Minirobots – small tools for a great automation experience

Our aim was optimizing the teaching content, methods and materials of our automation lab course module to more effectively provide the students with the related skills and knowledge.
Originally, we did run this course on two large “real-world” liquid handling robots because simpler and for teaching purposes more suitable instruments were not available. Our experience was, that these robots were somewhat too complex for the purpose of teaching basic concepts of automation. From a didactic point of view, we needed to spend too much time in a «demonstration and ex-cathedra» mode, explaining instrument-specific system requirements to enable the students to work with them. This suboptimal approach did not only cost a lot of course time and prevents the students from having extensive hands-on time which should be the focus of a lab course. Therefore, we applied for eight “minirobots” that fulfill much better the needs of the course. The implementation of these instruments required a re-design of the course program, i.e., we needed to develop new course tasks and the accompanying materials. This also included the need for a course-specific introduction to the programming language Python that is now provided at the beginning of the course.

The course was developed as outlined in the project proposal:
The purchase process of the eight Opentrons robots was initiated. The project developer was hired. One robot was used for testing and to gain experience with its hard- and software. The Lab Automation Facility team generated an excellent workstation design: a custom-build carts for each robot, that holds the robot and provides space for computer, a screen and the experiment materials. We tested various experiments for their doability and suitability for students and then designed the course program including the course materials. Major chapters of the established course program:
1.) Theory of automated pipetting.
2.) Programming in Python 10.
3.) Hard- and software of the Opentrons robots
4.) Practical tasks (methods that the students need to develop): a) Seeding cells in assay plates. b) Drug dilution series and verification of transferred volumes. c) Exchange of cell culture medium.
Challenges:
I) Making a user-friendly interface using an excel spreadsheet
II) Painting by numbers
III) Bacterial colony picking from agar plates.
After running extensive tests of the course, it was successfully run in the master student program with very good results of the students and very positive feedback.

The project goals were successfully achieved: The students received an introduction to Python as part of our lab course. This was beneficial also for another of our lab course modules (Image Analysis) that benefited from our course content. Based on the student’s feedback and our result assessment, the students were able to gradually build their programming skills as expected, starting off with very simple pipetting scripts and then progressing towards more complicated workflows (e.g., pipetting dilution curves). Students got trained in bug fixing their own code by using the build-in help functionalities of python and online resources. After mastering the programming of the robots, all students were able to measure the pipetting accuracy of the robots. They utilized liquids with different physical properties (simulating the variations of the regular laboratory environment) and gained so an understanding of how to optimize pipetting parameters for different applications. The pipetting accuracy was measured repeatedly by every student (at least 4x times) with different pipetting parameters. Students then had to prepare a protocol, where they described their expectations and observations and discussed the results from the pipetting accuracy tests. These protocols were used to assess if the students had understood the course contents and were reviewed/revised by the course supervisors until the teaching goals were reached. We were deviating from our didactic concept at this point, where we were envisioning that the students would present their results in a colloquium in the last course week. Since more students were part of the master program than originally expected, we had to create an additional student group and needed an additional week for the practical teaching. Due to semester time constraints, we replaced the colloquium with the written protocols to assess student performance.
Naturally, some students could build onto previous experiences in programming and were able to progress through the exercises quickly (first day), whereas others needed almost the whole course duration (2 days) to fulfill the tasks. The students who completed the first part of the course, where allowed to continue programming the so-called “challenges”. Students were free to pick tasks with different difficulty levels. We had to deviate here somewhat from our didactic concept, as we were expecting that all students would be able to finish all challenges. However, we realized that some students needed more guidance in learning the basics of python programming, and therefore needed more time.
For teaching larger student populations as expected in the coming years, we will have to assign two students to each robot. There will be a challenge to ensure that both students of each group will have an equal learning experience, but students may also benefit from discussing the project with their partner and faster progression through the exercises can be expected.