ReaderBench Learns Dutch: Building a Comprehensive Automated Essay Scoring System for Dutch Language

Mihai Dascalu; W. Westera; Stefan Ruseti; Stefan Trausan-Matu; H.J. Kurvers

doi:10.1007/978-3-319-61425-0_5

ReaderBench Learns Dutch: Building a Comprehensive Automated Essay Scoring System for Dutch Language

Mihai Dascalu
, W. Westera
, Stefan Ruseti
, Stefan Trausan-Matu
, H.J. Kurvers

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

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.

Original language	English
Title of host publication	Artificial Intelligence in Education
Subtitle of host publication	18th International Conference, AIED 2017, Wuhan, China, June 28 – July 1, 2017, Proceedings
Editors	Elisabeth André , Ryan Baker, Xiangen Hu, Ma. Mercedes T. Rodrigo , Benedict du Boulay
Publisher	Springer International Publishing AG
Pages	52-63
Edition	1
ISBN (Electronic)	978-3-319-61425-0
ISBN (Print)	978-3-319-61424-3
DOIs	https://doi.org/10.1007/978-3-319-61425-0_5
Publication status	Published - 2017
Event	Artificial Intelligence in Education: 18th International Conference, AIED 2017, Wuhan, China, June 28 – July 1, 2017 - Wuhan, China Duration: 28 Jun 2017 → 1 Jul 2017 http://119.97.166.163/

Publication series

Series	Lecture Notes in Artificial Intelligence (subseries)
Volume	10331

Conference

Conference	Artificial Intelligence in Education
Abbreviated title	AIED 2017
Country/Territory	China
City	Wuhan
Period	28/06/17 → 1/07/17
Internet address	http://119.97.166.163/

Keywords

Automated essay scoring
textual complexity assessment
academic performance
Readerbench framework
Dutch semantic models
Games
Learning

Access to Document

10.1007/978-3-319-61425-0_5Licence: CC BY-NC-SA

Rage: Realising an Applied Gaming Eco-system
Westera, W. (PI), Georgiadis, K. (CoI), Saveski, G. (CoI), van Lankveld, G. (CoI), Bahreini, K. (CoI), van der Vegt, W. (CoI), Berkhout, J. (CoI), Nyamsuren, E. (CoI), Kluijfhout, E. (CoI) & Nadolski, R. (CoI)
European Union
1/02/15 → 31/07/19
Project: Research

Cite this

Dascalu, M., Westera, W., Ruseti, S., Trausan-Matu, S., & Kurvers, H. J. (2017). ReaderBench Learns Dutch: Building a Comprehensive Automated Essay Scoring System for Dutch Language. In E. André , R. Baker, X. Hu, M. M. T. Rodrigo , & B. du Boulay (Eds.), Artificial Intelligence in Education: 18th International Conference, AIED 2017, Wuhan, China, June 28 – July 1, 2017, Proceedings (1 ed., pp. 52-63). Article 5 Springer International Publishing AG. https://doi.org/10.1007/978-3-319-61425-0_5

Dascalu, Mihai ; Westera, W. ; Ruseti, Stefan et al. / ReaderBench Learns Dutch : Building a Comprehensive Automated Essay Scoring System for Dutch Language. Artificial Intelligence in Education: 18th International Conference, AIED 2017, Wuhan, China, June 28 – July 1, 2017, Proceedings. editor / Elisabeth André ; Ryan Baker ; Xiangen Hu ; Ma. Mercedes T. Rodrigo ; Benedict du Boulay . 1. ed. Springer International Publishing AG, 2017. pp. 52-63 (Lecture Notes in Artificial Intelligence (subseries), Vol. 10331).

@inproceedings{69327f76c5c74278af3d591f33d2bcff,

title = "ReaderBench Learns Dutch: Building a Comprehensive Automated Essay Scoring System for Dutch Language",

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{\textquoteright} scores, while a Discriminant Function Analysis predicted with substantial accuracy (75.1\%) whether they are high or low performance students.",

keywords = "Automated essay scoring, textual complexity assessment, academic performance, Readerbench framework, Dutch semantic models, Games, Learning",

author = "Mihai Dascalu and W. Westera and Stefan Ruseti and Stefan Trausan-Matu and H.J. Kurvers",

year = "2017",

doi = "10.1007/978-3-319-61425-0\_5",

language = "English",

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publisher = "Springer International Publishing AG",

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booktitle = "Artificial Intelligence in Education",

address = "Switzerland",

edition = "1",

note = "Artificial Intelligence in Education : 18th International Conference, AIED 2017, Wuhan, China, June 28 – July 1, 2017, AIED 2017 ; Conference date: 28-06-2017 Through 01-07-2017",

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}

Dascalu, M, Westera, W, Ruseti, S, Trausan-Matu, S & Kurvers, HJ 2017, ReaderBench Learns Dutch: Building a Comprehensive Automated Essay Scoring System for Dutch Language. in E André , R Baker, X Hu, MM T. Rodrigo & B du Boulay (eds), Artificial Intelligence in Education: 18th International Conference, AIED 2017, Wuhan, China, June 28 – July 1, 2017, Proceedings. 1 edn, 5, Springer International Publishing AG, Lecture Notes in Artificial Intelligence (subseries), vol. 10331, pp. 52-63, Artificial Intelligence in Education, Wuhan, China, 28/06/17. https://doi.org/10.1007/978-3-319-61425-0_5

ReaderBench Learns Dutch: Building a Comprehensive Automated Essay Scoring System for Dutch Language. / Dascalu, Mihai; Westera, W.; Ruseti, Stefan et al.
Artificial Intelligence in Education: 18th International Conference, AIED 2017, Wuhan, China, June 28 – July 1, 2017, Proceedings. ed. / Elisabeth André ; Ryan Baker; Xiangen Hu; Ma. Mercedes T. Rodrigo ; Benedict du Boulay . 1. ed. Springer International Publishing AG, 2017. p. 52-63 5 (Lecture Notes in Artificial Intelligence (subseries), Vol. 10331).

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

TY - GEN

T1 - ReaderBench Learns Dutch

T2 - Artificial Intelligence in Education

AU - Dascalu, Mihai

AU - Westera, W.

AU - Ruseti, Stefan

AU - Trausan-Matu, Stefan

AU - Kurvers, H.J.

PY - 2017

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N2 - 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.

AB - 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.

KW - Automated essay scoring

KW - textual complexity assessment

KW - academic performance

KW - Readerbench framework

KW - Dutch semantic models

KW - Games

KW - Learning

U2 - 10.1007/978-3-319-61425-0_5

DO - 10.1007/978-3-319-61425-0_5

M3 - Conference Article in proceeding

SN - 978-3-319-61424-3

T3 - Lecture Notes in Artificial Intelligence (subseries)

SP - 52

EP - 63

BT - Artificial Intelligence in Education

A2 - André , Elisabeth

A2 - Baker, Ryan

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A2 - du Boulay , Benedict

PB - Springer International Publishing AG

Y2 - 28 June 2017 through 1 July 2017

ER -

Dascalu M, Westera W, Ruseti S, Trausan-Matu S, Kurvers HJ. ReaderBench Learns Dutch: Building a Comprehensive Automated Essay Scoring System for Dutch Language. In André E, Baker R, Hu X, T. Rodrigo MM, du Boulay B, editors, Artificial Intelligence in Education: 18th International Conference, AIED 2017, Wuhan, China, June 28 – July 1, 2017, Proceedings. 1 ed. Springer International Publishing AG. 2017. p. 52-63. 5. (Lecture Notes in Artificial Intelligence (subseries), Vol. 10331). doi: 10.1007/978-3-319-61425-0_5

ReaderBench Learns Dutch: Building a Comprehensive Automated Essay Scoring System for Dutch Language

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