Predicting comprehension from students’ summaries

Mihai Dascalu, Larise Lucia Stavarache, Philippe Dessus, Stefan Trausan-Matu, Danielle McNamara, Maryse Bianco

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

5 Scopus citations

Abstract

Comprehension among young students represents a key component of their formation throughout the learning process. Moreover, scaffolding students as they learn to coherently link information, while organically constructing a solid knowledge base, is crucial to students’ development, but requires regular assessment and progress tracking. To this end, our aim is to provide an automated solution for analyzing and predicting students’ comprehension levels by extracting a combination of reading strategies and textual complexity factors from students’ summaries. Building upon previous research and enhancing it by incorporating new heuristics and factors, Support Vector Machine classification models were used to validate our assumptions that automatically identified reading strategies, together with textual complexity indices applied on students’ summaries, represent reliable estimators of comprehension.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings
EditorsCristina Conati, Neil Heffernan, Antonija Mitrovic, M. Felisa Verdejo
PublisherSpringer Verlag
Pages95-104
Number of pages10
ISBN (Print)9783319197722
DOIs
StatePublished - Jan 1 2015
Event17th International Conference on Artificial Intelligence in Education, AIED 2015 - Madrid, Spain
Duration: Jun 22 2015Jun 26 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9112
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other17th International Conference on Artificial Intelligence in Education, AIED 2015
CountrySpain
CityMadrid
Period6/22/156/26/15

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Keywords

  • Comprehension prediction
  • Reading strategies
  • Summaries assessment
  • Support vector machines
  • Textual complexity

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Dascalu, M., Stavarache, L. L., Dessus, P., Trausan-Matu, S., McNamara, D., & Bianco, M. (2015). Predicting comprehension from students’ summaries. In C. Conati, N. Heffernan, A. Mitrovic, & M. Felisa Verdejo (Eds.), Artificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings (pp. 95-104). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9112). Springer Verlag. https://doi.org/10.1007/978-3-319-19773-9_10