In the past few months I have led lots of online trainings for education data analysts. The topics have varied:
- how to transition a team of analysts from Stata to R,
- analysis for a quasi-experimental evaluation
- building and using enrollment forecasts in college admissions planning
- Shiny dashboard development and deployment
- many many more
Training is one of the core services we provide at Civilytics and I wanted to share four lessons we’ve learned about doing technical training online.
- Define success
- Keep it short
- Focus on the project
- Give generous feedback
Define Success
I begin every training engagement by listening to what the learners want to achieve and asking them what barriers they see to getting there. If your organization is working with us, our job is to help you overcome those barriers. So, together, we discuss the goals, barriers, and timeline and come up with a clear set of expectations about how we will know we have succeeded.
It’s tempting to jump right in with a how-to guide to install RStudio and start working with data. But in doing so, you may rush past parts that are critical to the success in the course or miss an opportunity to connect the content to existing expertise. Working professionals have many different goals when learning a new technical skill, and they have a lot of competing time pressures. Staying focused on their goals (by first helping them define them), and not your content as an instructor, leads to better outcomes.
Focus on a Project
Defining success often revolves around a project or task that the learner cannot complete without the training. The entire training needs to keep focused on that project. Working professionals need to see real returns on their learning, as soon as possible. Data analysts in education are busy juggling many competing priorities. Learning is asking them to take a step back in productivity, with the promise of a leap forward. Keeping focused on a project that is immediately relevant and yields productivity returns is critical to helping maintain motivation.
To be the most successful, then, you can’t just use the same training material over and over. I mean, you can and companies have built big businesses on it, but this model only serves the most engaged learners, with extra time to learn, and often does not result in that knowledge being applied to their actual work.
To overcome this, I plan the training content only after working together with the learners to define success and understand their projects. This also gives learners a voice in what they learn and allows us to develop an asset-based curriculum that focuses on existing strengths. Learning something like R is frustrating, but it is doubly so when it is presented as something completely disconnected from the other skills professional data analysts already have.
Keep it Short
Professionals are busy and it can be hard just to find the time to bring them together for a training session. There is a temptation to try to package all the training into as few meetings as possible – to simplify the calendaring. But this doesn’t work well for a couple of reasons.
First, even in the best of times, long meetings don’t work. Interruptions happen. Attention gets divided. Patience grows short. This is natural and human and even the most motivated and dedicated learners will feel this after a couple of hours. Yes, you can push through. Yes, you can schedule breaks. But, you shouldn’t. Because this is not the best of times. Learning new things is hard and it is important to be attentive to our limitations as we learn.
Second, a few long intensive online learning sessions are not how you learn a new craft. Think about any skill you are really masterful at. You did not get that way after a few 8 hour instructional videos and some practice. You got there by lots of practice, lots of feedback, and time to reflect and apply what you just learned.
Shorter and more engaged instructional sessions work best – especially now that everyone is living at work.
Give Generous Feedback
I learned to play guitar when I was 12, before there was YouTube. My parents were against it so I never had lessons, I was self-taught, and, pretty good, I thought.
This year I picked the guitar back up for the first time in 10 years. To get started, I watched a couple of YouTube videos. Instantly, I learned about ten things I had been doing wrong my whole life.
The last time I learned guitar, I was never able to watch someone good play or listen to them explain what they were doing (and no one ever watched me). It seems obvious – but this is also how most data analysis is taught and learned in the public sector. We may learn how to know if we have the right number (the right note), but we are never given feedback on how to get to that answer (the technique).
To be successful, learners need feedback and they need to see how to write their code, not just learn which code to write. This is where a project-based approach really shines because it keeps some focus on the things the learner is an expert at (the substance of the project) and positions learning the new skill as in service of that project (not central to the learner’s sense of efficacy or skill at their job). This creates a much more comfortable environment for feedback, and positions the instructor not as an all-knowing expert, but as a coach, helping the learner do their best.
Of course, there are other things that go into a successful professional training environment – but as many teams are working online together for the foreseeable future, I wanted to share some of our best tips for success to help you in your own professional learning.
If you or your team are looking for training to help you execute a challenging project, transition to a new set of data tools, or streamline your existing work, get in touch with us about how we can help coach you to success.