SEVEN EFFECTIVE STRATEGIES FOR WRITING DATA COMMENTARY: INSIGHTS FROM MASTER’S-LEVEL ACADEMIC WRITING PRACTICE

Abstract

The article explores both theoretical and practical aspects of writing data commentary within the framework of English academic writing. It highlights the relevance of this topic for master’s level students, as the ability to comment on visual data is an essential component of academic research and written assignments. The study focuses on developing skills in interpreting data, presenting research findings, expressing evaluative judgments, and adhering to structural and stylistic conventions of academic texts.
The theoretical foundation is based on the works of prominent scholars in academic writing, such as Tetyana Yakhontova, John Swales, Christine Feak, as well as Oxford-based researchers Edward de Chazal and Julie Moore. In particular, the article examines a three-stage model of data commentary writing and introduces seven effective strategies for interpreting visual information implemented using the coursebook “Oxford EAP. Advanced/C1”.
The practical section includes an analysis of students’ written assignments, discussed through peer evaluation. The research identifies common flaws, linguistic features (including nominal constructions and evaluative vocabulary), and the extent to which the proposed strategies were effectively applied.
The article concludes with several recommendations for enhancing data commentary skills: expanding the analysis of high-quality academic texts, encouraging the use of technological tools (e.g., AI, Grammarly, ChatGPT, Gamma), and fostering feedback through collaboration with peers and instructors.
Future research directions may involve interdisciplinary comparisons of data commentary practices, the development of interactive digital tools for teaching data description, the influence of students’ native languages on rhetorical structures, and pedagogical strategies for integrating critical thinking with quantitative data analysis.