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Starting from the presumption that writing style is proven to be a reliable predictor of comprehension, this paper investigates the extent to which textual complexity features of nurse students’ essays are related to the scores they were given. Thus, forty essays about case studies on infectious diseases written in French language were analyzed using ReaderBench, a multi-purpose framework relying on advanced Natural Language Processing techniques which provides a wide range of textual complexity indices. While the linear regression model was significant, a Discriminant Function Analysis was capable of classifying students with an 82.5% accuracy into high and low performing groups. Overall, our statistical analysis highlights essay features centered on document cohesion flow and dialogism that are predictive of teachers’ scoring processes. As text complexity strongly influences learners’ reading and understanding, our approach can be easily extended in future developments to e-portfolios assessment, in order to provide customized feedback to students.
|Title of host publication||Data Driven Approaches in Digital Education.|
|Subtitle of host publication||12th European Conference on Technology Enhanced Learning (EC-TEL 2017)|
|Editors||É. Lavoué , H. Drachsler, K. Verbert, J. Broisin, M. Pérez-Sanagustín|
|Publication status||Published - Oct 2017|
|Event||12th European Conference on Technology Enhanced Learning: Data Driven Approaches in Digital Education - Tallinn, Estonia|
Duration: 12 Sept 2017 → 15 Sept 2017
|Series||Lecture Notes in Computer Science LNCS|
|Conference||12th European Conference on Technology Enhanced Learning|
|Abbreviated title||EC-TEL 2017|
|Period||12/09/17 → 15/09/17|
- Health care
- Nursing school
- Textual complexity
- Infectious diseases and hygiene
- Case analysis
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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