Guest post: Coding Club – trying to overcome the fear factor in teaching and learning quantitative skills

This is a guest blog post by ecologists Isla Myers-Smith and Gergana Daskalova from the University of Edinburgh. In case you missed it, they wrote a wonderful guest post this summer on iPads and digital data collection in the field.

Ecology is a fast-paced science, with possibly hundreds of relevant papers published every week and new techniques and quantitative skills being developed all the time. It is easy to feel very behind and overwhelmed. The quantitative skills taught in undergraduate and graduate programs in ecology often lag behind those used in the literature. As ecologists at different stages of our academic careers, who have felt (and still do sometimes) pretty behind the eight ball in terms of quantitative skills, we wanted to do something about that for our students and peers. And that is how we came up with the idea of Coding Club.

How did it all begin?

Just about two years ago we had an idea. What if we set up an informal group and a website to teach key quantitative skills that could be useful to undergrads, grad students, postdocs, profs and ecologists working outside of academia? What if that website was built in a way that anyone could contribute tutorials or help to make the existing tutorials better? What if we taught people how to learn in their own working environment and how to develop their workflow using best practices in open science like version control from the very beginning? What if this content was aimed at people who felt afraid, anxious and behind in their own quantitative skills development. This was the beginning of Coding Club.

screen cap of the homepage for Coding Club; header says: Coding Club: A positive peer-learning community

The Coding Club website where we host all of our tutorials on data manipulation, data visualisation, modelling and more!

 

What is Coding Club?

Coding Club combines online and in-person resources to help teach quantitative skills to ecologists at all career stages. We have focused on trying to overcome “code fear” and “statistics anxiety”. Statistics anxiety – the worry about a lack of quantitative skills – and code fear – the fear of programming – can prevent people from learning. By building a sense of community around the development of skills, we hope to overcome the fear factor of ecology involving more code and math than people sometimes expect.

left panel shows six people posed, smiling at the camera; upper right panel shows a computer lab with people at work and someone at front; lower right shows three women talking and smiling

Part of the Coding Club team and snapshots of some of our workshops. Check out our team page for the full list of undergraduates, postgraduates and profs that have contributed to Coding Club! Photo credit for image on left: Sam Sills

 

Peer-to-peer teaching helps to reduce the fear factor

In Coding Club, we focus on peer teaching and interaction rather than having “trained experts” leading workshops as we feel people engage more when they are less intimidated. All of our teaching materials are developed by people who are actively learning data science skills at the same time as teaching them. We avoid hierarchy (though we love content on hierarchical modelling!) and encourage participation across different career stages from undergrad students through to PhD students, postdocs and staff. Moving away from the professor-student model and allowing everyone to engage as teachers and learners can be a pretty powerful way to break down barriers.

Coding Club covers a growing number of different quantitative skills

The Coding Club website contains a growing list of tutorials aimed at all levels of quantitative skills useful for ecologists and beyond. We cover topics from intro to advanced R tutorials, version control, data visualization to working with large datasets. We have a lot of R content but we don’t just do R! We are currently working on developing more tutorials using Python for process-based modelling and the Google Earth Engine for remote sensing analyses. We have been using the tutorials to teach in-person workshops at the British Ecological Society conference and at universities around the UK, but the tutorials are there online for everyone to use, provide feedback on or suggest revisions through GitHub. We are always looking for people to develop new content as well!

four badges, one for sharing quantitative skills, one for meta-analysis & bayesian statistics, one for spatial and population data, and one for pandas

A sample of the Coding Club tutorials, including a tutorial on how to make tutorials on GitHub. Data visualisation, mixed effects models, Stan models and more over here.

 

Quantitative learning should be active and not passive

We believe that the best way to teach coding and quantitative skills is through a problem-based approach that is question driven. We try to avoid approaches like ‘live coding’ as it encourages learners to be very passive with the subject matter and we believe this results in lower retention of the new material. To effectively learn a new skill, it is vitally important to know why you might want to learn that skill in the first place and to have a question that you want to answer to motivate you to learn. We also recognize that people learn in different ways and at different paces. In our in-person sessions, we encourage people to take as long or as little time as they wish to complete the tutorials. We believe this casual, non-compulsory and non-assessed nature of Coding Club also helps to reduce the fear and anxiety associated with quantitative skills.

 

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Coding our way towards finding out how population trends vary among different taxa, with cookies along the way. Not pictured: the standard error cookie. We forgot to make one, but of course we are all for reporting the uncertainty around effect sizes!

 

Quantitative skills are not hard – they just take some work to learn

We believe teaching quantitative skills is all about overcoming fear and building confidence. We try to avoid labeling skills as “hard” or “easy”, because we don’t want people labeling themselves as quantitative or not, or pre-judging the limits to their own capabilities. We aim to train people to be able to answer their own questions, resolve their own coding problems and seek out new skill sets independently. We are trying to teach people to train themselves beyond the timespan of a single workshop or course. Finally, we don’t think there is only one way to teach quantitative skills and promoting a diversity of approaches will reach the most people.

 

Coding Club has exceeded our expectations!

As of October 2018, the Coding Club website has received over 160,000 visits from over 73,000 unique IP addresses from over 180 countries. Our tutorials have been contributed by people from multiple universities (University of Edinburgh, University of Aberdeen, McGill University, Ghent University, Aarhus University) and used for quantitative training across several institutions so far (University of Edinburgh, University of Aberdeen, University of St Andrews, Queens University Belfast, Dartmouth College, Hebrew University, Calvin College, Centre for Ecology and Hydrology and more), and we are hoping to reach out further! If we can set up a network of people at universities and research institutes around the world who can work together to develop quantitative training from the ground up, then maybe we will all feel just a little less overwhelmed by our fast-paced discipline.

World map showing numbers of visitor, represented as blue dots. The dots are especially dark and big over the UK, but include visitors from around the world

The international audience of Coding Club – it’s been great to get feedback from people using our tutorials around the world!

 

The start of the new academic year feels like a fresh start. A chance to purchase some new office supplies, catch up on all the science missed over the summer, start a new work routine to enhance productivity and to set yourself some new challenges. Now that the term has started, maybe it is time for you to take the plunge and learn a new quantitative skill.

 

Are you a student or group of students wanting to increase your own quantitative skills? Are you someone who has a cool analytical technique that you want to share with your peers? Are you a prof. who wants to encourage your students and mentees academic development? Are you someone who feels like the quantitative training you got years ago is not enough for the ecological research today and want to brush up on your skills? Do you have thoughts on how we can improve quantitative training in ecology? If you answered yes to any of these questions, please comment below, check out the Coding Club website and get in touch if you are keen to join the team!

Guest Post: iPads and digital data collection in the field

From Meghan: This is a guest blog post by ecologists Isla Myers-Smith and Gergana Daskalova from the University of Edinburgh. I loved their comment on my post on our new lab notebook backup system and asked them if they could turn it into a guest post. I was very happy that they agreed! Isla and Gergana are off to the Arctic this summer with the Team Shrub field crew for another year of hopefully successful digital data collection. To find out more about their research check out the Team Shrub website and blog (https://teamshrub.com/).

Guest post:

Two things have really changed my academic life over the past five years: the first is embracing GitHub for version control of code, data, manuscripts and my research group’s individual and combined science, and the other is switching over to digital data collection. For ecologists who haven’t made the switch from paper field books to iPads and digital data collection it is not as scary as you might think!!!

Caption: Collecting plant phenology data – the recorder sitting in the back with an iPad! (photo credit: Jeff Kerby)

The benefits of going digital

Digital data collection can be more rigorous with error checking as data are collected to prevent mistakes. Data can be better backed up. And finally, it forces us to put thought into the structure of data before we collect it (significant digits, continuous or categorical data, are the data unrestricted or constrained to a particular range or particular set of values, etc.), which helps down the road when it comes time for analysis. Digital data collection has saved days, if not months, of data entry each year for my team and has allowed us to go from ecological monitoring in the field to analysis of results within hours instead of days. Our work flows are streamlined and our iPads are waterproof, so data collection can occur under any conditions – and we work in the Arctic, so we experience it all from wet to dry, hot to cold, rain, snow, you name it.

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Guest post: Women and relationships in academia: a curious journey of self-reflection

Note from Meghan: This is a guest post by Merritt Turetsky (@queenofpeat on twitter)

It’s not the first time a survey caught me by surprise. There was that time I glanced through a Cosmo survey – a guilty pleasure on a long flight – and realized that I was now lumped into the oldest age category.

How did that happen?

I actually like being mature, so was able to brush this off fairly quickly. But this survey was different. It somehow felt more personal. And I can’t stop thinking about it.

This survey was part of a department-wide review of gender balance issues. For years, I talked glowingly about my department, with a sense of pride that came from being part of an environment with strong women. When I was hired, I negotiated with a female chair. There was a good balance of female professors across full and associate rankings. Plus, there were several couples in the department. In my mind, this was all evidence that my department supported women in STEM and work-life balance. And as my husband and I accepted separate advertised positions and joined the department in 2008, we became yet another couple in a family-friendly work place. As a group, we seemed like we were on the right track towards gender equity.

Right?

Over the past month, we’ve taken on some self-analysis and it has revealed a few surprising trends. Despite our feel-good aura, our gender balance has not budged in the past 20 years. Females comprise 20% of our faculty, and this has been more or less constant.

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Guest post: a career as an ecologist at a non-profit conservation organization

Note from Jeremy: this is a guest post from Aaron Hall. Thank you very much to Aaron for taking the time to share his experience.

This post is part of our ongoing series on non-academic careers for ecologists. See here for links to previous posts in the series.

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If there were no barriers to men’s participation, we would all be doing it: a unique perspective on how to be a male ally to women in ecology

Note from Jeremy: this is a guest post from graduate student Anna Vinton and professor, author, comedian, and consultant Christopher Kilmartin.

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While at lunch in the Ecology and Evolutionary biology department, I [Anna] was discussing my position as chair of Women in Science at Yale. As the largest women in STEM organization at the University, we hold events geared towards supporting women in science and advocating for gender equality in all fields. A faculty member expressed his approval of the organization, but when I asked if he had attended events, he responded that it isn’t always clear when it was appropriate for him to get involved. This reaction is understandable, as many of these meetings serve as a safe space for those who don’t identify as men. But the conversation stuck with me, and I realized that once this safe space was established, the next step may be to establish spaces where men could listen in and learn how they can be effective allies.  People in dominant groups (heterosexual, white, cisgendered, wealthy, male, etc.) have important roles to play in the struggle for equality.

It is for this reason that I reached out to Dr. Christopher Kilmartin, an author, stand-up comedian, consultant and professional psychologist (among other things). Kilmartin lectures on the facilitators and barriers regarding men’s involvement with efforts to increase gender equality. He agreed to come to Yale on September 26th to give a public seminar regarding how to be an ally to women in the STEM fields thanks to funding from the European Society for Evolutionary Biology Equal Opportunities Fund. In discussing his lecture topics and workshop, we’ve come up with some take homes that can be useful to those not attending the lecture.

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Bad coauthors: how to avoid them and what to do when you have one

Note from Jeremy : this is a guest post by Abe Miller-Rushing and Richard B. Primack. Richard was Abe’s PhD advisor, and they continue to collaborate on many projects.

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BadCo-Authors

In this staged photo, Richard Primack and his research team exhibit disagreement and conflict.  In practice, weekly lab meetings and social activities (lunches, pot-luck dinners, walks, etc.) create opportunities for communication and shared goals.

We have written 45 articles together over the past 15 years. We know each other well and trust each other a lot.

But we (and probably most of you) have had experiences working and coauthoring papers with people we don’t know well—sometimes people we don’t know at all before a project begins. Most of the time the result is great! There are a lot of awesome scientists out there. And even when coauthors don’t click, it usually works out just fine—not everyone is going to be best friends, but most ecologists can get along well.

Occasionally, however, we have worked with bad coauthors: people who make doing research and writing papers way more complicated, difficult, and unpleasant than it needs to be. We have witnessed others work with bad coauthors, too. As editor-in-chief of a journal, one of us (Richard) has had to step in and mediate failed coauthor relationships too many times.

What makes a “bad coauthor?”

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A happy ending to a tenure-track job search

Note from Jeremy: This is a guest post from Greg Crowther.

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Previously I have whined about the difficulties of getting a good, stable college teaching job.  This whining is perhaps justified by the extremely low supply of these jobs relative to the demand.  But since almost everyone, including me, likes happy endings, I now wish to present a happy ending.  That’s right – I have received and accepted an offer for an ongoing full-time position.  At the age of 44, I have finally climbed aboard the tenure track.

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The teaching job that slipped through my fingers, and what I learned from that experience

Note from Jeremy: This is a guest post from my friend, biologist Greg Crowther. Thanks very much to Greg for being brave enough to share some personal experiences and advice that I’m sure will resonate with many readers. Thanks as well to Greg for only sharing non-embarrassing anecdotes about our time together as undergrads. 🙂

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This blog has featured fascinating personal stories (from Jeremy and Carla) on the often-long, sometimes-quixotic quest for a traditional faculty job.

Today I’d like to add another job-search saga to the pile – this one focused on teaching-focused positions – and to extract some lessons, if possible.

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Imperfect analogies: shortcuts to active learning

Note from Jeremy: this is a guest post from my friend Greg Crowther. Who among other things has been a biochemist, and an instructor in various biology courses including ecology. He’s an unusually thoughtful and creative teacher, for instance using songs to teach anatomy and physiology. Oh, and he has three papers in Annals of Improbable Research (e.g.), which is like the science humor equivalent of having three Nature papers. Thanks to Greg for writing us a guest post on a handy teaching tip. 

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Most people who think hard about how to teach well accept that students should engage in “active learning,” which has been defined (by Freeman et al. 2014) as follows: “Active learning engages students in the process of learning through activities and/or discussion in class, as opposed to passively listening to an expert. It emphasizes higher-order thinking and often involves group work.”

Sounds good, right?  In general, it is good.  I enjoy challenging students with hard problems and helping them find their way toward an answer, and they are usually glad to be moving and talking, especially if the problems resemble ones they’ll encounter on tests.

Active learning is relatively easy to include in teaching about a specific research study.  For example, after providing some appropriate context, one can simply work through the figures by asking students how and why the data in each figure were collected and what they mean (Round & Campbell 2013).

When teaching basic conceptual material, though, I slip into straight-up lecture mode more often than I’d like.  It can be very time-consuming to add nontrivial interactivity to coverage of this material.

However, I do have one fall-back strategy for quickly turning a traditional lecture slide into a mini-discussion.  I call this approach the “Dissection of the Imperfect Analogy.”  Here’s how it works.

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