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

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

doi:10.1007/978-3-319-66610-5_4

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 proceeding › Conference Article in proceeding › Academic › peer-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 language	English
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
Publisher	Springer
Pages	43-53
Volume	10474
ISBN (Electronic)	978-3-319-66610-5
ISBN (Print)	978-3-319-66609-9
DOIs	https://doi.org/10.1007/978-3-319-66610-5_4
Publication status	Published - Oct 2017
Externally published	Yes
Event	12th European Conference on Technology Enhanced Learning: Data Driven Approaches in Digital Education - Tallinn, Estonia Duration: 12 Sept 2017 → 15 Sept 2017 http://ectel2017.httc.de/index.php?id=777

Publication series

Series	Lecture Notes in Computer Science LNCS
Volume	10474

Conference

Conference	12th European Conference on Technology Enhanced Learning
Abbreviated title	EC-TEL 2017
Country/Territory	Estonia
City	Tallinn
Period	12/09/17 → 15/09/17
Internet address	http://ectel2017.httc.de/index.php?id=777

Keywords

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

Access to Document

10.1007/978-3-319-66610-5_4Licence: CC BY-NC-ND

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., 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). Springer. https://doi.org/10.1007/978-3-319-66610-5_4

Dascalu, Mihai ; Dessus, Philippe ; Thuez, Laurent et al. / 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, Vol. 10474).

@inproceedings{b0c3d3eaf7d2481ea929fbb7db9609eb,

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

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{\textquoteright} 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{\textquoteright} scoring processes. As text complexity strongly influences learners{\textquoteright} reading and understanding, our approach can be easily extended in future developments to e-portfolios assessment, in order to provide customized feedback to students.",

keywords = "Health care, Nursing school, Textual complexity, Infectious diseases and hygiene, Case analysis",

author = "Mihai Dascalu and Philippe Dessus and Laurent Thuez and Stefan Trausan-Matu",

year = "2017",

month = oct,

doi = "10.1007/978-3-319-66610-5_4",

language = "English",

isbn = "978-3-319-66609-9",

volume = "10474",

series = "Lecture Notes in Computer Science LNCS",

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booktitle = "Data Driven Approaches in Digital Education.",

note = "12th European Conference on Technology Enhanced Learning : Data Driven Approaches in Digital Education, EC-TEL 2017 ; Conference date: 12-09-2017 Through 15-09-2017",

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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, Springer, Lecture Notes in Computer Science LNCS, vol. 10474, pp. 43-53, 12th European Conference on Technology Enhanced Learning, 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 et al.
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 proceeding › Conference Article in proceeding › Academic › peer-review

TY - GEN

T1 - How Well Do Student Nurses Write Case Studies? A Cohesion-Centered Textual Complexity Analysis

AU - Dascalu, Mihai

AU - Dessus, Philippe

AU - Thuez, Laurent

AU - Trausan-Matu, Stefan

PY - 2017/10

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N2 - 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.

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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KW - Case analysis

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DO - 10.1007/978-3-319-66610-5_4

M3 - Conference Article in proceeding

SN - 978-3-319-66609-9

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BT - Data Driven Approaches in Digital Education.

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A2 - Broisin, J.

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PB - Springer

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ER -

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, Vol. 10474). doi: 10.1007/978-3-319-66610-5_4

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

Abstract

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Keywords

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