The spread of information communication technologies (ICT) enables people to have opportunities of ICT-driven education, where a lot of educational logs are collected from e-learning systems, e-portfolio systems, e-book systems and so on. There are two difficulties in utilizing educational big data. One is how to tackle the issue of asynchronous activities of students and teachers. The other is how to realize real-time feedback to students and teachers. To tackle these issues, in this study, the project will develop a new stream processing platform in which data integration, modeling, change detection, prediction and case-based database are implemented for analyzing various sizes of spatio-temporal big data.
We propose a novel method of summarizing lecture slides to enhance preview efficiency and improve students’ understanding of the content. Students are often asked to prepare for a class by reading lecture materials. However, this does not always produce good results because the attention span of students is limited. We conducted a survey involving preview of lecture materials by more than 300 students and found that they want summarized materials to preview. Therefore, we developed an automatic summarization method to reduce the original preview materials to a summarized set. Our approach is based on the use of image processing and text processing to extract important pages from lecture materials, and then optimizing the selection of pages in accordance with a specified preview time. We applied the proposed summarization method to lecture slides. In our user study involving more than 300 students, we compared the relative effectiveness of the summarized slides and the original materials in terms of quiz scores, preview achievement ratio, and time spent previewing. We found that students who previewed the summarized slides achieved better scores on pre-lecture quizzes even though they spent less time previewing the material.
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 answer 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.
We tackle browsing-pattern mining from e-Book logs based on non-negative matrix factorization (NMF). We applied NMF to an observation matrix with 21-page browsing logs of 110 students, and discovered five kinds of browsing patterns.