Text analysis projects look for, analyze, and sometimes visualize patterns across bodies of textual data.
Learning objectives are clearly defined statements of expected goals and outcomes from the student perspective. When a student finishes an activity or a lesson, what will they know, articulate, or be able to do?
Every digital pedagogy project should have learning objectives. Here are a few tips for creating student-centered objectives:
Getting started: try Bloom's Taxonomy Action Verbs for sample action verbs to use in learning objectives.
Students will be able to...
- compare language use across a poet’s entire oeuvre to determine thematic shifts in the poet’s writing over time.
- work with large bodies of text to choose an effective corpus, choose stop words, recognize potential for bias and error, and decide on a method of analysis.
Voyant - a web-based, user-friendly tool for analyzing text.
Poemage - an open-source text visualization application for close reading.
Hathitrust Research Center - a set of software tools that allow for text mining and computational analysis of Hathitrust texts.
Project Gutenburg - a free collection of digital texts that can be downloaded and analyzed.
Python - Python is a free programming language that can manage large amounts of data and has packages built for text analysis.
See the Research Hub events calendar for upcoming workshops on Python.
Icon "text" by ProSymbols from the Noun Project
To get started with digital pedagogy and lesson planning after exploring this guide, contact Sarah Morris (semorris@email.unc.edu or (919) 962-2094).
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