When we wrote Data Science in Education Using R (DSIEUR), we knew it would be easy to assume that new learners would have more prior knowledge about R than they actually did–a phenomenon called “ the curse of knowledge". We knew this intuitively as more experienced R users because we only vaguely associated feelings of triumph and frustration with the memory of running that first chunk of R code. The details of where we got stuck in our learning were harder to remember.
Over time, we traded in fresh memories of these learning pain points for experience and ease of use. That led to a design problem we had to solve while writing DSIEUR: How do we write with empathy for new learners when our own memories of learning R have gotten foggier?
To better identify with the learning experiences of others, we needed to identify where our blind spots were. To identify our blind spots, we needed strategies to empower others to give us feedback by sharing. Bringing our strengths to this collaboration but checking that we understand one another’s work, writing in the open, and using a project workflow to support creativity were our strategies to do just this.
As we neared the end of writing the DSIEUR manuscript, we set out to review each chapter and edit it for readability. To create an opportunity for fresh eyes to read our work, we each reviewed a chapter we hadn’t written. Ryan picked chapter 13, which is on using multilevel models to analyze student survey responses about their online classes. Having a new perspective on experiencing the chapter—particularly when the chapter includes lots of technical explanations—turned out to be a great way to discover blind spots in our work.
Here were Ryan’s thoughts after reviewing chapter 13:
“I don’t use multilevel models regularly in my work, so I could tell right away I was going to learn something new. I read through the sections to make small edits, but also took breaks every so often to check my comprehension of the concepts.”
About halfway through the chapter review, we started a great, back-and-forth conversation and brainstorm about conveying how standardizing coefficients works in multilevel models. Thinking back, we see that our different experiences with multilevel models were critical for accomplishing two things: identifying areas where the writing could be clearer and more accessible and also staying true to the technical parts of the topic.
We read through the chapter and picked out sections that didn’t seem clear. Then, we had conversations to clarify how standardizing coefficients work in multilevel models. We wrote out some initial thoughts to convey where we-together-ended up in our conversation. Then, we read the section again. And round and round we went, until finally we arrived at an execution we were happy with.
In this way, we supercharged the iterative writing process with two elements of collaboration. First, differing backgrounds gave us an opportunity to find blind spots. Josh has used, taught, and written about multilevel models regularly for years. Ryan had a basic understanding of the concepts, but less experience using them. And second, a collective goal—in this case, writing a book together—motivated us to communicate openly and experiment with different ways to create a great experience for our readers.
There are all kinds of ways to build empathy for new learners, like “listening” on social media, interviewing members of the R community, and regularly trying to learn new things ourselves. Creating in the open is another approach—one that the R community and others have embraced. Open source educational resources, software, and science make code available to readers to encourage collaboration and accountability. But can open source writing also help us check our biases about what learners need by including the learners themselves in the development of the content?
When data scientists share their writing and code through sites like GitHub and Kaggle, that sharing comes with an unspoken invitation to communicate with the creators. Most of the time, that communication is about improving code. When the open source project is designed to teach something new, the communication can also be about improving the learning experience.
Consider a scenario where a classroom teacher asks their students to complete worksheets—an example of (tacitly) closed source education materials. Not only do worksheets hide the underlying thinking behind their creation: they also invite compliance more than they invite conversation about what the learner needs.
On the other hand, providing the code for our book at every stage of
writing empowered us to share how we thought through an analysis. It
also set the tone for conversation on social media platforms and GitHub
about how we can improve the book. For example, in Chapter 8 we created
a visualization to explore scores from student classwork assignments.
For this post, we added
reorder() to change the order of values in the
# Scatterplot of continuous variable classwork_df %>% ggplot(aes(x = reorder(classwork_number, -score, median), y = score, fill = classwork_number)) + geom_boxplot() + labs(title = "Distribution of Classwork Scores", x = "Classwork", y = "Scores") + scale_fill_dataedu() + theme_dataedu() + theme( # removes legend legend.position = "none", # angles the x axis labels axis.text.x = element_text(angle = 45, hjust = 1) )
By making the code for this plot
available, we invited
readers (implicitly by being openly available but also explicitly by
requesting feedback on Twitter) to tell us where we could do more to
scaffold the lesson. For example, a reader might tell us they need more
explanation of how
reorder() is used to arrange the boxplots by median
In other cases, readers let us know when the writing itself didn’t make sense. For example, one community member read the online version of DSIEUR and emailed to tell us a plot that showed the importance of different variables for predicting a student’s final grade didn’t match the interpretation we wrote. It turns out we made revisions to the analysis that changed the plot, but we hadn’t updated the plot’s interpretation. We tracked this feedback and others in a GitHub issue and corrected it.
Indeed, while writing DSIEUR, sharing the book and its code led to conversations and opportunities for improvement we might not have had if we didn’t write in the open.
When we structure our creative endeavors like a formal project—including setting a deadline and working toward a product—we create many opportunities to encounter our oversights and blind spots.
One way to do this is to write for an audience (of any size). If you’ve written a blog or social media post before, you might recognize the experience of doing so: As soon as you publish the post, you find gaps (or typos!) in your writing you feel motivated to fix. Moreover, if you write a blog post in R Markdown, the process of publishing the post will expose issues, warnings, or messages related to the code—issues you may wish to address before (or after) publishing the post. Knowing someone will read your work gives that extra bit of productive pressure to offer value to your readers. Indeed, the audience has a role to pay in the creative process because they aren’t just reading, they’re participating. Conversations can start in the comment section or on social media. These conversations help you learn how well you’ve connected with the audience and help you uncover what’s important to your readers.
Another part of structuring creative endeavors is creating opportunities to have your work reviewed, either by you or by someone else. In the context of book writing, working through the copy-edits with our publisher helped (or forced!) us to encounter other parts of our writing we hadn’t considered: We realized late in the process that we used both the singular and plural form of ‘data’ throughout the manuscript (without expressed reasons for doing so!). Even an early GitHub issue about this very topic hadn’t prompted the same level of urgency as the copy-edits did. While more particular than the broader blind spots discovered through collaboration and writing in the open, these blind spots are critical for creating a clear, professional, and readable book for the audience. Whether reviewing your own work or having others review for you, an intentional revision process can uncover missed opportunities for improvements.
Finally, deadlines can help. The due date for the manuscript motivated us to improve how we communicated with each other. In Change By Design, Tim Brown writes:
“Curse deadlines all you want, but remember that time can be our most creative constraint.”
Indeed, the frequency of team calls and the commitment to revising and finalizing our manuscript together grew as our deadline approached. Thus, our publisher’s deadline provided a structure that encouraged organization, efficient decision-making, problem-solving, and collaboration. With the deadline looming, we got to the essential question as quickly as possible: Does what we wrote connect meaningfully with the audience and if not, how can we speak to them better?
In the end, the solution for writing the best book we could for new R users in the education field was to empower the community with tools to look at our work and share their reactions with us. Writing Data Science in Education Using R in the open was a way for us to express what we’ve learned, but also a way for us to keep learning.