Analytic techniques for automated analysis of writing

Shibani, A

Analytic techniques for automated analysis of writing

Shibani, A

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Publisher:: Springer Nature
Publication Type:: Chapter
Citation:: Digital Writing Technologies in Higher Education: Theory, Research, and Practice, 2023, pp. 317-331
Issue Date:: 2023-09-14

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Field	Value	Language
dc.contributor.author	Shibani, A
dc.date.accessioned	2025-01-27T05:30:07Z
dc.date.available	2025-01-27T05:30:07Z
dc.date.issued	2023-09-14
dc.identifier.citation	Digital Writing Technologies in Higher Education: Theory, Research, and Practice, 2023, pp. 317-331
dc.identifier.isbn	9783031360329
dc.identifier.uri	http://hdl.handle.net/10453/184218
dc.description.abstract	Analysis of academic writing has long been of interest for pedagogical and research purposes. This involves the study of students' writing products and processes, often enabled by time-consuming manual analysis in the past. With the advent of new tools and analytic techniques, analysis and assessment of writing has become much more time and resource efficient. Advances in machine learning and artificial intelligence also provide distinct capabilities in supporting students' cognitive writing processes. This chapter will review analytical approaches that support the automated analysis of writing and introduce a taxonomy, from low-level linguistic indices to high-level categories predicted from machine learning. A list of approaches including linguistic metrics, semantic and topic-based analysis, dictionary-based approaches, natural language processing patterns, machine learning, and visualizations will be discussed, along with examples of tools supporting their analyses. The chapter further expands on the evaluation of such tools and links above analysis to implications on writing research and practice including how it alters the dynamics of digital writing.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Digital Writing Technologies in Higher Education: Theory, Research, and Practice
dc.relation.isbasedon	10.1007/978-3-031-36033-6_20
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Analytic techniques for automated analysis of writing
dc.type	Chapter
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Provost
pubs.organisational-group	University of Technology Sydney/Provost/TD School
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/The Trustworthy Digital Society
utslib.copyright.status	open_access	*
dc.date.updated	2025-01-27T05:30:00Z
pubs.publication-status	Published

Abstract:

Analysis of academic writing has long been of interest for pedagogical and research purposes. This involves the study of students' writing products and processes, often enabled by time-consuming manual analysis in the past. With the advent of new tools and analytic techniques, analysis and assessment of writing has become much more time and resource efficient. Advances in machine learning and artificial intelligence also provide distinct capabilities in supporting students' cognitive writing processes. This chapter will review analytical approaches that support the automated analysis of writing and introduce a taxonomy, from low-level linguistic indices to high-level categories predicted from machine learning. A list of approaches including linguistic metrics, semantic and topic-based analysis, dictionary-based approaches, natural language processing patterns, machine learning, and visualizations will be discussed, along with examples of tools supporting their analyses. The chapter further expands on the evaluation of such tools and links above analysis to implications on writing research and practice including how it alters the dynamics of digital writing.

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http://hdl.handle.net/10453/184218