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

Intelligent Database Systems Lab

OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments

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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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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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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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Comments• Advantages– Tutors assess the essays faster and more

accurately and consistently• Applications– Concept Map Mining.