Projects per year
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.
|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|
|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
|Series||Lecture Notes in Artificial Intelligence (subseries)|
|Conference||Artificial Intelligence in Education|
|Abbreviated title||AIED 2017|
|Period||28/06/17 → 1/07/17|
- Automated essay scoring
- textual complexity assessment
- academic performance
- Readerbench framework
- Dutch semantic models
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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