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Please use this identifier to cite or link to this item: http://hdl.handle.net/1820/9058
Title: ReaderBench Learns Dutch: Building a Comprehensive Automated Essay Scoring System for Dutch Language
Authors: Dascalu, Mihai
Westera, Wim
Ruseti, Stefan
Trausan-Matu, Stefan
Kurvers, Hub
Keywords: Automated essay scoring
textual complexity assessment
Academic performance
Readerbench framework
Dutch semantic models
Games
Learning
Issue Date: Jun-2017
Publisher: Springer International Publishing AG
Citation: Dascalu, M., Westera, W., Ruseti, S., Trausan-Matu, S., & Kurvers, H. (2017). ReaderBench Learns Dutch: Building a Comprehensive Automated Essay Scoring System for Dutch. In E. André, R. Baker, X. Hu, M. M. T. Rodrigo & B. du Boulay (Eds.), 18th Int. Conf. on Artificial Intelligence in Education (AIED 2017), LNAI/LNCS 10331, (pp. 52–63). Wuhan, China: Springer International Publishing AG. DOI: 10.1007/978-3-319-61425-0_5
Abstract: Automated Essay Scoring has gained a wider applicability and usage with the integration of advanced Natural Language Processing techniques which enabled in-depth analyses of discourse in order capture the specificities of written texts. In this paper, we introduce a novel Automatic Essay Scoring method for Dutch language, built within the Readerbench framework, which encompasses a wide range of textual complexity indices, as well as an automated segmentation approach. Our method was evaluated on a corpus of 173 technical reports automatically split into sections and subsections, thus forming a hierarchical structure on which textual complexity indices were subsequently applied. The stepwise regression model explained 30.5% of the variance in students’ scores, while a Discriminant Function Analysis predicted with substantial accuracy (75.1%) whether they are high or low performance students.
URI: http://hdl.handle.net/1820/9058
Appears in Collections:1. RAGE Publications

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