About the Information Ecosystems Cookbook
Contents of this Page:
- Where did this resource come from and what is it for?
- Why “Cookbook?”
- How do I use this resource?
- How might I go about making a similar resource?
Where did this resource come from and what is it for?
The Information Ecosystems Project at the University of Pittsburgh grew out of a 2018-2019 Mellon Sawyer Seminar entitled, “Information Ecosystems: Creating Data (and Absence) from the Quantitative to the Digital Age.” This original year-long seminar gathered together just over thirty participants from the Pittsburgh region to share our knowledge and use our conversation to gain a much deeper understanding of the social and political life of data in the twenty-first century.
This Information Ecosystems Cookbook responds to one of the clear pedagogical needs that the seminar revealed: introductory, easily-available, modular, training resources to help learners acquire basic, practical skills to use the tools needed to responsibly store, arrange, manipulate and deploy the data that documents the human experience.
We chose to publish these modules as Open Educational Resources in the hopes that they can be used as widely as possible, and we have aimed to convey information at the late-high-school, early-undergraduate level. That said, we have not assumed that our audience is actively in high school or college! We very much hope that anyone who wishes to engage with these tools and techniques at the level of an advanced beginner will find this resource useful.
In their article, “Task-Driven Programming Pedagogy in the Digital Humanities” (2017), Alison Langmead (a co-editor of this resource) and her colleague, David Birnbaum, argued that a very effective technical pedagogy for learners who wish to put their new knowledge into direct use is a task-based, proficiency-oriented one. They noted that there is already a long-standing model for this type of technical resource in the computer programming community: the “cookbook.” Such resources have been around at least since the time of COBOL (1978). A more recent example, the C++ Cookbook, first published around 2005, offers the tagline, “Solutions and Examples for C++ Programmers,” which does a good job of gesturing towards its intended practical goals and its aims for utility and a problem-solving level.
This is to say, such cookbooks do not focus on teaching technical subjects by starting from the basics, as if starting with the nouns and verbs of a human-spoken language. instead they offer a list of common problems that a beginning (or advanced) programmer might be having, and then offer solutions as well as discussions of those solutions.
This approach was the model for the Information Ecosystems Cookbook. We wished to offer task-based, proficiency-oriented resources that will allow advanced beginners to master the skills of organizing, arranging, manipulating and deploying data.
How do I use this resource?
There are two main ways that we feel you can best approach the modules in this cookbook. The first, and the one we recommend for beginners, are our “Curricular Pathways.” Each module in this cookbook has four main sections (described below), a pattern modeled on instructional techniques found in books such as the excellent Small Teaching Online. This pedagogical style works best when a facilitator guides the learners through the sections, and we hope that the Curricular Pathways serve as an asynchronous form of facilitation. They offer a small collection of modules–a short curriculum, if you will–under a named theme such as, “Data in Context,” or “Producing and Using Data Sets.” If you find yourself wishing to learn more about a particular one of these curricular themes, you can begin with any one of the modules listed and complete them in the order that seems right for you.
That said, it is not mission-critical that you follow the Curricular Pathways. We attempted to create modules that will also allow for individual, asynchronous engagement, so you may feel free to head over to the alphabetized list of modules and begin wherever you like!
Either way you choose to engage, it is worth knowing that each module–as mentioned above–is made up of four consistent sections: Watch, Do, Explore, and then Guiding Realizations. The “Watch” section is a video in which an expert guides the learner through a task that exemplifies the module’s topic. The “Do” section then offers a hands-on exercise that the learner can do semi-independently once they watched the video by following along closely with the written instructions provided. Once completed, the learner will find use in the “Explore” section which offers yet more resources that allow the learner to think more broadly about the topic’s theme. Finally, there are the “Guiding Realizations” which constitute a list of the the fundamental ideas that the contributor had in mind when designing the module. These can then be used by the learner to perform a self-check on their understanding, and also to serve as a way to connect ideas across the modules.
How might I go about making a similar resource?
In the spirit of fostering participation in open-educational resources, we offer some guidance on producing your own modules in our Info Eco DIY Guide. This guide is based on our process and experiences throughout our original development process. It outlines our approach for planning and developing an Info Eco Cookbook style learning module.
Information Ecosystems Cookbook Team
Alison Langmead, Project Lead and Co-Editor
Jane Thaler, Project Coordinator and Co-Editor
Elise Silva, Implementation Lead and Co-Editor
|Zhuoru Deng, New York University||Discourse Analysis|
|Michael Dietrich, University of Pittsburgh||Bibliometrics|
|Bob Gradeck, Western Pennsylvania Regional Data Center||Dataset Summaries|
|Karl Grossner, University of Pittsburgh||Linked GIS Data|
|Susan Grunewald, University of Pittsburgh||Linked GIS Data|
|Mario Khreiche, New York University||Discourse Analysis|
|Alison Langmead, University of Pittsburgh||Databending|
|Michael Madison, University of Pittsburgh||Data Governance|
|Ruth Mostern, University of Pittsburgh||Linked GIS Data|
|Liz Monk, Western Pennsylvania Regional Data Center||Dataset Summaries|
|Elise Silva, University of Pittsburgh||Information Discovery|
|Alexandra Straub, University of Pittsburgh||Linked GIS Data|
|Jane Thaler, University of Pittsburgh||Data Representation|
This work is licensed under a Creative Commons Attribution 4.0 International License.