17 Learning More

17.1 Introduction

If you’re reading this book cover to cover, you’ve been through quite a journey! So far, you’ve:

  • Learned about the challenges of doing data science in education
  • Practiced some basic coding and statistics techniques
  • Worked through examples of analytic routines using education datasets
  • Reflected on introducing data science to your education organization over time
  • Learned about teaching data science to others

We hope this book sparked an interest in data science that you want to nurture. We’ve talked to many people in your shoes - folks who care about educating students and want to help by using their data skills. Indeed we’ve found the common thread in our audience is wanting to use data to improve the experience of learners. It’s important to nurture this passion by keeping the learning going.

Surrounding yourself with continuous learning experiences can turn this spark into a specialization that makes a real contribution to the lives of students. There are three reasons we feel these ongoing learning experiences are essential to realizing your vision for data in education. First, developing technical skills is a continuous process. The learning mindset is the same whether you’re taking your first steps toward using data science techniques or you’re a seasoned data scientist trying to make a bigger impact in education: there is always something new to learn about programming and statistics. Setting regular time aside to evolve your craft is a commitment to this mindset.

Second, education and data science are like most industries–they are constantly evolving. That means today’s tools and best practices might be tomorrow’s outdated techniques. To keep up with changes, it is important to develop a learning routine that exposes you to the pulse of these two fields. Sometimes this means learning a new technique, sometimes it means deepening expertise in a technique you haven’t mastered, and other times it means revisiting a skill you’ve mastered long ago.

And last, when you surround yourself with learning experiences, you inevitably surround yourself with others who are learning. Along your journey, you’ll interact with folks who are struggling through the same concepts as you, folks who are struggling through more complex concepts, and folks who are struggling with concepts you’ve already mastered. Participating in a community of learners has magical properties–it’s a place to learn, teach, inspire, and get inspired all at once. In Chase Javis’ book Creative Calling touches on this very point:

Whether online or in person, connecting with a community will support your learning efforts. It will also expose you to a diverse set of ideas that will dramatically enrich your perspective on what you’re learning. If you weren’t in love with your new skill before, this step can tip the balance. Passion is infectious.

You’ll need to use your intuition to find the areas where you want to deepend your knowledge. When you feel it, go there and dive in. Remember that the learning experience includes all kinds of activities. It’s a combination of reading, doing, discussing, walking away, and coming back. Here are some activities to include in your practice. We hope you take these and construct your own system of rewarding learning experiences.

17.2 Adopt a Growth Mindset

It’s normal to feel overwhelmed while learning skills like R and data science. This is particularly true when these fields themsleves are learning and growing. The R, data science, and education communities are constantly developing new techniques to move the field forward. It’s part of the beauty of this work!

When you’re feeling overwhemed by everything you’re trying to learn, consider adopting a growth mindset. Carol Dweck argues that we think of ourselves as being or not being a type of person. For example, we might think of ourselves as “math people” or “reading people”. What matters is whether or not this state is changeable. When we believe we can change, we adopt a desire to learn, choose to be around people who help us learn, and make the effort to learn. When we move from a fixed mindset to a growth mindset, we create the possibility of mastering new techniques and realizing our vision for using data in education.

The nuances of the growth mindset (Dweck, 2015) are beyond the scope of this book, but we do encourage the general belief that we can learn how to apply these techniques. We encourage you to adopt a growth mindset as a way to inspire learning and belief that you can introduce data science in your education job. In doing so, you’ll be joining other data scientists who created a way to contribute to their fields.

17.3 Discover New Information

The content you surround yourself with matters. You can learn a lot and stay inspired by high quality books, blog posts, journals, journalism, and talks. In his book Steal Like An Artist Kleon (2012) encourages people to surround themselves with great content:

There’s an economic theory out there that if you take the incomes of your five closest friends and average them, the resulting number will be pretty close to your own income. I think the same thing is true of our idea incomes. You’re only going to be as good as the stuff you surround yourself with.

In our Resources chapter, we share books and online resources that inspire us and help us learn. Use these as a starting point and build on them by seeking out authors, data scientists, and educators that inspire you to learn and master your craft. There are lots of ways to do this. Some folks follow data scientists on social media and take note of articles or talks that are getting attention. Others read data informed publications like FiveThirtyEight (https://fivethirtyeight.com/), The Economist (https://www.economist.com/), or The Upshot (https://www.nytimes.com/section/upshot) in the New York Times. Whichever you choose, make sure to stick with something that you’re drawn to and you just might find yourself with a new learning habit that is rewarding and fun.

17.4 Ask for Help

So far, we’ve discussed learning activities you can do on your own. Data science is a team sport, so at eventually your learning will lead you to others in the data science community. You can do this in many ways, both virtual and in real life. Here are a few examples you can try online. Try these and learn about what you’re comfortable with. Then build on that to surround yourself with many ways to ask and answer questions.

17.4.1 Discussion Forums

Visiting discussion forums is a common way to learn and participate in the R community. Websites like R Studio Community (https://community.rstudio.com/) and Stack Overflow (https://stackoverflow.com/) are very popular ways to do this. On these forums you’ll find many years worth of discussion about R and statistics. It’s quite unusual to search these and not find a way to get unstuck. Many discussions include a reproducible example of code that you can copy and paste into your own R console. This is a fantastic way to learn!

Consider learning best practices for asking forum questions. Including a reproducible example, or “reprex”, to communiate problems is a widely-accepted norm. Bryan (2019)’s video about making reproducible examples is a great place to learn more.

17.4.2 GitHub Repositories

When you want to learn more about how a package works or engage a package’s online community, consider visiting the its GitHub repository. {dplyr}’s repository (https://github.com/tidyverse/dplyr) is a great example. You can start with the README then dive deeper in the vignettes, which contain demonstrations of the package’s functions. You can even browse the code on GitHub to learn more about how the packages work. Don’t worry, you won’t break anything!

When you’re ready to see how the community engages a package’s authors, you can read through the Issues page. Each respository’s Issues page contains questions, feature requests, and bugs submitted by the programming community. Visit this page when you want to see if someone’s already submitted the coding challenge you’re working through. If you find you’re working on something that’s not a known problem, you can contribute by adding an issue. And finally, you can contribute to the development of packages by submitting code to the respository–this process is called a pull request. To learn more about contributing to packages, check out Woo (2018) talk.

17.5 Share What You’ve Learned

If you keep asking questions and finding solutions, you will soon find yourself ready to help others who are just getting started. The adage of learning by teaching applies here–answering someone else’s question also helps you deepen your learning and build empathy for new learners.

Adopting a regular sharing routine is a great way to start helping others. A sharing routine encourages participation in the community, invites feedback for improvement, and calls on you to build your craft in a way that others can understand it.

So what can you share? Really, what can’t you share? If you’ve built a cool function or visualization that took your project to the next level, you just might help or inspire someone else by sharing it. Maybe you’ve found an R package that really helped you–chances are it will help others.

Sharing isn’t always about the output of your work, it can also be about how you work. Consider sharing a workflow you’ve developed or your experience at a recent data science conference. Anything that you learned or found interesting will be relevant to others too!

What you share doesn’t have to be perfect. You can decide when you’re ready to share. Some data scientist’s blogs are polished and others are ideas-in-progress or shorter posts. You never know when someone will find value in your work, regardless of whether your work is in a refined state or not.

Laslty, you can select your best work from all your sharing and use it as an online work portfolio.

17.5.1 Where to Share

There are many ways to share your work online. For rapid fire conversational sharing, Twitter. Be sure to use the hashtag #rstats to reach more data scientists. For long form sharing, consider posting to a data science blog. Robinson (2018)’s blog post Advice to aspiring data scientists: start a blog is wonderful inspiration for getting started.

If you decide to post to a blog, there are tools to help you post data science content regularly. As noted earlier, Xie et al. (2019)’s {blogdown} is designed to help you create websites using R Markdown and a static website creator called Hugo. Blogdown makes it easy to create, run, and publish code directly from R Studio. Hill (2017) has a great introduction on getting started with with Blogdown.

When you do share a blog post or a tweet, broadcast what you have to say! On Twitter, use hashtags or “at” other community members to include them in the Tweet. On your blog, use blog aggregators that help share your posts to a wider audience. Here are two aggregators to get you started:

Finally, share the love by engaging your fellow data scientists! Retweet others, leave comments, and interact with the vibrant data science and R communities online.

17.6 Welcome Others

If you find yourself becoming an envangelist for R and data science in education–that’s what happened to us!–welcome folks who are curious and ready to learn. The strength of any community comes from its inclusiveness, safe learning environment, and capacity to welcome new members. The data science community is no exception–many members work hard to create an environment with active participants, engaging conversations, and celebrations for little and big data science wins. Our call to action is this: continue growing this inclusive and positive environment by being the community member you’d want in your own network.

Data science in education is a wonderful Venn diagram of communities, with new members joining every day. Welcoming, helping, and teaching new members is a great way to contribute to a positive community and to continue your own learning. What better way to inspire new members than to share your work and how it has impacted the lives of students!