Abstract:

Laboratory courses are a staple in the higher education areas of science, technology, and engineering. Owing to budget and time constraints, experiments in these courses oftentimes use outdated equipment and instructions get rarely revised. As a result, student perception of these experiments is poor, and the transfer of knowledge is low. This is a missed opportunity since the hardware needed for modern and powerful data acquisition systems has become affordable in the past couple of years. With such new hardware, these lab courses present an ideal environment to teach highly sought-after skills in the areas of data science and data analysis.

This work describes the transformation process of one such experiment at the KIT (Karlsruhe Institute of Technology) advanced physics laboratory course. From the initial assessment of the status quo, over the development of a new didactic concept, to the incorporation of Python as a contemporary software framework, and the creation of supporting materials for students and tutors in the form of Jupyter Notebooks.

State-of-the-art data analysis packages are integrated into the workflow, so students get an introduction to modern data analysis with a real-world example during the lab day. Small consecutive tasks with increasing complexity are set to be worked on under the supervision of a tutor, teaching the necessary skills for the lab report as well as future experiments. This all while maintaining a tight focus on the physics at hand, fostering a deep understanding of the science and engineering behind the experiment.

Keywords:

HEA, laboratory course, data analysis.