Intelligent Database Systems Lab N.Y.U.S.T. I. M. 1 Mining concept maps from news stories for measuring civic scientific literacy in media Presenter : Wu, Jia-Hao Authors : Yuen-Hsien Tseng, Chun-Yen Chang Shu-Nu Chang Rundgren, Carl-Johan Rundgren CE (2010) 國立雲林科技大學 National Yunlin University of Science and Technology

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 22 Outline Motivation Objective Methodology Experiments Conclusion Personal Comments

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 3 Motivation Most people acquire their knowledge about science from school textbooks. Later in life, media becomes the major source of an individual’s knowledge about science. Many text mining techniques and knowledge representation framework developed in recent decades, this task can be done in such a way that mining concepts and their relations in media. Java Programming

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 4 Objective The authors want to develop an instrument aimed for measuring Taiwanese civic scientific literacy in media. Use the general approach to concept map mining from texts, which involves two important steps : key terms extraction and term association analysis.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 5 Methodology Deals with the extraction of key terms from each of documents. Deals with the association for each key term pair.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 6 Methodology-Key term extraction

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 7 Methodology-Key term extraction ex. 100 Taiwan news articles A longest-match strategy based on a lexicon of 123,266 terms 33 key terms (terms that occur at least twice) 11 terms were not covered by the lexicon of 123,266 terms 954 new terms contains 79 illegal words (error rate of 8.3%) 2197 extracted key terms, the error rate is only 3.6%

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 8 Methodology-Term association analysis

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 9 Experiments – Chinese news article.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 10 Experiments – SLiM Scientific literacy in media ( SLiM)  About nature, life, and technology.  901,446 documents starting from 2000/01/01 to 2001/12/31  1,082,937 key terms extracted.  Filtering those without associated terms, 323,918 key terms remained.  The key terms were matched against with the 3,657 textbook terms, resulting in a list of 876 terms.  After the experts to examined, 39 key terms were selected. Together with their related terms, a set of 95 terms results from this selection process.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 11 Experiments – Concept map

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 12 Experiments – an item was developed

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 13 Experiments – Biology

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 14 Experiments – Earth Science

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 15 Experiments – Physics & Chemistry

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 16 Experiments – Summary 50 items were generated from the 95 terms and their relations. Test items were sent out for validation by eight experts in different areas of science.  A total of 1034 participants answered the SLiM questionnaires, among them 954 people were valid samples.  The average difficulty of the whole 50 items of SLiM ranges from 0.19 to 0.91 (the reliability of SLiM based on the valid samples ranges from 0.60 to 0.85)  The discrimination powers are 0.1 – 0.59 Biology 22, earth science 19, physics 6, chemistry 3 Biologyearth sciencephysicschemistry 45.26%37.90%11.58%5.26%

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 17 Conclusion This paper use the method to visualize the science textbook terms and their relations. The ability to mine concept maps from text documents or learning material has several advantages in the concept map applications. The concept map turned out to be a convenient tool for item classification, developer collaboration, and expert review and discussion.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 18 Comments Advantage  An interesting method to build concept map. Drawback  The review work about the concept map is too long…  The authors don’t use table to display their experiment. Application  Concept map learning.