At work, we are beginning to go through our employee evaluation period and I want to explore the types of data that could be used to measure my performance. Standardized, quantifiable, and easily comparable data are prioritized in performance and accountability policies (Anagnostopoulos, 2013; Ozga, 2016; Williamson, 2017). I decided to use my Git commit history as a metric for my performance as it satisfies the three characteristics above. At the end of each day, I recorded the total number of additions, deletions, and files modified. Also, I did not record any contextual information about the modification as quantifiable data often requires the removal of supplemental context. Using this data, the following visualization was developed.
Like other types of performance and accountability data, the process of committing is susceptible to manipulation allowing the data to be quantified positively. For example, when I make substantial changes to a file I often duplicate the file and save it as filename_old while working on the file. If I do not finish the modifications right away, I will commit both files to the repository, therefore artificially increasing the number of additions.
By reducing the history to quantifiable data and removing contextual information can potentially result in misleading conclusions as it says little about the quality of the code written, completeness of the project (which can often be difficult to assess), and the time needed to troubleshoot, find solutions, and test/play around with code. This could lead to a culture of incentivizing poorly written and purposely lengthed code, which may not be noticeable to the administrators responsible in implementing these types of policies.
Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. 2013. Conclusion: The Infrastructure of Accountability: Tensions, Implications and Concluding Thoughts. In, Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. (Eds) The Infrastructure of Accountability: Data use and the transformation of American education.
Ozga, J. 2016. Trust in numbers? Digital Education Governance and the inspection process. European Educational Research Journal, 15(1) pp.69-81
Williamson, B. Digital Education Governance: political analytics, performativity and accountability. Chapter 4 in Big Data in Education: The digital future of learning, policy and practice. Sage.
Another really neat and ordered visualisation here. The choice of colour, as well as the ‘directional’ graphic, really convey the sense of opposing kinds of performance.
I think you reflect well here on the implications of a lack of context in such visualisations, particularly where they might be used to hold individuals to account over performance.