Towards an Automated Model of Comprehension (AMoC)

Mihai Dascalu, Ionut Cristian Paraschiv, Danielle McNamara, Stefan Trausan-Matu

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

1 Scopus citations

Abstract

Reading is a complex cognitive process wherein learners acquire new information and consolidate their knowledge. Readers create a mental representation for a given text by processing relevant words that, along with prior inferred concepts, become activated and establish meaningful associations. Our automated model of comprehension (AMoC) uses an automated approach for simulating the ways in which learners read and conceptualize by considering both text-based information consisting of syntactic dependencies, as well as inferred concepts from semantic models. AMoC makes use of cutting edge Natural Language Processing techniques, transcends beyond existing models, and represents a novel alternative for modeling how learners potentially conceptualize read information. This study presents side-by-side comparisons of the results generated by our model versus the ones generated by the Landscape model.

Original languageEnglish (US)
Title of host publicationLifelong Technology-Enhanced Learning - 13th European Conference on Technology Enhanced Learning, EC-TEL 2018, Proceedings
EditorsRaymond Elferink, Hendrik Drachsler, Viktoria Pammer-Schindler, Mar Perez-Sanagustin, Maren Scheffel
PublisherSpringer Verlag
Pages427-436
Number of pages10
ISBN (Print)9783319985718
DOIs
StatePublished - Jan 1 2018
Event13th European Conference on Technology Enhanced Learning, EC-TEL 2018 - Leeds, United Kingdom
Duration: Sep 3 2018Sep 6 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11082 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other13th European Conference on Technology Enhanced Learning, EC-TEL 2018
CountryUnited Kingdom
CityLeeds
Period9/3/189/6/18

Keywords

  • Comprehension modeling
  • Landscape Model
  • Natural Language Processing
  • Semantic models

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Dascalu, M., Paraschiv, I. C., McNamara, D., & Trausan-Matu, S. (2018). Towards an Automated Model of Comprehension (AMoC). In R. Elferink, H. Drachsler, V. Pammer-Schindler, M. Perez-Sanagustin, & M. Scheffel (Eds.), Lifelong Technology-Enhanced Learning - 13th European Conference on Technology Enhanced Learning, EC-TEL 2018, Proceedings (pp. 427-436). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11082 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-98572-5_33