Overview#
Data permeates nearly every corner of the research landscape. Whether you are a social scientist exploring the impacts of zoning policies on urban demographics, or a physical scientist predicting the effects of climate change on agricultural productivity, you will almost certainly need to develop skills in accessing, processing, visualizing and performing statistical analysis on datasets.
New technologies have enabled us to have access to ever-increasing volumes of data. At first we usually think, the more data the better! But very quickly we run into challenges. Often a dataset will be too large for us to download and manage it on our local laptop, and we find ourselves trying to subset the data, work with it in the cloud, or find someone else who has a bigger hard drive than yours to download the data. Other times there may be new approaches such as machine learning that would advance our science, but we do not have access to example workflows for applying the tools to our datasets. Even for the most technically savvy of us, keeping up with the dizzying expansion of tools, data formats and technologies is nearly impossible.
So, how do we learn these tools? If we’re already enrolled in a formal education system, we may be lucky enough to find good courses that keep pace with the shifting data science landscape. If not, we typically try to learn from what is on the internet, or we seek out a training workshop.
Emergence of the Hackweek#
Back in 2014, there were very few formal educational pathways to learn data science. So, a group of astronomers designed a workshop combining tutorials and open project work, and called it a “hackweek”. They invited members of their own communities to create learning content, and made sure there was ample time for people to work on projects to gain practical experience. Nearly 10 years after the formation of AstroHackweek, this model has been applied across multiple disciplines in over 30 events. Hackweeks have helped us to teach people new skills, create community software and build collaborative networks.
Fast Forward#
Today the educational landscape has changed. Many institutions now have their own data science programs, and software is being taught as a component of many discipline-specific offerings. As we continue to reimagine the role of hackweeks, it is useful to explicitly distinguish between these different pathways of learning and community development.
We’ll do this by adopting terminology from a recent publication that compares Formal Higher Education with Short-Format Training [11]. Formal Higher Education includes those courses now offered in many universities and colleges. These are typically traditional classroom lectures and may involve some kind of hands-on or lab exercise component. Student performance is evaluated through grading, and everyone follows along a standardized curriculum. Teaching generally requires pedagogical rigor, course evaluation and continuity of curriculum.
In contrast, Short Format Training refers to workshops, summer schools, boot camps or any other ad hoc gatherings aimed at teaching to a specific set of topics. These events are time-bounded and typically of much shorter duration than a university course. The training is often conducted by volunteers who create training modules and may or may not have experience in pedagogical best practices. These events rarely involve any form of evaluation or program improvement, and people hosting and teaching the events usually do not get formally recognized for the work. We see hackweeks as a special category of Short Format Training, one that is highly adaptive to the emerging needs of a particular community.
Benefits of Short-Format Training#
If Formal Higher Education is finally catching up with the learning needs of our communities, do we still need Short Format Training and hackweeks? We believe the answer is yes! Here are some reasons why:
Many Formal Higher Education institutions still lack the funding to create data science programs, or to infuse software development into existing classes. Short Format Training can help fill the gap, especially for under-resourced communities.
The flexibility of Short Format Training enables us to continually adapt to emerging research needs at a pace that may not be possible in a Formal Higher Education setting.
Short Format Training provides unique opportunities for networking and community building.
The absence of learner evaluation requirements in Short Format Trainings frees participants to pursue creative work that might not otherwise occur.
The Best of Both Worlds?#
A critique of Short Format Training is that they lack rigor and that there is limited evidence that they produce positive learning outcomes. We think a big part of this relates to the fact that there have been few studies of their efficacy! However, we recognize that Short Format Training events are often organized with limited funding support and rely largely on the goodwill of a dedicated team of organizers. In such an environment, it is not always realistic to expect the level of consistency and rigor that is possible within a larger Formal Educational structure.
But, what if we could find ways to injected some of the rigor of Formal Higher Education into our hackweeks, while maintaining our need for flexibility and creativity? In a nutshell, this is the goal of the training and resource materials in this website!
Professionalizing Short-Format Training#
The modules in this website are designed to prepare organizers of the University of Washington eScience Institute’s Hackweek program. We hope to share professional development tools for you to improve your capacity to teach, share ideas, lead project work and advocate for open and reproducible science. We hope the skills acquired here are applicable to other related training you participate in, including those in a Formal Higher Education setting.
Our first two modules focus on improving our capacity to train hackweek participants to learn new tools for their research. We’ll begin by exploring how to choose appropriate learning content, which refers to the topics that we choose to present to learners and the depth and breadth of coverage of that content. We will also consider pedagogy, which refers to how we teach material, whether in a lecture, discussion- or project-based format, as well as the methods we employ to interact with learners. In our Tutorial Design and Project Design modules we will teach you about designing hackweek learning content, and our recommended pedagogical approaches for hackweek particiapants, within the existing framework of our tutorial and project sessions.
Next we will focus on Technology where we will teach you about the tools we use to create centralized, web-browser accessible learning materials. You will learn to use automated workflows in GitHub to generate consistent and quality controlled Jupyter Notebooks, and we will share resources for hosting sample datasets.
In our Strategy and Planning module, we will teach you about the tools we use to manage our time and set clear expectations for the various tasks of the organizing team. By structuring the way we work together we can better honor the limited time each of us has to contribute, and ensure that we still meet our broad goals for the event.
Finally, in our module on Learning Culture, we will provide opportunities for all of us to practice ways to support a vibrant and healthy learning environment. We will practice skills in building empathy, listening and navigating complex group dynamics.
Our Intention for Hackweek Participants#
The overall intention of our work here is to support the learning and development of hackweek participants. We believe each participant of our hackweeks should have an opportunity to:
feel like their contributions are valued
meet new people outside of their existing circles
feel empowered to challenge themselves, make mistakes and have fun
learn something new that they came to get from the hackweek