Contextual Background
The context for this case study is the Introducing Computer & Data Science module, which is shared between two-degree programmes: Computer Science and Data Science. Towards the end of the module, I received a message from the Data Science course leader based on discussions with their students in classes to: i) explicitly touch on the data science concepts in the course and how they would be useful to data science students, and ii) What is the “Data Science” in the “Introducing Computer and Data Science” module? I explore how to plan for and support learning with these differences in student expectations.
Evaluation
In class, I sat down with the data science students (who tend to sit together on one table anyway) and answered the proposed questions with them in a discussion. The course leader feedback in conversation later suggested that this was effective, and that the students had told her that I’d sat down with them and reassured them on these questions. I decided to use this conversational style based on my previous teaching experience as a teaching assistant in group work situations, where face to face discussions usually helped to mitigate student’s concerns.
Moving forward
It is interesting that the data science students all sit on one table, and as this is the only module they have the computer science students, at times it is likely they may feel excluded – or had less time with the computer science students to build bonds. This has been highlighted data science module leader previous, who I know has made efforts of their own to tighten bonds between the two courses. I want to next time mix the seating arrangements of the students throughout the course, to see if creating stronger social bonds across the courses might help to support their learning and feel less excluded.
Another approach could be to spend more time planning the course to discuss the intersection of the two disciplines (where data science is a subset of computer science) and the differences in emphasis. Maybe a closeness between the students could create a discussion similar to the one I had with the data science students, supporting their learning and recognition of the common interests between the two courses.
There is also an opportunity to better differentiate the curriculum (Eikeland and Ohna, 2022) to include data science and computer science exercises. Students could then be supported in exercises more aligned with their disciplinary interests. In line with my teaching philosophy, a more Brechtian approach (Demirdiş, 2021; see Blog Post 2) could also playfully make the different courses learn each other’s similarities and differences, perhaps by using a gamified (Yildirim, 2017) approach and having the students compete against each other. Or by making use of classroom space to expose how the different groups are not intersecting. On the other hand, these approaches might make data science students feel more excluded as clear differences are observable; care needs to be taken to ensure that their reflections on issues lead to moments of connection.
References
Demirdiş, M. (2021) ‘Bertolt Brecht’s Theatrical Techniques’ Connection with Critical Pedagogy and Their Usability in Learning Environments’, in. Available at: https://api.semanticscholar.org/CorpusID:252019494.
Eikeland, I. and Ohna, S.E. (2022) ‘Differentiation in education: a configurative review’, Nordic Journal of Studies in Educational Policy, 8(3), pp. 157–170. Available at: https://doi.org/10.1080/20020317.2022.2039351.
Yildirim, I. (2017) ‘The effects of gamification-based teaching practices on student achievement and students’ attitudes toward lessons’, The Internet and Higher Education, 33, pp. 86–92.