Using Leximancer to Identify Themes and Patterns in the Talk of Three High-Distinction Students

Abstract

Three graduate students were asked a series of questions about what they did to get either distinctions or high distinctions. The interviewer focused on the student's understanding of assessment requirements, motivation, interest levels, and the relevance of the set task. Other questions queried prior knowledge, course learnt knowledge, the lecturer's input, and peer input. Finally, these students were asked for advice that might assist students in getting high distinctions, or that might assist lecturers in helping students to achieve these outcomes. The transcribed interview with one of these students was entered into Leximancer for text analysis. Leximancer provides an automated procedure that computes the distance between each of the terms and presents the results in a two-dimensional spatial representation. The authors in the current study report and comment on the outcome of separate and merged Leximancer analyses of the talk of one of these three students in ways that illustrate the strengths and weaknesses of text analysis.