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Quantifying OER recommendations

Librarians can help faculty work with OERs on campus. Kingsborough librarians can consult with faculty from other departments, offering advice on aspects of OERs such as repositories, peer review and licensing. However, in addition to qualitative assessments, the Kingsborough librarians wanted to offer some quantitative evidence to back our recommendations of specific OER repositories.

The result was the following project:

We decided to create a Python script that would analyze existing LibGuides about OERs. LibGuides are a means by which librarians share resources on specific topics. Many LibGuides about OERs already exist at colleges around the world. We wanted to harness the collective wisdom of these LibGuides by aggregating their recommendations. So here’s what we did:

  • Step 1: We pulled the OER recommendation lists from the top 50 OER LibGuides, as determined by Google. We saved these as html documents.
  • Step 2: We wrote a script that would scrape through all of these html documents for hyperlinks. We harvested a total of 1289 links.
  • Step 3: The script aggregated all of the links to produce a list of the most cited OER repositories.
  • Step 4: We posted a Top-20 list to our own OER LibGuide.

The benefit of this approach is that it relies upon the collective wisdom of librarians, who have put a lot of thought into the OER resources they’ve recommended. By aggregating and quantifying this information, we’ve harnessed a great deal of existing knowledge about OERs. Our Top 20 list is our way of bringing this collective knowledge back to the community.


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