Evaluating ML for Pharmacy Student Reflection

UTS will be represented at the 20th International Conference on Artificial Intelligence in Education by CIC research fellow in writing analytics, Ming Liu, who leads a new paper from our ongoing collaboration with Cherie Lucas (UTS School of Pharmacy), now joined by her pharmacy colleague Efi Mantzourani (Cardiff University).

Building on our previous work in the Academic Writing Analytics project, which uses a rule-based implementation of Ágnes Sándor’s concept matching framework, this is our first paper to investigate the potential of machine learning approaches to the detection of reflective statements in student writing about their work placements.

Liu, M., Buckingham Shum, S., Mantzourani, E. and Lucas, C. (2019). Evaluating Machine Learning Approaches to Classify Pharmacy Students’ Reflective StatementsProceedings AIED2019: 20th International Conference on Artificial Intelligence in Education, June 25th – 29th 2019, Chicago, USA. Lecture Notes in Computer Science & Artificial Intelligence: Springer. 

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.

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