Community Data Science Collective

January 30, 2017January 30, 2017

Supporting children in doing data science

As children use digital media to learn and socialize, others are collecting and analyzing data about these activities. In school and at play, these children find that they are the subjects of data science. As believers in the power of data analysis, we believe that this approach falls short of data science’s potential to promote innovation, learning, and power.

Motivated by this fact, we have been working over the last three years as part of a team at the MIT Media Lab and the University of Washington to design and build a system that attempts to support an alternative vision: children as data scientists. The system we have built is described in a new paper—Scratch Community Blocks: Supporting Children as Data Scientists—that will be published in the proceedings of CHI 2017.

Our system is built on top of Scratch, a visual, block-based programming language designed for children and youth. Scratch is also an online community with over 15 million registered members who share their Scratch projects, remix each others’ work, have conversations, provide feedback, bookmark or “love” projects they like, follow other users, and more. Over the last decade, researchers—including us—have used the Scratch online community’s database to study the youth using Scratch. With Scratch Community Blocks, we attempt to put the power to programmatically analyze these data into the hands of the users themselves.

To do so, our new system adds a set of new programming primitives (blocks) to Scratch so that users can access public data from the Scratch website from inside Scratch. Blocks in the new system gives users access to project and user metadata, information about social interaction, and data about what types of code are used in projects. The full palette of blocks to access different categories of data is shown below.

The new blocks allow users to programmatically access, filter, and analyze data about their own participation in the community. For example, with the simple script below, we can find whether we have followers in Scratch who report themselves to be from Spain, and what their usernames are.

Simple demonstration of Scratch Community Blocks

In designing the system, we had two primary motivations. First, we wanted to support avenues through which children can engage in curiosity-driven, creative explorations of public Scratch data. Second, we wanted to foster self-reflection with data. As children looked back upon their own participation and coding activity in Scratch through the project they and their peers made, we wanted them to reflect on their own behavior and learning in ways that shaped their future behavior and promoted exploration.

After designing and building the system over 2014 and 2015, we invited a group of active Scratch users to beta test the system in early 2016. Over four months, 700 users created more than 1,600 projects. The diversity and depth of users creativity with the new blocks surprised us. Children created projects that gave the viewer of the project a personalized doughnut-chart visualization of their coding vocabulary on Scratch, rendered the viewer’s number of followers as scoops of ice-cream on a cone, attempted to find whether “love-its” for projects are more common on Scratch than “favorites”, and told users how “talkative” they were by counting the cumulative string-length of project titles and descriptions.

We found that children, rather than making canonical visualizations such as pie-charts or bar-graphs, frequently made information representations that spoke to their own identities and aesthetic sensibilities. A 13-year-old girl had made a virtual doll dress-up game where the player’s ability to buy virtual clothes and accessories for the doll was determined by the level of their activity in the Scratch community. When we asked about her motivation for making such a project, she said:

I was trying to think of something that somebody hadn’t done yet, and I didn’t see that. And also I really like to do art on Scratch and that was a good opportunity to use that and mix the two [art and data] together.

We also found at least some evidence that the system supported self-reflection with data. For example, after seeing a project that showed its viewers a visualization of their past coding vocabulary, a 15-year-old realized that he does not do much programming with the pen-related primitives in Scratch, and wrote in a comment, “epic! looks like we need to use more pen blocks. :D.”

Additionally, we noted that that as children made and interacted with projects made with Scratch Community Blocks, they started to critically think about the implications of data collection and analysis. These conversations are the subject of another paper (also being published in CHI 2017).

In a 1971 article called “Teaching Children to be Mathematicians vs. Teaching About Mathematics”, Seymour Papert argued for the need for children doing mathematics vs. learning about it. He showed how Logo, the programming language he was developing at that time with his colleagues, could offer children a space to use and engage with mathematical ideas in creative and personally motivated ways. This, he argued, enabled children to go beyond knowing about mathematics to “doing” mathematics, as a mathematician would.

Scratch Community Blocks has not yet been launched for all Scratch users and has several important limitations we discuss in the paper. That said, we feel that the projects created by children in our the beta test demonstrate the real potential for children to do data science, and not just know about it, provide data for it, and to have their behavior nudged and shaped by it.

This blog-post and the work that it describes is a collaborative project between Sayamindu Dasgupta and Benjamin Mako Hill. We have also received support and feedback from members of the Scratch team at MIT (especially Mitch Resnick and Natalie Rusk), as well as from Hal Abelson from MIT CSAIL. Financial support came from the US National Science Foundation. We will be presenting this paper at CHI in May, and will be thrilled to talk more about our work and about future directions.

July 4, 2016

Studying the relationship between remixing & learning

With more than 10 million users, the Scratch online community is the largest online community where kids learn to program. Since it was created, a central goal of the community has been to promote “remixing” — the reworking and recombination of existing creative artifacts. As the video above shows, remixing programming projects in the current web-based version of Scratch is as easy is as clicking on the “see inside” button in a project web-page, and then clicking on the “remix” button in the web-based code editor. Today, close to 30% of projects on Scratch are remixes.

Remixing plays such a central role in Scratch because its designers believed that remixing can play an important role in learning. After all, Scratch was designed first and foremost as a learning community with its roots in the Constructionist framework developed at MIT by Seymour Papert and his colleagues. The design of the Scratch online community was inspired by Papert’s vision of a learning community similar to Brazilian Samba schools (Henry Jenkins writes about his experience of Samba schools in the context of Papert’s vision here), and a comment Marvin Minsky made in 1984:

Adults worry a lot these days. Especially, they worry about how to make other people learn more about computers. They want to make us all “computer-literate.” Literacy means both reading and writing, but most books and courses about computers only tell you about writing programs. Worse, they only tell about commands and instructions and programming-language grammar rules. They hardly ever give examples. But real languages are more than words and grammar rules. There’s also literature – what people use the language for. No one ever learns a language from being told its grammar rules. We always start with stories about things that interest us.

In a new paper — titled “Remixing as a pathway to Computational Thinking” — that was recently published at the ACM Conference on Computer Supported Collaborative Work and Social Computing (CSCW) conference, we used a series of quantitative measures of online behavior to try to uncover evidence that might support the theory that remixing in Scratch is positively associated with learning.

Of course, because Scratch is an informal environment with no set path for users, no lesson plan, and no quizzes, measuring learning is an open problem. In our study, we built on two different approaches to measure learning in Scratch. The first approach considers the number of distinct types of programming blocks available in Scratch that a user has used over her lifetime in Scratch (there are 120 in total) — something that can be thought of as a block repertoire or vocabulary. This measure has been used to model informal learning in Scratch in an earlier study. Using this approach, we hypothesized that users who remix more will have a faster rate of growth for their code vocabulary.

Controlling for a number of factors (e.g. age of user, the general level of activity) we found evidence of a small, but positive relationship between the number of remixes a user has shared and her block vocabulary as measured by the unique blocks she used in her non-remix projects. Intriguingly, we also found a strong association between the number of downloads by a user and her vocabulary growth. One interpretation is that this learning might also be associated with less active forms of appropriation, like the process of reading source code described by Minksy.

The second approach we used considered specific concepts in programming, such as loops, or event-handling. To measure this, we utilized a mapping of Scratch blocks to key programming concepts found in this paper by Karen Brennan and Mitchel Resnick. For example, in the image below are all the Scratch blocks mapped to the concept of “loop”.

We looked at six concepts in total (conditionals, data, events, loops, operators, and parallelism). In each case, we hypothesized that if someone has had never used a given concept before, they would be more likely to use that concept after encountering it while remixing an existing project.

Using this second approach, we found that users who had never used a concept were more likely to do so if they had been exposed to the concept through remixing. Although some concepts were more widely used than others, we found a positive relationship between concept use and exposure through remixing for each of the six concepts. We found that this relationship was true even if we ignored obvious examples of cutting and pasting of blocks of code. In all of these models, we found what we believe is evidence of learning through remixing.

Of course, there are many limitations in this work. What we found are all positive correlations — we do not know if these relationships are causal. Moreover, our measures do not really tell us whether someone has “understood” the usage of a given block or programming concept.However, even with these limitations, we are excited by the results of our work, and we plan to build on what we have. Our next steps include developing and utilizing better measures of learning, as well as looking at other methods of appropriation like viewing the source code of a project.

This blog post and the paper it describes are collaborative work with Sayamindu Dasgupta, Andrés Monroy-Hernández, and William Hale. The paper is released as open access so anyone can read the entire paper here. This blog post was also posted on Benjamin Mako Hill’s blog, on Sayamindu Dasgupta’s blog and on Medium by the MIT Media Lab.

April 15, 2016April 15, 2016

Jackie Robinson Day (Re-estimated)

When major league baseball held its opening day on April 15 in 1947, a 28 year-old infielder made his highly-anticipated debut at first base for the Brooklyn Dodgers. He would go on to record an extraordinary season and career worthy of induction into the Baseball Hall of Fame, winning Rookie of the Year honors in 1947, a batting title and Most Valuable Player award in 1949, and a World Series title in 1955. He also produced two seasons that rank among the top 100 ever (by the metric of Wins-Above-Replacement among position players).

Jackie Robinson (1954 public domain photo by Bob Sandberg for Look Magazine).

Looking at the box score, Jackie Robinson didn’t make an overwhelming impact on the outcome of his first game, but his presence on the field challenged the racist status quo of professional baseball and American society. What’s more, the intense public-ness of the challenge made Robinson’s presence a symbol and a spectacle: of the roughly 26,500 spectators in attendance at Ebbets field, an estimated 14,000 were black. I cannot imagine what it was like to be at that game — one of those rare places and moments where it becomes possible to see an historic social transformation as it unfolds. Just the thought gives me goosebumps.

Every major league player, coach, and umpire will don Robinson’s iconic number 42 in recognition today. Watching games and highlights from Jackie Robinson Days past, I’ve been troubled by how easily such observances drift into a hagiographic reverie that sometimes even take on a self-congratulatory tone. Stories of Robinson’s incredible athletic and personal accomplishments sometimes efface his struggle against horrible, violent, and aggressive responses. Worse yet, the stories usually play down the persistence of racism and its effects today. Baseball celebrates Jackie Robinson Day out of a strange combination of guilt and pride; knowledge and ignorance; resistance and complicity.

As I indicated earlier, Robinson’s performance and impact qualified him for the Hall of Fame along multiple dimensions. However, another way to think about his unique contribution to baseball is to consider how such virulent racism likely affected his play and how unbelievably, mind-blowingly great a player he might have been under less racist conditions.

There’s no obviously valid way to construct a counterfactual Jackie Robinson, but research on the phenomenon of stereotype threat suggests a very simple, naive statistical adjustment strategy. To paraphrase a bunch of scholarly studies and the (pretty extensive) Wikipedia article, stereotype threat reduces the performance of individuals who belong to negatively stereotyped groups, largely by inducing feelings of anxiety.

Stereotype threat affects various kinds of behaviors including athletic achievement. A 1999 study by Jeff Stone and colleagues (pdf) estimates the effects of some typical forms of stereotype threat on a sample of black men’s athletic performance, reporting that race-based priming resulted in a 23.5% worse outcome on a miniature golf (!) task than a control condition with no priming.

Consider that the priming in this Stone et al. study was done in a fairly polite, impersonal, non-hateful, non-threatening way in relation to a mini-golf task with absolutely nothing at stake. Consider just how personal, vitriolic, and violent the responses to Jackie Robinson were — many of them coming directly from opposing players and “fans” who went to great pains to heckle him in the middle of at-bats, physically target him with violent slides and more on the field, or issue death threats to him and his family. Consider how much Robinson had at stake and just how public his successes and his failures would have been.

Some people may like to imagine (and filmmakers may like to depict) that the hatred helped to motivate and focus Robinson, spurring him to even greater performance. Similarly, part of the mystique of the greatest athletes is that they seem to empty their heads of all the noise and distractions that would debilitate the rest of us at precisely those moments when the stakes and pressures are highest. It’s easy to say that Robinson didn’t respond to the pressure in the same way as most humans would, but the research on stereotype threat suggests that it probably affected him on the field anyway. Just being reminded — even in very subtle, socially-coded ways — that you belong to a socially excluded group reduced athletic performance by nearly a quarter. The sort of cognitive burden that comes along with being singled out and targeted by the kind of racial hatred that Robinson experienced must be orders of magnitude greater. What sort of impact would this burden have had on Robinson’s play?

Now, go look at the stat lines again from those two spectacular seasons (1949, WAR 9.6, and 1951, WAR 9.7) that Robinson had and imagine them without the stress, the pain, the distraction of all that hate. Be a little bit generous and inflate the WAR statistics by the same 23.5% that Stone et al.’s subjects performance dropped in a laboratory study in ridiculously low-key conditions. Under these assumptions, Robinson’s two greatest seasons might have yielded WAR of 11.9 and 12.0 respectively — easily placing them both among the top 10 seasons by a position player ever.

April 7, 2016April 6, 2016

Dogfooding

This quarter, I am teaching a graduate seminar called “The Practice of Scholarship” that is required for second-year students in the Northwestern MTS and TSB programs. Following Mako’s lead, I am using the Community Data Science Collective wiki to host the (editable) syllabus. In other words, I am eating to my heart’s content.

We had our first class session yesterday and it went really well. The goal for the quarter is for every student to prepare a manuscript for submission to a peer reviewed venue. I told the students that the course will serve as a hybrid writing boot camp and extended group therapy session. There will be much workshopping and iteration and sharing of feelings. There will also be polite, friendly, and unyielding pressure to produce scholarly work of exceptional quality.

In keeping with the wikified ethos, much of the course schedule remains tbd at this point, so please drop me a line with comments, suggestions, or pointers to great readings that brilliant, interdisciplinary, empirical social scientists and HCI researchers like my students would appreciate.

April 6, 2016

Back in the blogging game

Hello world! It’s been a while since I’ve done any blogging, but I’ve been wanting to return for some time now, so here we are. My old blog was a hodge podge that hovered at the edges of my research. Current events featured prominently, especially those having to do with governance in online communities, knowledge production and access, and research ideas. I have a few different goals for this blog.

Sunrise-LakeMichigan-2014-10-8 — A new day dawns for blogging on the shores of Lake Michigan…

First, since it’s part of the Community Data Science Collective site, I plan to talk about our research, affiliates, community events, and related topics. Second, I want to use the blog as a space to sketch out research ideas more regularly. When I blogged previously, I was a graduate student. I had more unstructured time in which to brainstorm and reflect. The transition to faculty and the subsequent accumulation of responsibilities, projects, students, and commitments has left me seeking time to think broadly and with less structure. I need a semi-structured space and time to do so. As a result, I return to blogging.

This relates to a third goal: a minimum of one post per week. In the old days, Mako coordinated the Cambridge instance of Iron Blogger, a group blogging accountability project in which all the participants agreed to write one post per week or pay $5 into a common pot (that we then used to throw a party whenever it got big enough). The incentives sound misaligned, but the semi-public commitment, a deadline, and the nominal material cost of failure got a weekly post out of me roughly 90% of the time.

There is no iron blogger group in Chicago (yet?), but I’m going to recreate the structure with a little public accountability infrastructure with some friends. So far, Rachel and I have committed to posting weekly and tracking our posts. If others want to join, we can add further infrastructure as needed. No fines for now, but if I fail to post frequently between now and the end of the academic year, I’ll revisit.

Finally, since I do a lot more mentoring and teaching now than I used to, I imagine that these activities will occupy a fair amount of my attention as well. I feel more comfortable publishing material about my teaching now than when I first started at Northwestern. I am also realizing that my approach to teaching would lend itself really well to blogging as I am continually tinkering with the structure of my assignments, readings, evaluations, and lessons. A space to reflect on my experiences more actively and to solicit feedback from students and others seems like a helpful thing.

That’s it for this opening post. Thanks for reading.

March 31, 2015March 27, 2016

Community Data Science Workshops in Spring 2015

The Community Data Science Workshops are a series of project-based workshops being held at the University of Washington for anyone interested in learning how to use programming and data science tools to ask and answer questions about online communities like Wikipedia, Twitter, free and open source software, and civic media.

The workshops are for people with absolutely no previous programming experience and they bring together researchers and academics with participants and leaders in online communities. The workshops are run entirely by volunteers and are entirely free of charge for participants, generously sponsored by the UW Department of Communication and the eScience Institute. Participants from outside UW are encouraged to apply.

There will be a mandatory evening setup session 6:00-9:00pm on Friday April 10 and three workshops held from 9am-4pm on three Saturdays (April 11 and 25 and May 9). Each Saturday session will involve a period for lecture and technical demonstrations in the morning. This will be followed by a lunch graciously provided by the eSciences Institute at UW. The rest of the day will be followed by group work on programming and data science projects supported by more experienced mentors.

Setup and Programming Tutorial (April 10 evening) — Because we expect to hit the ground running on our first full day, we will meet to help participants get software installed and to work through a self-guided tutorial that will help ensure that everyone has the skills and vocabulary to start programming and learning when we meet the following morning.

Introduction to Programming (and April 11) — Programming is an essential tool for data science and is useful for solving many other problems. The goal of this session will be to introduce programming in the Python programming language. Each participant will leave having solved a real problem and will have built their first real programming project.

Importing Data from web APIs (April 25) — An important step in doing data science is collecting data. The goal of this session will be to teach participants how to get data from the public application programming interfaces (“APIs”) common to many social media and online communities. Although we will use the APIs provided by Wikipedia and Twitter in the session, the principles and techniques are common to many other online communities.

Data Analysis and Visualization (May 9) — The goal of data science is to use data to answer questions. In our final session, we will use the Python skills we learned in the first session and the datasets we’ve created in the second to ask and answer common questions about the activity and health of online communities. We will focus on learning how to generate visualizations, create summary statistics, and test hypotheses.

Our goal is that, after the three workshops, participants will be able to use data to produce numbers, hypothesis tests, tables, and graphical visualizations to answer questions like:

Are new contributors in Wikipedia this year sticking around longer or contributing more than people who joined last year?
Who are the most active or influential users of a particular Twitter hashtag?
Are people who join through a Wikipedia outreach event staying involved? How do they compare to people who decide to join the project outside of the event?

An earlier version of the workshops was run in Spring and Fall 2015 and the curriculum we used for both are online.

Sign up and Participate!

Participants! If you are interested in learning data science, please fill out our registration form here. The deadline to register is Friday April 3. We will let participants know if we have room for them by Monday April 6. Space is limited and will depend on how many mentors we can recruit for the sessions.

Interested in being a mentor? If you already have experience with Python, please consider helping out at the sessions as a mentor. Being a mentor will involve working with participants and talking them through the challenges they encounter in programming. No special preparation is required. And we’ll feed you! Because we want to keep a very high mentor-to-student ratio, recruiting more mentors means we can accept more participants. If you’re interested you can fill out this form or email makohill@uw.edu. Also, thank you, thank you, thank you!

About the Organizers

The workshops are being coordinated, organized by Benjamin Mako Hill , Dharma Dailey, Jonathan Morgan, Ben Lewis, and Tommy Guy and a long list of other volunteer mentors. The workshops have been designed with lots of help and inspiration from Shauna Gordon-McKeon and Asheesh Laroia of OpenHatch and lots of inspiration from the Boston Python Workshop.

These workshops are an all-volunteer effort. Fundamentally, we’re doing this because we’re programmers and data scientists who work in online communities and we really believe that the skills you’ll learn in these sessions are important and empowering tools.

The workshops are being supported by the UW Department of Communication and the eScience Institute.

If you have any questions or concerns, please contact Benjamin Mako Hill at makohill@uw.edu.

Photo from the Boston Python Workshop - a similar workshop run in Boston that has inspired and provided a template for the CDW. — Photo from the Boston Python Workshop – a similar workshop run in Boston that has inspired and provided a template for the CDSW.

December 29, 2014March 27, 2016

New Paper: Consider the Redirect

This post was originally published on Benjamin Mako Hill‘s blog Copyrighteous.

In wikis, redirects are special pages that silently take readers from the page they are visiting to another page. Although their presence is noted in tiny gray text (see the image below) most people use them all the time and never know they exist. Redirects exist to make linking between pages easier, they populate Wikipedia’s search autocomplete list, and are generally helpful in organizing information. In the English Wikipedia, redirects make up more than half of all article pages.

Over the years, I’ve spent some time contributing to to Redirects for Discussion (RfD). I think of RfD as like an ultra-low stakes version of Articles for Deletion where Wikipedians decide whether to delete or keep articles. If a redirect is deleted, viewers are taken to a search results page and almost nobody notices. That said, because redirects are almost never viewed directly, almost nobody notices if a redirect is kept either!

I’ve told people that if they want to understand the soul of a Wikipedian, they should spend time participating in RfD. When you understand why arguing about and working hard to come to consensus solutions for how Wikipedia should handle individual redirects is an enjoyable way to spend your spare time — where any outcome is invisible — you understand what it means to be a Wikipedian.

That said, wiki researchers rarely take redirects into account. For years, I’ve suspected that accounting for redirects was important for Wikipedia research and that several classes of findings were noisy or misleading because most people haven’t done so. As a result, I worked with my colleague Aaron Shaw at Northwestern earlier this year to build a longitudinal dataset of redirects that can capture the dynamic nature of redirects. Our work was published as a short paper at OpenSym several months ago.

It turns out, taking redirects into account correctly (especially if you are looking at activity over time) is tricky because redirects are stored as normal pages by MediaWiki except that they happen to start with special redirect text. Like other pages, redirects can be updated and changed over time are frequently are. As a result, taking redirects into account for any study that looks at activity over time requires looking at the text of every revision of every page.

Using our dataset, Aaron and I showed that the distribution of edits across pages in English Wikipedia (a relationships that is used in many research projects) looks pretty close to log normal when we remove redirects and very different when you don’t. After all, half of articles are really just redirects and, and because they are just redirects, these “articles” are almost never edited.

Another puzzling finding that’s been reported in a few places — and that I repeated myself several times — is that edits and views are surprisingly uncorrelated. I’ll write more about this later but the short version is that we found that a big chunk of this can, in fact, be explained by considering redirects.

We’ve published our code and data and the article itself is online because we paid the ACM’s open access fee to ransom the article.

For more details see the paper: Hill, Benjamin Mako, and Aaron Shaw. (2014) “Consider the Redirect: A Missing Dimension of Wikipedia Research.” In Proceedings of the 10th International Symposium on Open Collaboration (OpenSym 2014). ACM Press, 2014.

October 16, 2014March 27, 2016

Community Data Science Workshops in November 2014

The Community Data Science Workshops in November 2014 are a series of project-based workshops being held at the University of Washington for anyone interested in learning how to use programming and data science tools to ask and answer questions about online communities like Wikipedia, Twitter, free and open source software, and civic media.

There will be a mandatory evening setup session 6:30-9:30pm on Friday November 7 and three workshops held from 9am-4pm on three Saturdays in November (November 8, 15, and 22). Each Saturday session will involve a period for lecture and technical demonstrations in the morning. This will be followed by a lunch graciously provided by the eSciences Institute at UW. The rest of the day will be followed by group work on programming and data science projects supported by more experienced mentors.

Setup and Programming Tutorial (November 7 evening) — Because we expect to hit the ground running on our first full day, we will meet to help participants get software installed and to work through a self-guided tutorial that will help ensure that everyone has the skills and vocabulary to start programming and learning when we meet the following morning.

Introduction to Programming (and November 8) — Programming is an essential tool for data science and is useful for solving many other problems. The goal of this session will be to introduce programming in the Python programming language. Each participant will leave having solved a real problem and will have built their first real programming project.

Importing Data from Wikipedia and Twitter APIs (November 15) — An important step in doing data science is collecting data. The goal of this session will be to teach participants how to get data from the public application programming interfaces (“APIs”) common to many social media and online communities. Although we will use the APIs provided by Wikipedia and Twitter in the session, the principles and techniques are common to many other online communities.

Data Analysis and Visualization (November 22) — The goal of data science is to use data to answer questions. In our final session, we will use the Python skills we learned in the first session and the datasets we’ve created in the second to ask and answer common questions about the activity and health of online communities. We will focus on learning how to generate visualizations, create summary statistics, and test hypotheses.

Our goal is that, after the three workshops, participants will be able to use data to produce numbers, hypothesis tests, tables, and graphical visualizations to answer questions like:

Are new contributors in Wikipedia this year sticking around longer or contributing more than people who joined last year?
Who are the most active or influential users of a particular Twitter hashtag?
Are people who join through a Wikipedia outreach event staying involved? How do they compare to people who decide to join the project outside of the event?

An earlier version of the workshops was run between April and May 2014 and the curriculum we used in the Spring is available online.

Sign up and Participate!

Participants! If you are interested in learning data science, please fill out our registration form here. The deadline to register is Thursday October 30. We will let participants know if we have room for them by Saturday November 1. Space is limited and will depend on how many mentors we can recruit for the sessions.

Interested in being a mentor? If you already have experience with Python, please consider helping out at the sessions as a mentor. Being a mentor will involve working with participants and talking them through the challenges they encounter in programming. No special preparation is required. And we’ll feed you! Because we want to keep a very high mentor-to-student ratio, recruiting more mentors means we can accept more participants. If you’re interested, email makohill@uw.edu. Also, thank you, thank you, thank you!

About the Organizers

The workshops are being coordinated, organized by Benjamin Mako Hill , Frances Hocutt, Jonathan Morgan, and Tommy Guy and a long list of other volunteer mentors. The workshops have been designed with lots of help and inspiration from Shauna Gordon-McKeon and Asheesh Laroia of OpenHatch and lots of inspiration from the Boston Python Workshop.

The workshops are being supported by the UW Department of Communication and the eSciences Institute.

If you have any questions or concerns, please contact Benjamin Mako Hill at makohill@uw.edu.

September 27, 2014March 27, 2016

Community Data Science Workshop Post-Mortem

[Reposted from Benjamin Mako Hill’s blog Copyrighteous.]

Earlier this year, I helped plan and run the Community Data Science Workshops: a series of three (and a half) day-long workshops designed to help people learn basic programming and tools for data science tools in order to ask and answer questions about online communities like Wikipedia and Twitter. You can read our initial announcement for more about the vision.

The workshops were organized by myself, Jonathan Morgan from the Wikimedia Foundation, long-time Software Carpentry teacher Tommy Guy, and a group of 15 volunteer “mentors” who taught project-based afternoon sessions and worked one-on-one with more than 50 participants. With overwhelming interest, we were ultimately constrained by the number of mentors who volunteered. Unfortunately, this meant that we had to turn away most of the people who applied. Although it was not emphasized in recruiting or used as a selection criteria, a majority of the participants were women.

The workshops were all free of charge and sponsored by the UW Department of Communication, who provided space, and the eScience Institute, who provided food.

The curriculum for all four session session is online:

Friday April 4th: Setup and Programming Practice
Saturday April 5th: Introduction to Python
Saturday May 3rd: Building data sets using web APIs
Saturday May 31st: Data analysis and visualization

The workshops were designed for people with no previous programming experience. Although most our participants were from the University of Washington, we had non-UW participants from as far away as Vancouver, BC.

Feedback we collected suggests that the sessions were a huge success, that participants learned enormously, and that the workshops filled a real need in the Seattle community. Between workshops, participants organized meet-ups to practice their programming skills.

Most excitingly, just as we based our curriculum for the first session on the Boston Python Workshop’s, others have been building off our curriculum. Elana Hashman, who was a mentor at the CDSW, is coordinating a set of Python Workshops for Beginners with a group at the University of Waterloo and with sponsorship from the Python Software Foundation using curriculum based on ours. I also know of two university classes that are tentatively being planned around the curriculum.

Because a growing number of groups have been contacting us about running their own events based on the CDSW — and because we are currently making plans to run another round of workshops in Seattle late this fall — I coordinated with a number of other mentors to go over participant feedback and to put together a long write-up of our reflections in the form of a post-mortem. Although our emphasis is on things we might do differently, we provide a broad range of information that might be useful to people running a CDSW (e.g., our budget). Please let me know if you are planning to run an event so we can coordinate going forward.

March 9, 2014March 27, 2016

Community Data Science Workshops at UW

The workshops are for people with no previous programming experience. The goal is to bring together both researchers and academics as well as participants and leaders in online communities. The workshops will all be free of charge. Participants from outside UW are encouraged to apply.

There will be three workshops held from 9am-4pm on three Saturdays in April and May. Each session will involve a period for lecture and technical demonstrations in the morning. This will be followed by a lunch graciously provided by the eSciences Institute at UW. The rest of the day will be followed by group work on programming and data science projects supported by more experienced mentors.

Introduction to Programming (April 5) — Programming is an essential tool for data science and is useful for solving many other problems. The goal of this session will be to introduce programming in the Python programming language. Each participant will leave having solved a real problem and will have built their first real programin their group. We will be relying on the curriculum from the Boston Python Workshops. Because we expect to hit the ground running, we will also run a session in the evening of Friday April 4 to help participants get software installed.

Importing Data from Wikipedia and Twitter APIs (May 3) — An important step in doing data science is collecting data. The goal of this session will be to teach participants how to get data from the public application programming interfaces (“APIs”) common to many social media and online communities. Although, we will use the APIs provided by Wikipedia and Twitter in the session, the principles and techniques are common to many online communities.

Data Analysis and Visualization (May 31) — The goal of data science is to use data to answer questions. In our final session, we will use the Python skills we learned in the first session and the datasets we’ve created in the second to ask and answer common questions about the activity and health of online communities. We will focus on learning how to generate visualizations, create summary statistics, and test hypotheses.

Our goal is that, after the three workshops, participants will be able to use data to produce numbers, hypothesis tests, tables, and graphical visualizations to answer questions like:

Are new contributors to an article in Wikipedia sticking around longer or contributing more than people who joined last year?
Who are the most active or influential users of a particular Twitter hashtag?
Are people who participated in a Wikipedia outreach event staying involved? How do they compare to people that joined the project outside of the event?

Our first session will be modeled after the Boston Python Workshops, but the curriculum of the later sessions is still in development and will be influenced by the needs of the participants.

Sign up and Participate!

Participants! If you are interested in learning data science, fill out our registration form here. The deadline to register is Wednesday March 26th. We will let participants know if we have room for them by Saturday March 29th. Space is limited and will depend on how many mentors we can recruit for the sessions.

Interested in being a mentor? If you already have experience with Python, please consider helping out at the sessions as a mentor. Being a mentor will involve working with participants and talking them through the challenges they encounter in programming. No special preparation is required. And we’ll feed you! Because we want to keep a very high mentor to student ratio, recruiting more mentors means we can accept more participants. If you’re interested, email makohill@uw.edu. Also, thank you, thank you, thank you!

About the Organizers

The workshops are being coordinated, organized, and led by Benjamin Mako Hill at the University of Washington Department of Communication and Jonathan Morgan at the Wikimedia Foundation. They have been designed with lots of help and inspiration from Shauna Gordon-McKeon and Asheesh Laroia of OpenHatch and lots of inspiration from the Boston Python Workshop.

These workshops are an all-volunteer effort. Fundamentally, we’re doing this because we’re programmers and data scientists that work in online communities and we really believe that the skills you’ll learn in these sessions are important and empowering tools.

The workshops are being supported by the UW Department of Communication and the eSciences Institute.

If you have any questions or concerns, contact Benjamin Mako Hill at makohill@uw.edu.