Evaluating machine learning approaches to classify pharmacy students' reflective statements

Liu, Ming, Shum, Simon Buckingham, Mantzourani, Efi ORCID: https://orcid.org/0000-0002-6313-1409 and Lucas, Cherie 2019. Evaluating machine learning approaches to classify pharmacy students' reflective statements. Presented at: 20th International Conference, AIED, Chicago, US, 25-29 Jun 2019. Published in: Isotani, Seiji, Millán, Eva, Ogan, Amy, Hastings, Peter, McLaren, Bruce and Luckin, Rose eds. Artificial Intelligence in Education. Lecture Notes in Artificial Intelligence. Lecture Notes in Artificial Intelligence , vol.11625 Cham, Switzerland: Springer Verlag, pp. 220-230. 10.1007/978-3-030-23204-7_19

PDF - Accepted Post-Print Version Download (383kB)

Abstract

Reflective writing is widely acknowledged to be one of the most effective learning activities for promoting students’ self-reflection and critical thinking. However, manually assessing and giving feedback on reflective writing is time consuming, and known to be challenging for educators. There is little work investigating the potential of automated analysis of reflective writing, and even less on machine learning approaches which offer potential advantages over rule-based approaches. This study reports progress in developing a machine learning approach for the binary classification of pharmacy students’ reflective statements about their work placements. Four common statistical classifiers were trained on a corpus of 301 statements, using emotional, cognitive and linguistic features from the Linguistic Inquiry and Word Count (LIWC) analysis, in combination with affective and rhetorical features from the Academic Writing Analytics (AWA) platform. The results showed that the Random-forest algorithm performed well (F-score = 0.799) and that AWA features, such as emotional and reflective rhetorical moves, improved performance.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Pharmacy
Publisher:	Springer Verlag
ISBN:	9783030232030
ISSN:	0302-9743
Date of First Compliant Deposit:	16 December 2019
Date of Acceptance:	1 July 2019
Last Modified:	26 Oct 2022 08:28
URI:	https://orca.cardiff.ac.uk/id/eprint/127563

Citation Data

Cited 17 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item