Final reflections
What are ‘data’ – further reflections
The first reflective task at the start of the course was on ‘what are data’. My answer at the time was defined data as a set piece of information (or fact) which has an assigned value or measurement. Now however, following completion of the collection and visualisation exercises, alongside the readings, I have a better appreciation of the depth, scope and complexity of data. The data visualisation exercises for me highlighted some of the issues that can arise in data; collation, interpretation, and analysis, as well as how the presentation of data can influence the information it is trying to convey and how data can have many uses. Williamson’s (2020) reference to data as ‘social products’ carries far greater meaning now having attempted my own data collection exercises. Taking account of the various definitions of ‘big data’, specifically here considering Kitchin’s (2016) reference to the seven traits of big data (with volume and velocity being two key traits), the scope and impact of data in our daily lives is persistent, immense, and complex.
Data collection
When considering a theme for my visualisations, I often started with a reasonably narrow intention of what data to record, only later adding to this as I noted additional information which I recognised as being important related info. Inevitably when reviewing what I had recorded I also realised that interesting trends or patterns I found were not as anticipated, instead the more revealing pattern often related to this additional information. For me this was the first lesson in paying attention to the criteria of data recording, data is not neutral but comes loaded with baggage and potential misrepresentation.
Each time I therefore reflected on the data I collected I therefore found it was important to take a step back and consider the wider picture, not just the data I had but what data I may have left out. This was something I touched on in my visualisation on communication. Where due to potential weaknesses in the data I collected the value placed on the data may not have been a true representation of the work required to complete the task I was recording. There are areas of data I could have collected which would have added greater depth and meaning, it may have changed the value assigned to each piece of data and would likely have produced a different visualisation.
Which leads me to a related point about the importance of capturing accurate data. It is not just the omitted or ignored data that can generate false interpretations of that data, but inaccurate data or poorly categorised data can lead to a data set which results in false interpretations. This was obviously not a significant issue for the tasks that I completed but did highlight to me the significance that missing, inaccurate, or wrongly valued data could have if the output of the data was used as the basis of setting policy agenda or assessing the performance of an individual or institution.
To ensure these kinds of issues don’t occur there has to be the correct mixture of people involved in the development process when designing processes and software to ensure relevant, accurate, key information is selected as ‘data’. In turn the appropriate value needs to be assigned to that piece of data to allow effective data analysis to be able to take place.
Presentation
Before setting out on my first visualisation I had thought the most important part of the process would be collecting the data as this would surely drive the visualisation to present the most obvious pattern or trend. However, as I played around with different formats and layouts for my visualisation, I began to appreciate just how different the same information could be presented depending on how I chose to represent each piece of data, or where I grouped and bunched pieces of data together. When choosing the presentation format I appreciate I was choosing what information I felt was the most important, I was assigning agency to the findings and presenting what I felt was the key observation or theme of the data analysis.
Considering my experience of everyday working practices in education there is therefore a need to be wary of information that is presented as finished conclusive analysis alongside recommendations based on these findings. Was the data the real driver for change or did the desired outcome drive, consciously or subconsciously, the direction of the analysis and presentation to best suit the needs of the person or organisation producing the information?
On the flip side, even with the best intention of presenting information as clearly and accurately as possible there is still a potential issue that the format is not suitable for non-digital literate individuals to interpret. Representing numerous pieces of data visually could lead to one piece of information being favoured over a different piece therefore skewing the message the visual information is trying to convey. These different interpretations could lead to actions which are not the best resolution to a situation.
Can we rely on data?
With data permeating so many aspects of our lives it is perhaps worth asking ourselves if we should be concerned about increased ‘datafication’, Williamson et al, (2020) in our lives. The increasing reliance and pervasive use of data highlights the importance of understanding the variety of actors, the varying degrees of agency, and the complexity of entanglements between these. This allows us to acknowledge that data can be used to inform and provide useful insights, whilst still applying what Fontaine (2016) would refer to as a ‘healthy scepticism’ of data. Therefore, caution should be applied, with questions being asked at key stages in the processes before we can accept and benefit from these insights. However, as Selwyn & Gašević (2020) suggests there could be gains in education for teachers who could be empowered by educational data. There also needs to be an acknowledgement however that too much interference, or monitoring by teachers or institutions, can lead to overload on learners or teachers which may inadvertently change pedagogy and policies, which may not be of benefit to all. Therefore, successful use of data in education relies on finding a balance between the actors, agency and their entanglements.
References
Fontaine, C. 2016. The Myth of Accountability: How Data (Mis)Use is Reinforcing the Problems of Public Education, Data and Society Working Paper 08.08.2016
Kitchin, R., & McArdle, G. (2016). What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets. Big Data & Society, 3(1). https://doi.org/10.1177/2053951716631130
Selwyn,N,. & Gašević, D,. (2020) The datafication of higher education: discussing the promises and problems, Teaching in Higher Education, 25:4, 527-540, DOI: 10.1080/13562517.2019.1689388
Williamson, B. (2017) Big data in education: the digital future of learning, policy and practice, Sage Publications.
Williamson,B,. Bayne,S,. & Shay, S,. (2020) The datafication of teaching in Higher Education: critical issues and perspectives, Teaching in Higher Education, 25:4, 351-365, DOI: 10.1080/13562517.2020.1748811
