presenter : wu, min-cong authors : jorge villalon and rafael a. calvo 2011, est
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
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Intelligent Database Systems Lab
Presenter : WU, MIN-CONG
Authors : Jorge Villalon and Rafael A. Calvo
2011, EST
Concept Maps as Cognitive Visualizations of Writing Assignments
![Page 2: Presenter : Wu, Min-Cong Authors : Jorge Villalon and Rafael A. Calvo 2011, EST](https://reader036.vdocuments.us/reader036/viewer/2022062502/568163c5550346895dd4f170/html5/thumbnails/2.jpg)
Intelligent Database Systems Lab
OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments
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Intelligent Database Systems Lab
Motivation• This is a significant improvement over previous
efforts that focused on providing feedback on
the final product that students submit, Concept
map visualization can help students reflect
about their own writing.
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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).
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Methodology- The Concept Map Miner
C:set of conceptsR: set of relationships between conceptsT:the map's topology or spatial distribution of the concepts.
First step
Second step
Third step
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Methodology- The Concept Map Miner(Concept Identification)
Objectives : identified that compound nounsInput: sentence’s dependency tree
dependency tree
linking words
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Intelligent Database Systems Lab
Methodology- The Concept Map Miner(Concept Identification)
using the extracted terminological maps with all terminological map rules applied to obtain a reduced map.
vertices
it corresponds to thecompound noun ‘artificial language’.
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Intelligent Database Systems Lab
Methodology- The Concept Map Miner (Relationship Identification)
Objectives : identify concept’s relationshipsInput: terminological map and a set of concepts using Dijkstra's algorithm
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Methodology- The Concept Map Miner (summarization)
using Latent Semantic Analysis (LSA)
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Methodology- Relationship Extraction and CMM
requires that a group of human annotators build a ‘gold standard’ corpus with annotations.
compare
those extracted automatically.
problem
Identifying knowledge in text is a subjective task
Solve annotated by two ormore human coderswho are required to identify
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Experiment - Data Dataset: A set of essays (N=43) collected as a writing proficiency diagnostic activity for first year-university students
Average word Total words
Each essay 468 words
set of essays 18,431 words
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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.
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Experiment - Comparative Measures for CMs
Lexical term Precision (LP)
Taxonomic Overlap Precision (TP)
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Experiment - Results
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Experiment - Integration of CMM as Writing Support Tool
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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.
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Intelligent Database Systems Lab
Comments• Advantages– Tutors assess the essays faster and more
accurately and consistently• Applications– Concept Map Mining.