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Willis, Alistair; Charlton, Patricia and Hirst, Tony
(2020).
DOI: https://doi.org/10.1145/3328778.3366927
Abstract
Written communication skills are considered to be highly desirable in computing graduates. However, many computing students do not have a background in which these skills have been developed, and the skills are often not well addressed within a computing curriculum. For some multidisciplinary areas, such as data science, the range of potential stakeholders makes the need for communications skills all the greater. As interest in data science increases and the technical skills of the area are in ever higher demand, understanding effective teaching and learning of these interdisciplinary aspects is receiving significant attention by academics, industry and government in an effort to address the digital skills gap.
In this paper, we report on the experience of adapting a final year data science module in an undergraduate computing curriculum to help develop the skills needed for writing extended reports. From its inception, the module has used Jupyter notebooks to develop the students' skills in the coding aspects of the module. However, over several presentations, we have investigated how the cell-based structure of the notebooks can be exploited to improve the students' understanding of how to structure a report on a data investigation. We have increasingly designed the assessment for the module to take advantage of the learning affordances of Jupyter notebooks to support both raw data analysis and effective report writing.
We reflect on the lessons learned from these changes to the assessment model, and the students' responses to the changes.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)
