Integrating Interdisciplinarity in Higher Education: Lessons from Machine Learning Education
Summary
Drawing from his academic research in the history of late 20th-century computing and science, Dr. Michael Castelle asserts that, “Disciplines get stuck if they don’t have a lot of communication outside of their field.” He argues that “Scientific revolutions are caused by connection, cross-disciplines, bringing in different theories, perspectives, and disciplines.”
Within the case study reflecting on his experience teaching the interdisciplinary module on machine learning (a type of Artificial Intelligence) at the Centre for Interdisciplinary Methodologies (CIM), Michael emphasises that interdisciplinarity is “not something that you can just inject into teaching” but rather “has to be a part of one’s research.” He encourages embracing “other perspectives in order to be fluent in multi-disciplines.” Michael notes that assigning students to random small reading groups not only facilitates social engagement but also encourages them to engage in meaningful discussions about the reading material, leading to increased exploration of topics such as the social impact of AI, a key focus of the module.
Students Say
While feedback from students about Michael’s interdisciplinary module is largely positive, he recognises that "Not all students will be ready for an interdisciplinary class," especially those with specific career goals or skills they aim to acquire, making it "hard to win them over." To address this challenge, Michael has implemented a strategy of creating a "safe space for ideas from different disciplines." This includes assigning students to random small reading groups, a method he successfully employed during the pandemic.
Dr. Michael Castelle
Assistant Professor, CIM
Dr. Michael Castelle is the module convenor for Interdisciplinary Approaches to Machine Learning at the Centre for Interdisciplinary Methodologies (CIM). His research encompasses the economic sociology of markets, the history of late 20th-century computing, and science and technology studies. He is interested in the use of sociological, anthropological, historical, and semiotic perspectives to recontextualise and understand contemporary technological practices, including databases, distributed systems, machine learning, and artificial intelligence.
See Michael’s full bio here.
Highlights
“Disciplines get stuck if they don’t have a lot of communication outside of their field.”
Michael emphasises that interdisciplinarity is “not something that you can just inject into teaching” but rather “has to be a part of one’s research.”
“Create a safe space for ideas from different disciplines”
Further Resources
Curious to learn more?
Please find information on Interdisciplinary Approaches to Machine Learning Module and related resources below:
- IM931 Interdisciplinary Approaches to Machine Learning
- A new volume co-edited by Michael Castelle, The Cultural Life of Machine Learning
- The Book – The Cultural Life of Machine Learning: An Incursion into Critical AI Studies
You can also contact Michael: M.Castelle.1@warwick.ac.uk