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Lookup NU author(s): Professor Brian Lunn
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Feedback on teaching and assessment often comprises a mix of quantitative (number based) and qualitative (free text) elements. By dint of being numerical the former is often seen as more ‘objective’ despite the latter containing rich data in the form of narrative. Another issue is that in reporting free text there can be bias and selectivity introduced. With the development of algorithms to allow machine processing of free text using Free/Open Source Text Analysis Software it is possible to understand the semantics of what is written and quantify the weight of meaning and/or intent of the writer(s). An analysis of feedback in an undergraduate medical programme was undertaken using commonly available textual analyses for the free software environment for statistical computing and graphics, R. An introduction to the methodology will be presented, along with data that demonstrates how such tools can be used in understanding feedback, whether about or from students. This approach can also be used to allow collection of data about the quality of feedback provided to students, which can then be used to better train and inform their practice. Amongst issues covered will be the development of a corpus that allows sentiment analysis in a medical context as in standard corpora negative sentiment is attached to words that have an affectless technical meaning in medicine.
Author(s): Lunn B
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: Annual European Conference on Assessment in Medical Education
Year of Conference: 2019
Acceptance date: 25/07/2019
Publisher: European Board of Medical Assessors