Analysis using natural language processing of feedback data from two mathematics support centres

ABSTRACT

This paper explores analysis of feedback data collected from student consultations at two mathematics support centres at universities in Australia and Ireland. Unstructured text data was collected over six years and includes qualitative data on student queries collected during the consultations from mathematics and statistics related subjects. Topic modelling and clustering algorithms are used to uncover key themes in the data across stages. Common areas of difficulty experienced by undergraduate students at both universities are investigated and a comparison between them is shown. The results suggest that, despite institutional differences, there is considerable overlap in the types of mathematical and statistical difficulties experienced by students in their first and second year of university at these institutions. We discuss how the ability to uncover such common mathematical and statistical themes with the aid of text mining techniques can be used to improve the support provided by mathematics support centres in terms of providing an efficient and effective service. The code for analyses at both institutions is provided in a GitHub repository so other academic support centres may use it. Outcomes of this analysis have implications for mainstream mathematics and statistics instructors who wish to gain further insights into their students' learning.