Exploring Instances of Deleuzian Rhizomatic Patterns in Students’ Writing and in Online Student Interactions

Abstract

Globally, it is a standard practice to study students’ academic writing by using linear academic-writing models. This study investigated instances of Deleuzian rhizomatic patterns in students’ writing and in online student interactions at an open and distance e-learning (ODeL) institution in South Africa. A convenience sample of 13 students’ paragraph writing samples and of 370 first-year students was used. All the participants were enrolled in a level-one module, ENG1503, in the second semester of 2020. The study followed a mixed-method approach, and utilized AntConc and AntMover to analyse the students’ writing samples, as well as Microsoft Power Business Intelligence (MS Power BI) and Gephi, in order to analyse and visualise online student interactions. When students’ writing samples were analysed in terms of keywords (e.g., key themes) by using the software applications employed in this study, various rhizomatic patterns were detected in the students’ text files. For example, the key-word frequencies of key themes, such as religion and cult, showed that these two key themes were used differently at the end of each concordance spectrum, thereby underscoring their varying rhizomatic patterns of usage in students’ respective text files. Online student interactions on both myUnisa’s ODF and MS Teams were visualized rhizomatically. The findings of this study underscore the importance of investigating and analysing students’ writing – not only from linear models, but also from non-linear perspectives, such as a rhizomatic approach. Additionally, they underline the significance of leveraging the opportunities offered by students’ writing analysis technologies, such as those employed in this study.