Instructor Training: Instructor Notes


See Atul Gawande’s 2007 article “The Checklist” for a look at how using checklists can save lives (and make many other things better too).

Scheduling the Event [Coordinator]

  1. Decide if it will be in person, online for one site, or online for several.
  2. Talk through expectations with the host(s).
    • If it is in person, make sure the host knows they’re covering travel costs for trainers.
    • Determine who is allowed to attend.
      • We strongly prefer trainees to have attended workshops (as learners or helpers).
      • Other criteria may be negotiated by the Executive Directors as part of partnership agreements.
  3. Arrange trainers.
  4. Arrange space.
    • Make sure there are breakout rooms for video recording.
  5. Choose dates.
    • If it is in person, book travel.
  6. Get names and email addresses of attendees from host(s).
    • Register those people in AMY.
  7. Email attendees a welcome message that includes:
    • a link to the workshop home page
    • background readings
    • a description of any pre-requisite tasks
  8. Make sure attendees will all have network access.

Setting Up [Trainer]

  1. Create an Etherpad (
  2. Set up a one-page website for the workshop using as a starting point.
  3. Send the URL to the admins.
  4. If it is online:
    • Test the video conference link.
    • Set up meeting with the hosts to make sure the bluejeans channel works and give you a change to meet “face-to-face”
  5. Check whether any attendees have special needs.

During the Event [Trainer]

  1. Introduce yourself (see detailed guide below).
  2. Ask your trainees to introduce themselves to each other.
  3. Remind everyone of the code of conduct.
  4. Collect attendance.
  5. Distribute sticky notes.
  6. Use the etherpad.
  7. Collect participants’ GitHub IDs (if they are interested in teaching Software Carpentry).
  8. Go through the checkout procedure point by point.
  9. Explain how we format lesson submissions.

After the Event [Trainer]

  1. Email a list of attendees and no-shows to, noting the URL of your training event.
  2. Administer the post-training survey.
  3. Email attendees about the checkout process.
  4. Debrief with the head of instructor training.

Between Instructor Training Sessions [Trainer]

  1. Sign up to lead teaching demonstrations.
  2. Email a list of trainees who participated in teaching demo to Note whether they passed or failed.

After Trainees Complete [Head of Instructor Training]

  1. Send new instructors the completion message.
  2. Create and send PDF certificates.

Note that trainers do not examine their own trainees: having them examine each other’s helps balance load and maintain consistency of curriculum and standards.


You may use the following message templates to communicate with trainees:


To begin your class, the instructors should give a brief introduction that will convey their capacity to teach the material, accessibility/approachability, desire for student success, and enthusiasm. Tailor your introduction to the students’ skill level so that you convey competence (without seeming too advanced) and demonstrate that you can relate to the students. Throughout the workshop, continually demonstrate that you are interested in student progress and that you are enthusiastic about the topics.

Students should also introduce themselves (preferably verbally). At the very least, everyone should add their name to the Etherpad, but its also good for everyone at a given site to know who all is in the group. Note: this can be done while setting up before the start of the class.


Video Recorded Lessons

One of the key elements of this training course is recording trainees and having them, and their peers, critique those recordings. We were introduced to this practice by UBC’s Warren Code, and it has evolved to the following:

  1. On day 1, show trainees a short clip (3-4 minutes) of someone teaching a lesson and have them give feedback as a group. This feedback is organized on two axes: positive versus negative, and content versus presentation. The first axis is explained as “things to be repeated and emphasized” versus “things to be improved”, while the second is explained by contrasting people who have good ideas, but can’t communicate them (all content, no presentation) with people who speak well, but don’t actually have anything to say.

  2. Trainees are then asked to work in groups of three. Each person rotates through the roles of instructor, audience, and videographer. As the instructor, they have two minutes to explain one key idea from their research (or other work) as if they were talking to a class of interested high school students. The person pretending to be the audience is there to be attentive, while the videographer records the session using a cellphone or similar device.

  3. After everyone has taught, the trio sits together and watches all three videos in succession, writing out feedback on the same 2x2 grid introduced above. Once all the videos have been reviewed, the group rejoins the class; each person puts all the feedback on themselves into the Etherpad.

In order for this exercise to work well:

The announcement of this exercise is often greeted with groans and apprehension, since few people enjoy seeing or hearing themselves. However, it is consistently rated as one of the most valuable parts of the class, and also serves as an ice breaker: we want pairs of instructors at actual workshops to give one another feedback, and that’s much easier to do once they’ve had some practice and have a rubric to follow.

Live Coding Demo Videos

Note on closed captioning: Sometimes audio in the room can be poor. Two suggestions for improving accessibility are to have students watch it on their own laptops and to turn on closed captioning by pressing the cc button at the bottom of the video.

Part 1: how not to do it

Part 2: how to do it right

Motivation and Demotivation

The Big Picture

In 2014, George Monbiot wrote:

If we had set out to alienate and antagonize the people we’ve been trying to reach, we could scarcely have done it better. This is how I feel, looking back on the past few decades of environmental campaigning, including my own…

Experimental work suggests that when fears are whipped up, they trigger an instinctive survival response. You suppress your concern for other people and focus on your own interests… Terrify the living daylights out of people, and they will protect themselves at the expense of others…

A lot of advocates for open science and reproducible research make the same mistake. They frighten people with talk of papers that have been retracted when they should talk about all the new science people could do if they weren’t wasting hours trying to figure out how they created figure number three in the first place.

We have found that we have more impact when we emphasize how much more researchers can do when they are computationally competent. We have also found it’s importance for us to emphasize that what we teach and how we teach it is based on the best available evidence. We use live coding instead of slides because research shows that people learn more from doing than watching. Similarly, the tools we teach are ones that our instructors—who are active researchers themselves—use daily.

One final point to make in instructor training workshops is that our greatest impact may be what we teach our instructors about teaching and collaborating. As a species, we know as much about education as we do about public health, but since most university lecturers are self-taught teachers, they are completely unaware of this body of knowledge. At the same time, the massive, open collaboration that has made Wikipedia and open source software successful has never taken hold in teaching. Most university lecturers are still the sole creators and consumers of their lessons, which wastes time and impedes the spread of good ideas. Changing that could have more impact in the long run than anything to do with for loops and pull requests.

You Are Not Your Learners

Discussion of the practical implications of learning concepts brings us to our next big idea: people learn best when they care about the topic and believe they can master it. Neither fact is particularly surprising, but their practical implications have a lot of impact on what we teach, and the order in which we teach it.

First, most scientists don’t actually want to program. They want to do scientific research, and programming is just a tax they have to pay along the way. They don’t care how hash tables work, or even that hash tables exist; they just want to know how to process data faster. We therefore have to make sure that everything we teach is useful right away, and conversely that we don’t teach anything just because it’s “fundamental”.

Second, believing that something will be hard to learn is a self-fulfilling prophecy. This is why it’s important not to say that something is easy: if someone who has been told that tries it, and it doesn’t work, they are more likely to become discouraged.

It’s also why installing and configuring software is a much bigger problem for us than experienced programmers like to acknowledge. It isn’t just the time we lose at the start of boot camps as we try to get a Unix shell working on Windows, or set up a version control client on some idiosyncratic Linux distribution. It isn’t even the unfairness of asking students to debug things that depend on precisely the knowledge they have come to learn, but which they don’t yet have. The real problem is that every such failure reinforces the belief that computing is hard, and that they’d have a better chance of making next Thursday’s conference submission deadline if they kept doing things the way they always have. For these reasons, we have adopted a “teach most immediately useful first” approach described in this episode.

Software Carpentry Is Not Computer Science

Many of the foundational concepts of computer science, such as computability, inhabit the lower-right corner of the grid described above. This does not mean that they aren’t important, or aren’t worth learning, but if our aim is to convince people that they can learn this stuff, and that doing so will help them do more science faster, they are less compelling than things like automating repetitive tasks.


This course has been taught as a multi-week online class, as a two-day in-person class, and as a two-day class in which the learners are in co-located groups and the instructor participates remotely.

Two-Day In-Person (Currently used)

This was the second method we tried. The biggest change was the introduction of recorded teaching exercises.

Two-Day Online With Groups (Currently used)

Multi-Week Online (Retired)

This was the first method we tried.

Demo Sessions

Checklist for instructor trainers hosting a live-coding demo session as part of a trainee’s checkout procedure.

Before the Demo

Shortly Before the Demo

During the Demo

After the Demo

Example Starting Points for Demos