This is part 1 of a multi-part series on thinking through parts of neuroscience research that can be adopted when creating online video courses. I use the example of creating an online course on Software Testing for this series. I rely heavily on Make It Stick by Peter Brown and others for my research.

In this post I focus on setting up the context in which I have carried out the research and thinking. I share my journey of teaching software testing so far and also explain why I am trying to create the online course.

The LYFT Training Programme

The story begins with the LYFT (Light Your Future in Tech, no connection with the ride sharing service) training programme my friend Mo Bhojani and I designed. Many people in our local community have migrated to the UK from other parts of the world and for a variety of reasons are stuck in laborious jobs even when they have received good education in their home countries. The aim of the LYFT programme is to train such migrants in computing skills so that they can improve the quality of their lives and also contribute more significantly to the UK economy.

Obviously, there are many other skills that could have been taught to achieve the goal of improving the quality of life. However, we both are from computing backgrounds - Mo has about 10 years of software testing experience and I have played different kinds of software developer and team lead roles for about 11 years. We thus chose to teach a computing skill. When picking the specific subject to teach, we considered web development, DevOps, and automated testing, but settled on manual web and mobile application testing. It was the easiest subject to teach and had a demand in the UK market increasing the likelihood of participants getting a job after the course.

We have designed the programme as a 6 month long part time training, split into two equal parts. The first part involves classroom teaching and practice and the second part is a 3 month long part time internship at a web/mobile app development company. The content of the training is reverse engineered by answering the question:

If we were to interview an entry level software tester what would we expect them to know?

This resulted in teaching the participants the following topics:

• Development methodologies: TDD, BDD
• Development/Test processes: SCRUM, Kanban, Waterfall
• Test Case format: Gherkin, Standard
• Test management software: JIRA
• Mobile platforms: Android, iOS
• API testing tool: Postman
• Proxy/Debugging tools: Charles, Web inspector
• Device debugging tools: ADB, iOS Console

Batch 1 - The Highs and Lows

Training for the first batch of participants is currently underway with classroom based teaching and practice close to completion. We have about 15 participants who will go on to intern at a variety of startups and technology firms. We believe most of these participants have acquired enough skills to be entry level manual testers. Some are good enough to grow very quickly into mid-level testers as soon as they acquire some experience. While the end result (of how many actually get jobs) is still far away, the journey so far has been immensely satisfying.

The enthusiasm showed by the participants has fueled our desire to do as good a job as we could. We have pushed ourselves quite a bit, constantly reviewing and revising the course plan, re-teaching topics in a different way if we failed to teach well the first time, and coming up with innovative ways of providing practical experience. This has improved the course quality significantly, but it has also demanded a lot of time and effort at our end. With 5.5 hours of class time spread over three sessions per week and the time required to review curriculum, plan lectures, prepare slides, design and evaluate tests, etc we have spent 12+ hours/week each on the programme. As a result we have been forced to cut down on any other social activity or projects that we were involved in.

The Sustainability Question

This is clearly unsustainable if we want to conduct more batches of the programme. Even mid-way through the first batch we have felt some burnout effects, needing some downtime. So how do we ensure that the programme can continue?

We can train teachers from among the participants of batch 1 and they could teach the next batch. The participants from the next batch in turn can be trained and then they can teach the next batch, thus triggering a chain reaction of sorts. However, it isn’t clear how effective such a training programme will be. During the first batch, we have relied on our experience to answer a variety of questions from participants. We have also relied on our experience to explain complicated concepts from different perspectives depending on what made sense for the participants. Finally, we have relied on our depth of knowledge to distill the most important learnings and highlight them during the classes. These things would be very hard to achieve for the teachers in later batches.

Another approach can be to record videos of the lectures structure the course such that participants watch the video before coming to the class and discuss the video. With such a structure it will then be possible to reduce the number of classes to two per week, each lasting one hour. Further, we will require little prep work before the classes, even though the test design and evaluation will still take time. Such a structure can bring down time required to 3 hours/week each. Further, if we get the videos right the experience required for facilitating discussions can reduce. We can then train participants of batch 1 to facilitate classes for later batches and thus design a more sustainable program.

To enable a highly scalable learning mechanism the videos can form a self learning online course. This of course will not be as effective as a course that includes discussions, but will be significantly more scalable.

Creating the Right Videos

Having taught the first batch we have largely figured out what needs to be taught in the software testing programme. We also have lecture-by-lecture slides that details the content for each lecture. This raw material is a fantastic starting point for preparing the videos. However, with video based lectures, a teacher does not have the luxury of looking at the students to quickly guage whether they are following the content being taught. This means that the content of the video needs to be structured so that a variety of students are able to follow well and learn effectively.

For this purpose of ensuring effective learning, I have turned to understanding research on neuroscience of learning. I am in the process of identifying aspects of the research that will be useful when structuring our video based online course. I use this series as a way to organise my understanding of this research.

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In the next post I will talk about what I mean by learning and look at the first empirical technique of learning - Testing.