Using text analytics to understand open-ended student comments at scale: Insights from four case studies

Ullmann, Thomas and Rienties, Bart (2021). Using text analytics to understand open-ended student comments at scale: Insights from four case studies. In: Shah, Mahsood; Richardson, John; Pabel, Anja and Oliver, Beverley eds. Assessing and Enhancing Student Experience in Higher Education. Cham: Palgrave Macmillan, (In Press).

DOI: https://doi.org/10.1007/978-3-030-80889-1

Abstract

Students write tens of thousands of open-ended comments in student evaluation questionnaires, which are collected as part of institutional and national surveys. Often as part of a quality enhancement strategy, teachers analyse these comments in order to gain insights into student perspectives and guide revisions of modules. While institutions have access to enormous amounts of qualitative data, to date limited efforts have been made to analyse and disseminate these data, which could be used by academics and administrative leaders to identify areas of good practice and areas needing improvement. This chapter will examine several innovative uses of qualitative data with automated text analytics (i.e., natural language processing) used to assess and enhance the student experience. Using four case studies from the Open University UK, we will discuss the affordances and limitations of such methods. We found strong differences in quality and quantity of contributions to student comments based upon individual and disciplinary factors.

Viewing alternatives

Metrics

Public Attention

Altmetrics from Altmetric

Number of Citations

Citations from Dimensions

Item Actions

Export

About

Recommendations