Mining Texts, Learner Productions and Strategies with ReaderBench

  • Mihai Dascalu
  • Philippe Dessus
  • Maryse Bianco
  • Stefan Trausan-Matu
  • Aurélie Nardy
Chapter
First Online:
Part of the Studies in Computational Intelligence book series (SCI, volume 524)

Abstract

The chapter introduces ReaderBench, a multi-lingual and flexible environment that integrates text mining technologies for assessing a wide range of learners’ productions and for supporting teachers in several ways. ReaderBench offers three main functionalities in terms of text analysis: cohesion-based assessment, reading strategies identification and textual complexity evaluation. All of these have been subject to empirical validations. ReaderBench may be used throughout an entire educational scenario, starting from the initial complexity assessment of the reading materials, the assignment of texts to learners, the detection of reading strategies reflected in one’s self-explanations, and comprehension evaluation fostering learner’s self-regulation process.

Keywords

Cohesion-based discourse analysis Topics extraction Reading strategies Textual complexity 

Abbreviations

AA

Adjacent agreement

CAF

Complexity, accuracy and fluency

CSCL

Computer supported collaborative learning

DRP

Degree of reading power

EA

Exact agreement

FFL

French as foreign language

ICC

Intra-class correlations

LDA

Latent Dirichlet allocation

LMS

Learning management system

LSA

Latent semantic analysis

NLP

Natural language processing

POS

Part of speech

SVM

Support vector machine

TASA

Touchstone Applied Science Associates, Inc

Tf-Idf

Term frequency – inverse document frequency

WOLF

WordNet Libre du Français

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Notes

Acknowledgments

This research was supported by an Agence Nationale de la Recherche (ANR-10-BLAN-1907) grant, by the 264207 ERRIC–Empowering Romanian Research on Intelligent Information Technologies/FP7-REGPOT-2010-1 and the POSDRU/107/1.5/S/76909 Harnessing human capital in research through doctoral scholarships (ValueDoc) projects. We also wish to thank Sonia Mandin, who kindly provided experimental data used for the validation of sentence importance. Some parts of this paper stem from [55].

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mihai Dascalu
    • 1
  • Philippe Dessus
    • 2
  • Maryse Bianco
    • 2
  • Stefan Trausan-Matu
    • 1
  • Aurélie Nardy
    • 3
  1. 1.University Politehnica of BucharestBucharestRomania
  2. 2.Laboratoire des Sciences de l’EducationUniversity Grenoble AlpesGrenobleFrance
  3. 3.Laboratoire de linguistique et didactique des langues étrangères et maternellesUniversity Grenoble AlpesGrenobleFrance

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