Methods for extracting the important portions from the contexts in research document creation involving explorations
Issue title: Selected papers from the ISCA International Conference on Software Engineering and Data Engineering, and the ISCA International Conference on Computer Applications in Industry and Engineering, 2015, and Invited Papers
Guest editors: Takaaki Goto and Narayan C. Debnath
Affiliations: [a] Department of Computer Science and Mathematics, Fukushima University, Fukushima, Japan | [b] Division of Natural Science, Tokyo Gakugei University, Tokyo, Japan
Correspondence:
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Corresponding author: Ryo Onuma, Department of Computer Science and Mathematics, Fukushima University, Kanayagawa, Fukushima-shi, Fukushima 960-1296, Japan. E-mail:0oonuma@gmail.com
Abstract: Research activities are composed of stacks of several
tasks such as surveys, creation of novel ideas, writing of research
documents, and their elaboration. It is quite important to successfully
accumulate the results and circumstances of these tasks (i.e., contexts). It
is further important but difficult to understand the contexts and to apply
them to later work. In particular, it is much more difficult for research
beginners to ensure noteworthy targets within contexts that continuously
increase as the work progresses. The final goal of this research is to
develop methods for supporting the accumulation of contexts and facilitating
their utilization by visually presenting those that are noteworthy to
research beginners. This paper mainly describes a method for organizing the
contexts and extracting important portions from huge organized contexts.
This paper also describes an experiment that was conducted by using actual
history data, and discusses the features of the proposed methods on the
basis of the results.
Keywords: Context information, context network, small world, research documenting, research activity support, learning analytics
