lessons learned from 2022
a thank you
To us, being the best machine learning summer school in health & biosciences is not the same as being the most perfect one. Rather, being the best means that the space we have created is not only open to all feedback, positive or negative, but also harnesses the feedback in a way that benefits everybody going forward. It is important that we learn from and take ownership of our mistakes.
In asking our participants for feedback - through surveys and conversations both during and after the event - we gain invaluable insight into what works and what doesn’t.
Yes, we leave ourselves vulnerable to criticism; to be receptive and open to it is scary, but in doing so, it helps us as a learning space to grow and evolve. Constructive criticism is necessary for identifying flaws in our ideas, and providing suggestions for improvement. So in reading, listening to and understanding our negative feedback, we gain a sense of clarity toward a more positive future, helping us to become an improved, more successful space for our participants in the years to come.
We would like to express our gratitude to all who provided feedback for MLSS 2022: whether that was through surveys or conversations, during or after the summer school, named or anonymous. In particular, we would like to thank those who provided us with open, honest and critical feedback; without those comments and suggestions, we could not have made the positive changes necessary as we progress into MLSS 2023.
moving forward
So what will be different about 2023? With all the feedback read, we have taken note of the aspects of 2022 that went well - and therefore we will be continuing or adding more of - and those that did not go so well - and therefore we will be removing or changing.
These points are summarised below:
we are continuing ...
General
Lecturers
"it was impressive how many different leaders were joining the sessions" - anonymous attendee, post-MLSS feedback
Engineering lectures
Leadership conversation series
“What set the summer school apart were the leadership conversations at the end of every day where accomplished scientists and change makers held an honest and interactive discussion on ethics, leadership, and responsibility in our roles as data scientists.” - Muhammad Ali, MLSS ‘22 attendee, LinkedIn
“It was my great pleasure to be panelist during Bumblekite’s Machine Learning Summer School (MLSS) this week in Zurich” - Gorana Dasic, MLSS ‘22 panelist, LinkedIn
Office hours
Participants
“So many lovely people to meet and learn from” - Romana Burgess, MLSS ‘22 attendee, Twitter
Food and catering
Feedback
we have changed ...
Session topics
Office hours
Technical prerequisites
Lecturers
Technical preparation for tutorials
Tutorial structure
“I'd like more briefings or conclusions on the tutorials, what am I supposed to be looking for? What methods should or could I be using?” - anonymous attendee, mid-MLSS feedback
Social programme
Accommodation
Coffee breaks
Communication in practice
Venue communication
Programme availability
the data informing our changes
To approach this process of self-evaluation, we used a wide range of rich data collected from participants, lecturers and the Bumblekite team in 2022.
This included: 23 mid-MLSS questionnaires (9 anonymous), 8 post-MLSS questionnaires (3 anonymous), 85 completed daily reflections during the summer school (29 anonymous), post-MLSS interviews with 9 participants (each interview lasted around 30 minutes), project review notes from 4 Bumblekite team members, and unstructured conversations with the 2022 lecturers.