The visualisations during this course helped me to understand the importance of the data that is collected and that the way in which it is visualised is of similar if not more importance. Visualisations or dashboards are how most people will view and interact with the collected data which makes these a powerful tool in learning, teaching and governing. The data and dashboards normally come as a package from the vendor of the platform and as such the decisions about what data is collected and how it is visualised have already been made.
Through the different blocks a realisation I made was that the data that is desirable depends on where you stand within the hierarchy of education. The hierarchy in my view is represented in the following:
While there are organisations that provide consultancy and services to the individual levels of the hierarchy it is those in the hierarchy that ultimately have decision-making power. The process by which the data that is desirable is decided upon are by the policies that the government implements. At each layer, the policies are refined which gives guidance to the lower tiers around what data is required to meet those policies. The systems that are implemented to gather the data are normally decided by each level, as long as the data that is returned is in the correct form to satisfy the policy. The data gathered is more specific moving down the hierarchy but as it is passed back up through the hierarchy the data becomes more generalised meaning that the context is removed.
Within the learning with data block the focus was on learning management systems (LMS) that are used to provide an online service to students similar to that of a classroom and from this service they provide information back to teachers, for example around student engagement and indicators that a student might be struggling with material (Bulger 2016). One issue with these systems is the lack of context in the collected data, context is important due to the life altering consequences a successful or otherwise student career may have on a student’s life. If context can be given to the data such as through wearables for example (Knox et al 2020), then it is possible that services may be able to be provided to the student that could turn an unsuccessful student experience into a successful one. Another issue is the design of the systems for an individual style of learning or personalised learning experience, the education system that is mostly still currently in place is focused on the class or group environment. This class environment has been refined for a long time and to shift to this new individualised method without much research and study is worrying as students may be getting an inferior education as a result.
The next block covered how the data was used in teaching and how best to represent that for teachers. This section showed the limitation that is inherent with such data which is best described by the following quote by Williamson et al (2020), “Data and metrics set limits on what can be known and what can be knowable. They define what is rendered visible or left invisible, thereby impacting on how certain practices, objects, behaviours and so on gain value, while others are not measured or valued.” I believe the reason for such weight being put behind this data is the companies offering these platforms as well as think tanks who recommend this new type of learning push the data as being a huge benefit and market it as a way of being proactive, so no student is left behind. The issue with this is the lack of research into this new form of pedagogy (van Dijck 2018) and the expertise required to understand the data as data science is an entirely separate field. In my view a possible solution to these issues would be some form of regulation of the education sector which would put clear rules in place around what is required to be collected and what can give that all important context that is missing. This role of regulation would be required at the Department of Education level so that is has clear standing in the hierarchy.
The final block discussed governing with the data for example setting policy or ensuring that certain policies are being applied which in turn will provide the data to prove the policy is working. In the past Departments of Education around the world were the main group who had influence on how education would be taught and would set the policies (Williamson 2017). As this new form of learning through platforms has taken hold governments do not have the expertise to create such systems especially in the Global South and so must work with external companies normally in the Global North to achieve scale and be able to interpret the data that is generated (Prinsloo 2020). This setup creates a lot of black boxes that no one person understands how it works and how the output from these systems came to be. It is in this section where the context is lost from the data and it is also within governing that this data is important to have so better decisions can be made.
At the end of this process the main takeaways for me were, context is key, data gathering has limitations, and education is more of a social activity than an individual experience. Teachers based in a classroom environment have the ability to be able to monitor and investigate challenges students may face in real-time and the teacher’s relationship with their students would allow for context to be known surrounding issues. As discussed above in block two when data is gathered unless it is a complete representation of the scenario there are always missing pieces to the information as we do not currently have a method of gathering up data from every aspect of a person’s life. Finally, the way in which online platforms are offering the ability to learn is more of an individualistic approach which removes a lot of the expertise learned about how to teach people. The group and social aspect of education has been around for hundreds of years and regardless of what new system is recommended that may offer benefits it should be heavily investigated and researched before the old methods are thrown away.
Bulger, M. 2016. Personalized Learning: The Conversations We’re Not Having. Data & Society working paper. Available: https://datasociety.net/pubs/ecl/PersonalizedLearning_primer_2016.pdf
Knox, J, Williamson, B & Bayne, S 2019, ‘Machine behaviourism: Future visions of “learnification” and “datafication” across humans and digital technologies‘, Learning, Media and Technology, 45(1), pp. 1-15.
Williamson, B. Bayne, S. Shay, S. 2020. The datafication of teaching in Higher Education: critical issues and perspectives. Teaching in Higher Education. 25(4), pp. 351-365.
van Dijck, J., Poell, T., & de Waal, M. 2018. Chapter 6: Education, In The Platform Society, Oxford University Press
Williamson, B. 2017. Digital Education Governance: political analytics, performativity and accountability. Chapter 4 in Big Data in Education: The digital future of learning, policy and practice. Sage.
Prinsloo, P. 2020. Data frontiers and frontiers of power in (higher) education: a view of/from the Global South. Teaching in Higher Education, 25(4) pp.366-383