Science(all)


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