How Well Do Student Nurses Write Case Studies? A Cohesion-Centered Textual Complexity Analysis

Mihai Dascalu, Philippe Dessus, Laurent Thuez, Stefan Trausan-Matu

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationData Driven Approaches in Digital Education.
Subtitle of host publication12th European Conference on Technology Enhanced Learning (EC-TEL 2017)
EditorsÉ. Lavoué , H. Drachsler, K. Verbert, J. Broisin, M. Pérez-Sanagustín
PublisherSpringer
Pages43-53
Volume10474
ISBN (Electronic)978-3-319-66610-5
ISBN (Print)978-3-319-66609-9
DOIs
Publication statusPublished - Oct 2017
Externally publishedYes
EventData Driven Approaches in Digital Education: 12th European Conference on Technology Enhanced Learning: EC-TEL - Tallinn, Estonia
Duration: 12 Sep 201715 Sep 2017
http://ectel2017.httc.de/index.php?id=777

Publication series

NameLecture Notes in Computer Science LNCS
PublisherSpringer
Volume10474

Conference

ConferenceData Driven Approaches in Digital Education
CountryEstonia
CityTallinn
Period12/09/1715/09/17
Internet address

Fingerprint

nurse
French language
student
statistical analysis
contagious disease
comprehension
regression
teacher
language
Group

Keywords

  • Health care
  • Nursing school
  • Textual complexity
  • Infectious diseases and hygiene
  • Case analysis

Cite this

Dascalu, M., Dessus, P., Thuez, L., & Trausan-Matu, S. (2017). How Well Do Student Nurses Write Case Studies? A Cohesion-Centered Textual Complexity Analysis. In É. Lavoué , H. Drachsler, K. Verbert, J. Broisin, & M. Pérez-Sanagustín (Eds.), Data Driven Approaches in Digital Education. : 12th European Conference on Technology Enhanced Learning (EC-TEL 2017) (Vol. 10474, pp. 43-53). (Lecture Notes in Computer Science LNCS; Vol. 10474). Springer. https://doi.org/10.1007/978-3-319-66610-5_4
Dascalu, Mihai ; Dessus, Philippe ; Thuez, Laurent ; Trausan-Matu, Stefan. / How Well Do Student Nurses Write Case Studies? A Cohesion-Centered Textual Complexity Analysis. Data Driven Approaches in Digital Education. : 12th European Conference on Technology Enhanced Learning (EC-TEL 2017) . editor / É. Lavoué ; H. Drachsler ; K. Verbert ; J. Broisin ; M. Pérez-Sanagustín. Vol. 10474 Springer, 2017. pp. 43-53 (Lecture Notes in Computer Science LNCS).
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Dascalu, M, Dessus, P, Thuez, L & Trausan-Matu, S 2017, How Well Do Student Nurses Write Case Studies? A Cohesion-Centered Textual Complexity Analysis. in É Lavoué , H Drachsler, K Verbert, J Broisin & M Pérez-Sanagustín (eds), Data Driven Approaches in Digital Education. : 12th European Conference on Technology Enhanced Learning (EC-TEL 2017) . vol. 10474, Lecture Notes in Computer Science LNCS, vol. 10474, Springer, pp. 43-53, Data Driven Approaches in Digital Education, Tallinn, Estonia, 12/09/17. https://doi.org/10.1007/978-3-319-66610-5_4

How Well Do Student Nurses Write Case Studies? A Cohesion-Centered Textual Complexity Analysis. / Dascalu, Mihai; Dessus, Philippe; Thuez, Laurent; Trausan-Matu, Stefan.

Data Driven Approaches in Digital Education. : 12th European Conference on Technology Enhanced Learning (EC-TEL 2017) . ed. / É. Lavoué ; H. Drachsler; K. Verbert; J. Broisin; M. Pérez-Sanagustín. Vol. 10474 Springer, 2017. p. 43-53 (Lecture Notes in Computer Science LNCS; Vol. 10474).

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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AB - 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.

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Dascalu M, Dessus P, Thuez L, Trausan-Matu S. How Well Do Student Nurses Write Case Studies? A Cohesion-Centered Textual Complexity Analysis. In Lavoué É, Drachsler H, Verbert K, Broisin J, Pérez-Sanagustín M, editors, Data Driven Approaches in Digital Education. : 12th European Conference on Technology Enhanced Learning (EC-TEL 2017) . Vol. 10474. Springer. 2017. p. 43-53. (Lecture Notes in Computer Science LNCS). https://doi.org/10.1007/978-3-319-66610-5_4