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Presenter : Wu, Min-Cong Authors : Jorge Villalon and Rafael A. Calvo 2011, EST

Concept Maps as Cognitive Visualizations of Writing Assignments . Presenter : Wu, Min-Cong Authors : Jorge Villalon and Rafael A. Calvo 2011, EST. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

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Presenter : Wu, Min-Cong Authors : Jorge Villalon and Rafael A. Calvo 2011, EST

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  1. Concept Maps as Cognitive Visualizations of Writing Assignments Presenter : Wu, Min-Cong Authors : Jorge Villalon and Rafael A. Calvo2011, EST

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. Motivation • This is a significant improvement over previous efforts that focused on providing feedback on the finalproduct that students submit, Concept map visualization can help students reflect about their own writing.

  4. Objectives • We have also showed new approaches to help students reflect on their writing and how students understand the use of these new tools(CMM).

  5. Methodology- The Concept Map Miner C:set of concepts R: set of relationships between concepts T:the map's topology or spatial distribution of the concepts. First step Third step Second step

  6. Methodology- The Concept Map Miner (Concept Identification) Objectives : identified that compound nouns Input: sentence’s dependency tree dependency tree linking words

  7. Methodology- The Concept Map Miner (Concept Identification) it corresponds to the compound noun ‘artificial language’. using the extracted terminological maps with all terminological map rules applied to obtain a reduced map. vertices

  8. Methodology- The Concept Map Miner (Relationship Identification) Objectives : identify concept’s relationships Input: terminological map and a set of concepts using Dijkstra's algorithm

  9. Methodology- The Concept Map Miner (summarization) using Latent Semantic Analysis (LSA)

  10. Methodology- Relationship Extraction and CMM requires that a group of human annotators build a ‘gold standard’ corpus with annotations. compare those extracted automatically. problem Solve annotated by two or more human coders who are required to identify Identifying knowledge in text is a subjective task

  11. Experiment-Data Dataset: A set of essays (N=43) collected as a writing proficiency diagnostic activity for first year-university students

  12. Experiment-Annotation Method A first version of the benchmarking corpus the main problem found was that coders created relationships that were not explicitly present in the essay, but were an interpretation of several propositions.

  13. Experiment-Comparative Measures for CMs Lexical term Precision (LP) Taxonomic Overlap Precision (TP)

  14. Experiment-Results

  15. Experiment-Integration of CMM as Writing Support Tool

  16. Conclusions • Student : The results show that the automatic generation of CMs from documents is feasible, despite the complexities of noisy data. • Instructor: averaging 94% for LP with human coders.

  17. Comments • Advantages • Tutors assess the essays faster and more accurately and consistently • Applications • Concept Map Mining.

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