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

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


OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments


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.


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).


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


Methodology- The Concept Map Miner(Concept Identification)

Objectives : identified that compound nounsInput: sentence’s dependency tree

dependency tree

linking words


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’.


Methodology- The Concept Map Miner (Relationship Identification)

Objectives : identify concept’s relationshipsInput: terminological map and a set of concepts using Dijkstra's algorithm


Methodology- The Concept Map Miner (summarization)

using Latent Semantic Analysis (LSA)


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


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


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.


Experiment - Comparative Measures for CMs

Lexical term Precision (LP)

Taxonomic Overlap Precision (TP)


Experiment - Results


Experiment - Integration of CMM as Writing Support Tool


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.


Comments• Advantages– Tutors assess the essays faster and more

accurately and consistently• Applications– Concept Map Mining.

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

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