Syllabi Information Literacy Miner

Syllabi Information Literacy Miner
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Hosted on Google Colab<br>
<a href = "https://colab.research.google.com/drive/1N778ot87GI-wJSQHpjRJWkplL4NL5HWb?usp=sharing">https://colab.research.google.com/drive/1N778ot87GI-wJSQHpjRJWkplL4NL5HWb?usp=sharing</a>
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Purpose: Automatically mine academic syllabi for information literacy (IL) components in order to identify opportunities for liaison librarians to engage with courses. While this Jupyter Notebook can be used as a standalone tool, Baylor University Libraries also maintains a Power BI report that also identifies which courses liaison libraries are already providing instruction. This allows liaison librarians to to identify new IL opportunities with some measure of precision.
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Overview of Jupyter Notebook Procedures:
<ol>
<li>Load syllabi by uploading files or providing URL to .zip archive
<li>Convert syllabi to text format (using <a href="https://textract.readthedocs.io/en/stable/">textract</a>)
<li>IL components are identified by finding verb fragments with the presence of nouns within the specified number of context words
<li>The types of IL learning is then identified based on the verb. Types include Library Basics, Research Basics, Research in the Disciplines.
</ol>
Outputs:
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<li>Pie chart showing proportion of the three IL learning types
<li>Column chart showing counts of IL components (verbs with context nouns)
<li>Table showing the types of learning identified for each syllabi
<li>Table showing each granular IL component for each syllabi. This table is also automatically downloaded as an Excel spreadsheet.
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Permissions (Copyright 2021 Baylor University Libraries)
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<li>Use: Licensed under the MIT License - https://opensource.org/licenses/MIT.
<li>Citation: Publications and research reports should include the following citation:
Joshua Been, Amy James, and Beth Farwell. Syllabi Information Literacy Miner. Waco, TX: Baylor University Libraries. 2021.