Distribution of lecture concepts and relations in digital contents

Po Jen Chuang, Chu Sing Yang, Ming Chao Chiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Digital contents contains a large number of learning concepts most of which contribute to the main learning ideas. How to focus on the learning faults and improve the learning process is important. In this paper, we propose a novel approach to retrieving the main ideas from, as well as to constructing a domain tree to represent, the contents of materials. The nodes of the domain tree consist of meaningful texts. We collect the meaningful texts by segmenting words of the digital contents and then recombining these texts to form a binary number. We define a scoring method for the digital contents by assigning a sequence of 0's and 1 's to the texts. These binary numbers can then be easily calculated by a function of sequence with power n and base 2, where n ∈ N. Each sequence can get a unit score which indicates the location in the context. An expression of digital contents represents a unit, a chapter, a section, or a paragraph. This expression can be provided as a feedback to teachers or students. Based on the feedback, teachers can make questions in the exam sheet more evenly distributed while students can improve the way they learn.

Original languageEnglish
Title of host publicationNetwork-Based Information Systems - First International Conference, NBiS 2007, Proceedings
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783540745723
Publication statusPublished - 2007
Event1st International Conference on Network-Based Information Systems, NBiS 2007 - Regensburg, Germany
Duration: 2007 Sept 32007 Sept 7

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4658 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other1st International Conference on Network-Based Information Systems, NBiS 2007

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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