One of the new things I had to do during my first year on the tenure track was to design a course. I have not designed entire courses before, but this was a great experience that I learned a lot from, and even managed to integrate my research with my teaching. As I am writing about this in my teaching portfolio, I thought I could share some of the insights in a blog post as well, that could be helpful to others in a similar situation.
The goal was to design a project course on image analysis for first year students. As most courses at my university, this would be a 5 ECTS course and run for 8 weeks (+2 weeks for evaluation/exams). A project course meant that many of the learning goals focused on (already defined) project skills. My job was to create an assignment on which students could work together. A first year course meant that I could not assume a lot of prior knowledge of the students. I also had to align my course with the other existing project courses, connecting theory, modeling and experiments. And of course, I wanted to create a course that was fun.
I started designing the course early on – as I started my job in February 2017, while the course would only start in November 2017. I used the other project courses in our department, and other courses I could find online as inspiration. I also searched for information about how to organize group projects, and what aspects of projects students like or dislike. I saved all of this Evernote and later used these notes during brainstorming.
During brainstorming, it became clear I wanted to add real-world components to the course, such as having a client for the assignment and gathering data. I also wanted to design the course in such a way that success was not too dependent on programming skills. So, I needed an idea which had all these components, and somehow involved analyzing medical images.
The project I settled on was extracting visual properties, like “asymmetry”, from images of skin lesions. Dermatologists look at such properties when making a diagnosis, and by automatically measuring such properties, we can design machine learning algorithms (which students will come across later in their studies).
I found a client for the assignment – the developers of the app Oddspot, which asks the user questions about the lesion, and then calculates a risk score. The developers could be interested in extending the app with imaging, and the students’ assignment was to investigate the possibilities. This way I had the basics for the theory – which features to measure in images, the model – an algorithm that actually does this, and the experiment – testing whether the measurements were effective.
I thought that another real-life component would be for the students to gather data. My first idea was to gather images of skin lesions with smartphones. But my own phone was not good enough to produce good quality images so I doubted this would work. Instead, I decided to use a public dataset from the ISIC melanoma detection challenge.
To still have a data gathering process, I asked each group of students to visually assess the features they were planning to measure with the algorithms. This way, even if a group would not be able to get their algorithms to work, they could still perform experiments – for example, by looking at interobserver agreement.
The project courses are assessed with a final report and a presentation. I decided to replace this traditional presentation with a Youtube video, aimed at a more general audience, such as prospective students. I thought this would allow for more creativity than a traditional presentation, but also build in some accountability, since the presentation could in fact be watched by other people.
As I was thinking of all these things, I was writing the guide that the students would get from me, to try to understand if any important information was missing, and filling in required documents related to the design of the course – for example, which learning goal would be assessed in which assignment.
During the course
Since this was a project course, I actually gave only one lecture to the students, where I talked about image analysis, measuring features in images, and of course explaining the assignment. After that, the students met in groups, together with teaching assistants (TAs). The role of the TAs in this case is to oversee the project skills part is going well – they are not required to have any background in image analysis and are not supposed to help the students with the content of the assignment. During these weeks, I would meet with the TAs and the study coordinator, who took care of all the logistics of these project courses, to discuss the progress of the groups. I made notes during these meetings, to take into account when updating the course next year.
After having read lots of “advice for tenure trackers” types of blog posts, I was afraid that teaching a large course would leave me overwhelmed with email. So, both in materials I gave to students and during the lecture, I asked the students to ask all questions related to the course content via discussions on Canvas. Of course I still got emails, but I redirected those students to Canvas and then answered their questions there, so that the answers would be visible to everyone.
What this system achieved was that (i) I didn’t get any repeat questions (ii) all students had the same information, so it was more fair and (iii) students could learn from each others questions/answers. Another advantage for me was that I would get a digest from all new questions in Canvas at the end of the day, so I could schedule times I would go through them, rather than multi-tasking during the day, as happens with email.
Another thing I have to emphasize is that a lot of the logistics were handled by the study coordinator, who found the TAs, checked which students were absent too often etc. Meanwhile, I could just focus on the content of course, which takes a lot of stress away from the experience of teaching for the first time. So, hats off to my department for setting it up this way.
After the course
The students really surprised me with their Youtube videos (in Dutch), which were all very well done. I even tweeted this:
So I’m grading the assignments of the undergrads (freshmen) who did an image analysis project I designed, and I just realized it made me cry because they did such an awesome job!
— Dr Veronika Cheplygina (@vcheplygina) January 12, 2018
At the end I had a short meeting with each of the groups to give them feedback on their assignments and get more input for the course. For example, I asked them what they found the most surprising and the most difficult (of course, recording this in Evernote). I also brainstormed a bit with them how to update the assignment next year.
I’m happy to report that overall I got good feedback about the project. The students said they particularly enjoyed that it was a real assignment and not something that was already done many times. This is great, but of course also means that I will need to update the assignment each year, so that the students are building upon each other’s work, and not doing exactly the same things.
I worried about the programming part being too difficult. During the course, the students did find programming challenging, but at the same time it was clear they were figuring things out. And all groups did submit code which was of sufficient quality. Most students indeed complained about the level of difficulty in the course evaluations, although a few students commented that they liked having to figure it all out themselves. This is definitely something I will address next year.
Finally, I of course also received course ratings. I know I should take these as a grain of salt, since group projects probably get higher ratings overall, student evaluations are not correlated with learning, but… it still feels pretty great to have a success in the middle of all my rejected grants and unfinished papers.
Integrating research and teaching
Remember all those visual ratings the students had to do? I’m not sure I realized it at the time, but in fact, I had just crowdsourced a lot of annotations for medical images. I am now using these results in my current research, and recently I submitted a paper about it, where I acknowledge the students who took the course. Another real-world component?
My take-aways from this experience would be:
Take a lot of time for brainstorming
Find examples of other courses
Evernote is great for keeping track of ideas, feedback, etc.
Use the learning environment to reduce your email load
Think how large classes of undergraduates can still participate in your research
Having teaching support is absolutely the best and made this potentially stressful experience very enjoyable
I’d like to thank Josien Pluim for brainstorming about the course, Chris Snijders for participating with Oddspot, Rob van der Heijden for coordinating a LOT of things, Nicole Garcia, Jose Janssen and Nilam Khalil for administrative support, Maite van der Knaap, Femke Vaassen, Nienke Bakx and Tim van Loon for supervising the student groups, and last but not least, students who followed 8QA01 in 2017-2018.