Extended Multi-document Cohesion Network Analysis Centered on Comprehension Prediction

Bogdan Nicula, Cecile A. Perret, Mihai Dascalu, Danielle S. McNamara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Theories of discourse argue that comprehension depends on the coherence of the learner’s mental representation. Our aim is to create a reliable automated representation to estimate readers’ level of comprehension based on different productions, namely self-explanations and answers to open-ended questions. Previous work relied on Cohesion Network Analysis to model a cohesion graph composed of semantic links between multiple reference texts and student productions. From this graph, a set of features was derived and used to build machine learning models to predict student comprehension scores. In this paper, we build on top of the previous study by: a) extending the CNA graph by adding new semantic links targeting specific sentences that should have been captured within the learner’s productions, and b) cleaning the self-explanations by eliminating frozen expression, as well as entries which seemed nearly identical to the source text. The results are in line with the conclusions of the previous study regarding the importance of both self-explanations and question answers in predicting the students’ reading comprehension level. They also outline the limitations of our feature generation approach, in which no substantial improvements were detected, despite adding more fine-grained features.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Education - 21st International Conference, AIED 2020, Proceedings
EditorsIg Ibert Bittencourt, Mutlu Cukurova, Rose Luckin, Kasia Muldner, Eva Millán
PublisherSpringer
Pages228-233
Number of pages6
ISBN (Print)9783030522391
DOIs
StatePublished - 2020
Event21st International Conference on Artificial Intelligence in Education, AIED 2020 - Ifrane, Morocco
Duration: Jul 6 2020Jul 10 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12164 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st International Conference on Artificial Intelligence in Education, AIED 2020
CountryMorocco
CityIfrane
Period7/6/207/10/20

Keywords

  • Cohesion Network Analysis
  • Multi-document comprehension modeling
  • Natural Language Processing

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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