Exploring instances of Deleuzian rhizomatic patterns in student writing and online interactions at an open distance eLearning institution in South Africa
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Authors
Nkhobo, Tlatso Ishmael
Issue Date
2022-03-24
Type
Thesis
Language
en
Keywords
Rhizome , Rhizomatic patterns , Writing analysis , Student writing , Academic writing models , ODel , Student interaction patterns , myUnisa’s online discussion forum , Social network analysis , Social learning network analysis , Key themes , Concordances , Linking adverbials , MS Teams , MS Power BI , Gephi , AntConc , AntMover , AntWordProfiler , Readability index
Alternative Title
Abstract
This study aimed to explore and make visualisations of Deleuzian Rhizomatic Patterns in first-year students’ writing samples of academic writing. Online interactions on myUnisa’s online discussion forums and the Microsoft (MS) Teams virtual classes of 2020 in Academic Language and Literacy in English (ENG53) were examined rhizomatically. Traditionally, academic literacy studies employ linear models of studying students’ academic writing. However, recent academic literacy studies advocate that student writing be studied from multiple perspectives. One such approach is the Deleuzian Rhizomatic Approach to writing.
The Deleuzian Rhizomatic Approach to writing employs writing analytics that can be applied to the academic writing samples in terms of key themes (concordances). Therefore, in investigating linking adverbials in online interactions of students and lecturers, writing analytics were applied. Writing analytics as a part of learning analytics entails, in this case, various data related to student writing that could be computationally analysed using writing software tools. The writing samples were analysed using rhizoanalysis by means of the AntConc, AntMover, and AntWordProfiler software applications. Rhizomatic patterns in students’ writing samples drawn from interactions on the 2020 ENG53 MS Teams virtual classroom and myUnisa’s ODF were visualised using social network analysis (SNA), online tools MS Power BI and Gephi. In addition, a readability index of the writing samples was assessed through the AntWordProfiler multiplatform tool and was visualised rhizomatically.
The student writing samples revealed sectional rhizomatic patterns in various forms, as well as visualizations of MS Power BI and Gephi which portrayed rhizomatic patterns bearing various degrees of interaction nodes between students and lecturers. Furthermore, the AntWordProfiler revealed that readability levels of the writing samples were comprehensible but varied rhizomatically between students.