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Collaborative Lesson Development: teaching data on the web at MozFest 2014

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Aleksandra Pawlik

Aleksandra Pawlik

SSI fellow

Posted on 7 November 2014

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Collaborative Lesson Development: teaching data on the web at MozFest 2014

Posted by a.pawlik on 7 November 2014 - 5:30pm

By Aleksandra Pawlik, Training Leader.

After 2013's success of running the session on "What makes good code..." the Institute helped in running another session at this year's Mozilla Festival. The focus of the session was on "designing open lessons on Teaching Data on the Web by filling in gaps between current workshop offerings, and building domain-specific content."

The session was originally proposed by Data Carpentry with Karthik Ram (ROpenSci) as the main facilitator. Karthik was supported by Robert Davey (The Genome Analysis Centre), Milena Marin (School of Data), Billy Meinke (Creative Commons) and myself. The objectives of the sessions were not only to create and improve the existing Data Carpentry materials for teaching data on the web but also to familiarize the participants with the process of collaborative work on these materials.

The ideas for the lessons came from both the facilitators and the participants. Via the GitHub repository prepared for the session, Karthik suggested compiling datasets available openly on the web useful for teaching data handling in different disciplines or updating the R intermediate lessons at Software Carpentry. Stephen Turner proposed a very short introduction to R workshops for life scientists. I suggested some cleanups on the existing spreadsheet lessons and starting work on developing lessons on Open Refine with real biological datasets.

The latter was inspired by a session run the day before by Milena Marin and Yuandra Ismiraldi from the School of Data. Milena's session, Dealing with messy data, focused on learning how to do this with various tools in a collaborative skills workshop. One of the participants, Tina Götschi, proposed designing a short module on accessing open datasets via APIs returning JSON data that can then be used in JavaScript programs.

Eventually three topics were picked up by the participants: updating the R intermediate lessons, cleaning up the spreadsheet material and programmatically accessing open datasets. At the end of the session, several participants said one of the useful outcomes was to learn how to collaboratively develop teaching material.

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