LA Practice A2: Identify patterns in texts by abstracting textual data (Activity)
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In this project, the learner will collect, transform, and analyze data from Romeo & Juliet to think computationally about a text and to explore how numerical data affects the interpretation of literary work. (60-90 minutes)
Adapted from the lesson Plotting Plots from Tom Liam Lynch, on behalf of Mosaic Education Consulting Group, for CS4All.
Learning Objectives
By the end of this activity, learners will be able to...
- Examine text as unstructured and structured data to collect and organize textual data: Practice A2.1
- Make use of textual data to create visuals (tables, charts. graphs etc.) from text analysis data (i.e., word frequencies, syllable counts, meter) to identify patterns that could not be seen otherwise: Practice A2.2
- Analyze text by tracing patterns in usage and interactions of specific words in quantitative textual data and visualizations. Practice A2.3
- Using quantitative textual data and visualizations to identify patterns in a text (i.e. phrases, word choices, topics) that shape meaning or tone, re-read and analyze a text more closely. Practice A3.2
- Compose a mixed analysis that presents quantitative and qualitative textual data to support an interpretation of a text. Practice A3.5
Vocabulary
- Patterns: Repeating similarities among objects, ideas, and problems
- Data: Quantitative or qualitative information (e.g., text, symbols, etc.) collected for analysis
- Pattern Recognition: Finding patterns among decomposed problems that can help us solve more complex problems
- Pattern generalization: Process of creating models, rules, or theories of observed patterns to test predicated outcomes
Assessment
Creative Commons & Attributions
This project is adapted from the lesson Plotting Plots from Tom Liam Lynch, on behalf of on behalf of Mosaic Education Consulting Group, for CS4All which is licensed under under a Attribution-NonCommercial-ShareAlike 2.0 Generic (CC BY-NC-SA 2.0)