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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 |
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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 | |
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) |
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Volume | 10331 |
Conference
Conference | Artificial Intelligence in Education |
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Abbreviated title | AIED 2017 |
Country/Territory | China |
City | Wuhan |
Period | 28/06/17 → 1/07/17 |
Internet address |
Keywords
- Automated essay scoring
- textual complexity assessment
- academic performance
- Readerbench framework
- Dutch semantic models
- Games
- Learning
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Rage: Realising an Applied Gaming Eco-system
Westera, W., Georgiadis, K., Saveski, G., van Lankveld, G., Bahreini, K., van der Vegt, W., Berkhout, J., Nyamsuren, E., Kluijfhout, E. & Nadolski, R.
1/02/15 → 31/07/19
Project: Research