Student responses to creative coding in biomedical science education

Abstract

Biomedical science students need to learn to code. Graduates face a future where they will be better prepared for research higher degrees and the workforce if they can code. Embedding coding in a biomedical curriculum comes with challenges. First, biomedical science students often experience anxiety learning quantitative and computational thinking skills and second biomedical faculty often lack expertise required to teach coding. In this study, we describe a creative coding approach to building coding skills in students using the packages of Processing and Arduino. Biomedical science students were taught by an interdisciplinary faculty team from Medicine and Health, Science and Architecture, Design and Planning. We describe quantitative and qualitative responses of students to this approach. Cluster analysis revealed a diversity of student responses, with a large majority of students who supported creative coding in the curriculum, a smaller but vocal cluster, who did not support creative coding because either the exercises were not sufficiently challenging or were too challenging and believed coding should not be in a Biomedical Science curriculum. We describe how two creative coding platforms, Processing and Arduino, embedded and used to visualize human physiological data, and provide responses to students, including those minority of students, who are opposed to coding in the curriculum This study found a variety of students responses in a final year capstone course of an undergraduate Biomedical Science degree where future pathways for students are either in research higher degrees or to the workforce with a future which will be increasingly data driven.

Keywords: Arduino; biomedical science education; creative code; data visualization; processing.

FIGURE 1

Mean response to 10 survey questions in classes in 2017 and 2018

FIGURE 2

The mean response to each question grouped by cluster identified from the K‐means algorithm

FIGURE 3

The mean answer of each cluster to selected questions, clockwise from top left: Q1 and Q2, Q4 and Q5, Q2 and Q8, Q6 and Q9

FIGURE 4

The percent of responses for open‐ended question of the best attributes (Q11) of the activity grouped by cluster

FIGURE 5

The percent of responses for open‐ended question needs improvement (Q12) grouped by cluster

FIGURE 6

An example of the templates given to students. Left: An abstract representation of data, which is animated to show the movement of a pulse (pink dots) over time. Right: An L‐system representing a timeline of data