The course requirement of selecting, recording, visualizing and reflecting discrete data points on a weekly basis for 9 weeks was definitely a huge learning curve with differentiated and interactive learning experiences. Thinking about what data to capture and how to represent them was a “learning with data” approach in itself. Lupi and Posavec’s ‘Dear Data’ project was an eye opener on hand-recorded data visualizations, but setting a high bar in terms of what data is available and how to generate interesting and creative ones while in a Pandemic lockdown. The exercises made me appreciate data more and realize the contemplations of data collection and visualization to what I am familiar to. In the first half of this blog, I would like to reflect on the data capturing and visualization learning experience; and in the second half, I will focus on how data visualization helped me comprehend the course objectives.
Data Visualization Exercise, Findings and Reflections
For each week, I adopted a process focusing first on building a plan of the data set to be collected and what would be a likely linkage to the theme of that week/block. The presentation took few iterations but then drawing it and reflecting on it became the creative and interesting part of all. Alongside my process of plan, define, collect, represent and reflect on the data here are some of the findings from the weekly visualizations.
- Scope definition: I started each week with a question in my mind for the data collection and, at certain times, the data took me in other directions. It is important to keep the objectives in mind but equally important is to look at the data with a fresh eye and to adjust the scope as needed.
“First, the purpose of learning analytics is not tracking. Second, learning analytics does not stop at data collection and analysis but rather aims at ‘understanding and optimizing learning and learning environments. Instead, there is a clear focus on improving learning.” (Selwyn & Gašević 2020)
An example was Week 4, “My Teaching Roles”, as I started the week with general data about what I do on a daily basis and then I shifted towards a “teaching” category of my role and I was able to reflect on the data visualisations not only from a role perspective but what does it mean to be monitored as a teacher.
“Teachers, too, are increasingly known, evaluated and judged through data, and come to know themselves as datafied teacher subjects.” (Williamson et. al. 2020)
- Iterative process: in many cases I have either added or changed data attributes during the collection process. This was either impacted by the lack of depth to allow for a better visualization or to improve the messaging on educational themes. Going back to the drawing board makes is interesting but was only feasible being hand-drawn. The implications of an iterative process from data systems point of view would not be that easy or flexible.
“Data and metrics do not just reflect what they are designed to measure, but actively loop back into action that can change the very thing that was measured in the first place.” (Williamson et al 2020)
- Data reliability: Being the sole producer/owner of the data, made me believe that the transparency and openness conditions in producing authentic learning data are addressed (Tsai et. al 2020). However, I noticed that it was extremely hard to be fully inclusive of all data while capturing and tracking data accurately and without bias. How reliable is the data being presented each week? Is a tough question to answer. I reflected on these in more details in the learning with data blog.
‘There is likely to be a kind of trade-off between the reliability of the data that can be collected and the richness of what can be measured.” Eynon (2015)
- Learning from others: the most fascinating part was looking into other’s data visualizations and reflections. In many cases, we are collecting the same data points e.g., drinking coffee, distractions, spaces, study material and etc., however, the vast differences in the approach, depth and artwork were insightful and demonstrated how similar data can be visualized in many different perspectives. A real testimony that data is not just data but hold personal preferences/biases, environments, locations and many other external factors impacting a data point like number of coffee cups a day.
“Data do not exist independently of the ideas, instruments, practices, contexts and knowledges used to generate, process and analyse them.” (Kitchin 2014)
Learning, Teaching and Governing with Data
Although every week/block had specific theme/readings, at many instances, I found that one can use the same data sets to interpret and tackle multiple themes. This came more into effect during the Teaching and Governing with Data blocks. At the end of the 9 weeks, I can easily say that the three themes are interlinked and interdependent and focusing on one without understanding the implications on the other two would impact how we approach data in the educational sector.
Looking into week 7 data visualization, A Week of Communication, it could be replicated for all three themes. From a learning with data perspective, the data can be used to define how a student understand his/her learning communications to determine effective methods to priorities and manage learning objectives. From a teaching with data perspective, the same data can be used to generate understanding of what are the effective means of communication and how students respond to each method. The same data to decide on the right communication method for each student. Finally, from a governing with data angle, the data can be used to govern learning and teaching communication platforms and set some policies on learning environments and communication methods.
Each block presented a set of questions related how data is defined, produced and analysed from educational perceptions in attempt to understand how current data-driven technologies and systems/platforms are impacting the overall educational governance including teaching and learning. The analysis and interpretation of data could be subject to different objectives and motivations not necessarily pedagogical ones, especially, when considering predictive and AI based modelling of educational data.
“Machines are tasked with learning, attention needs to be given to the ways learning itself is theorised, modelled, encoded, and exchanged between students and progressively more ‘intelligent’, ‘affective’, and interventionist educational technologies.” (Knox et al 2020)
There are benefits gained from the “datafication of Higher Education” when analysing educational data and gaining insightful knowledge/information. However, here are some persisting questions: what instruments are being used? what are the design principles? what educational expertise and knowledge used to design/build these technologies? What are the underpinning infrastructures? And Who are the actors?
These are comprehensive questions to further analyze in this blog but will conclude with the following from Williamson et al 2020:
“Datafication brings the risk of pedagogic reductionism as only that learning that can be datafied is considered valuabe […] There is a clear risk here that pedagogy may be reshaped to ensure it ‘fits’ on the digital platforms that are required to generate the data demanded to assess students’ ongoing learning.”
Here is a video of all my data visualisations.
- Ben Williamson , Sian Bayne & Suellen Shay (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
- Jeremy Knox, Ben Williamson & Sian Bayne (2020) Machine behaviourism: future visions of ‘learnification’ and ‘datafication’ across humans and digital technologies, Learning, Media and Technology, 45:1, 31-45, DOI: 10.1080/17439884.2019.1623251
- Kitchin, Rob. The Data Revolution: Big Data, Open Data, Data Infrastructures & Their Consequences. Sage, 2014.
- Lupi, Giorgia, et al. Dear Data. Flow Press Media, 2018.
- Neil Selwyn & Dragan Gašević (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
- Rebecca Eynon (2015) The quantified self for learning: critical questions for education, Learning, Media and Technology, 40:4, 407-411, DOI: 10.1080/17439884.2015.1100797
- Tsai, Y-S. Perrotta, C. & Gašević, D. 2020. Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics, Assessment & Evaluation in Higher Education, 45:4, 554-567, DOI: 10.1080/02602938.2019.1676396