Instructor Notes

Experience as a instructor

This course material assumes that the instructor has taught workshops before …. but if you haven’t, this is a great time to start! It is highly recommended you complete https://carpentries.github.io/instructor-training/ if this is your first time teaching a workshop.

Considerations about this course material

This training was written with a few things in mind:

There is little RR training for those that are not doing computational based research. This course attempts to offer skills that are not coding-focused, so that a wider range of people can use them. While we do offer opportunities to introduce the idea of coding, we wanted to make this course inclusive to all disciplines and all levels of technical literacy.
People are in different stages and maturities on any topic. To make them feel welcome and comfortable, we highlight that this training won’t make them an expert, and we don’t expect them to have background knowledge. This training is moreso about helping them further down the path. We wrote the courses so that for each area, everyone should be able to walk away with an action item to improve their processes.
There are some RR improvements that need to happen in a bigger scale, such as the ‘publish or perish’ affect on reproducibility. We can’t solve the world’s problems today, but we can help the people in front of us, and that matters.

Career stage and how that will affect your talk

As per the culture page in your instructor menu, researchers from different career stages will have different considerations.

Generally, career level of attendees are skewed towards research students and early career researchers. However, depending on your department, you may find more senior staff in your cohort.

You may also consider how your attendees came to be here- did they sign up themselves? Were they encouraged to sign up by a supervisor or senior leader? You will find people who have signed up may have more background knowledge then those who were encouraged to go.

Different terminology and disciplines may respond differently

There’s a discussion to be had here on different terminology. Where a biologist may talk about a protocol, a humanities researcher may talk more of analysis, and a data scientist may talk about their code or pipeline.

The UKRN hosted a webinar on “Reproducibility, Transparency, Positionality? Perspectives From Different Research Fields” that may be a helpful resource. The recording and slides are available.

As a teacher

You don’t need to be an expert

People often mistakenly think that to teach a subject, you should be an expert.

As per orchid00’s article:

Expert Blind Spot

Experts are frequently so familiar with their subject that they can no longer imagine what it’s like to not see the world that way. This is called expert blind spot and can lead to what’s known as the expertise-reversal effect - experts are often less good at teaching a subject to novices than people with less expertise who still remember what it’s like to have to learn the things. This effect can be overcome with training, but it’s part of the reason world-famous researchers are often poor lecturers. The challenge of identifying and working around expert blind spots is one reason why we welcome instructors who still identify as “novices”! Someone who is still in the process of learning can be a more effective instructor because they are speaking from their own recent experience. In these ways and others, the high connectivity of an expert’s mental model poses challenges while teaching novices. However, that’s not to say that experts can’t be good teachers. Experts can be effective as long as they take the time to identify and correct for their own expert blind spots.

orchid00 (last updated Sept 2019) A Brief Introduction to the Carpentries Pedagogic Model. Retrieved on 2024-05-03 from https://orchid00.github.io/The_Carpentries_info/overview-of-carpentries-pedagogic-model.html licenced as CC-BY

People remember stories, not statistics

A study by Stanford professor Chip Heath found that during the recall of speeches, 63 per cent of people remember stories and how they made them feel, but only 5 per cent remember a single statistic.

Stories help people to relate to your lessons, to understand how and why it affects them.

Get a feel for your cohort

It can be helpful to ask your attendees a bit about themselves.

Finding out what area of research they are in can help you highlight resources that are more relatable. (Qualitative vs quantitative, computational vs not.)

Finding out their career level can help guage what they are familiar with already, and what their underlying concerns, risks and opportunities are.

Not sure on a question?

If a student asks you a question you don’t have an answer for, it is completely okay to say “I’m going to do some research into that, and get back to you.”

Contributing to these lessons

We aim to continuously improve these lessons and would love your feedback. To provide feedback or suggest changes, please contact Adam Partridge on a.partridge at sheffield.ac.uk or via a git issue submission here https://github.com/amandamiotto/ReproducibleResearch/issues .

Some other helpful resources are - teaching tips by the carpentries - Data storytelling and getting the impact across to non-experts