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Mining Texts, Learner Productions and Strategies with ReaderBench

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Educational Data Mining

Part of the book series: Studies in Computational Intelligence ((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.

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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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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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Authors and Affiliations

  1. University Politehnica of Bucharest, 313 Splaiul Independentei, 060042, Bucharest, Romania

    Mihai Dascalu & Stefan Trausan-Matu

  2. Laboratoire des Sciences de l’Education, University Grenoble Alpes, BP 47, 38040, Grenoble, France

    Philippe Dessus & Maryse Bianco

  3. Laboratoire de linguistique et didactique des langues étrangères et maternelles, University Grenoble Alpes, BP 25, 38040, Grenoble, France

    Aurélie Nardy

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  1. Mihai Dascalu
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  2. Philippe Dessus
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  3. Maryse Bianco
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  4. Stefan Trausan-Matu
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  5. Aurélie Nardy
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Correspondence to Mihai Dascalu .

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  1. Escuela Superior de Ingeniería Mecánica y Eléctrica, Zacatenco (ESIME-Z), World Outreach Light to the Nations Ministries (WOLNM), Instituto Politécnico Nacional (IPN), Gustavo A. Madero, Mexico City, Distrito Federal, Mexico

    Alejandro Peña-Ayala

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Dascalu, M., Dessus, P., Bianco, M., Trausan-Matu, S., Nardy, A. (2014). Mining Texts, Learner Productions and Strategies with ReaderBench . In: Peña-Ayala, A. (eds) Educational Data Mining. Studies in Computational Intelligence, vol 524. Springer, Cham. https://doi.org/10.1007/978-3-319-02738-8_13

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