This week I have collected data related to platformization (van Dijck et al. 2018). Parents and students are worried about data collected by various online platforms. Questions raised before data collection.
- How many times in 5 days am I going to receive cookie requests?
- What do I accept?
- What do websites/apps ask me to accept?
Findings: 1. I have received 9 cookie requests.
I drew 9 ‘cookies’. Every ‘cookie’ represents one cookie request. The number of cookie requests surprised me. On average, I have received 2 requests a day.
2. I always tried to reduce to the minimum the amount of data I would provide but still be able to use a website/app.
3. Most common: Data use for security, performance & personalised ads purposes.
Various ‘candies’ were drawn inside the ‘cookies’ to represent different data types that I was asked by the websites/apps. Different ‘candies’ show differences between various data purposes.
Visualization design choices: digital ‘cookies’ were drawn as eatable cookies to show what I want to represent clearly. ‘I chose candies’ with different colours to represent differences between data purposes. ‘Cookies’ themselves are not fully coloured and more like curved lines to represent a type of suffering that data provider can receive from providing personal data. This type of data representation can be useful to teachers because dashboards often impose limits (Williamson et al. 2020).
Data that cookies can provide can be useful to teachers. Datafication and personalisation should be followed by privacy, surveillance and data security and pedagogical foundations (van Dijck et al. 2018). It is essential the vast majority of learners data would be analysed by educators who concentrate on learning-related loops of experimentations, reflections, and change rather than profit. Teachers would not need to focus only on analysis but consider broader societal, institutional goals & individual and contextual practices (Raffaghelli & Stewart, 2020). Strong data literacy skills would need to be developed among teachers (Williamson et al., 2020).
However, teachers should also analyse provided data ethically because it can transform into student surveillance (Brown, 2020).
Brown, M. (2020). Seeing students at scale: How faculty in large lecture courses act upon learning analytics dashboard data. Teaching in Higher Education, 25(4), 384-400.
Raffaghelli, J. E., & Stewart, B. (2020). Centering complexity in ‘educators’ data literacy’ to support future practices in faculty development: a systematic review of the literature. Teaching in higher education, 25(4), pp.435–455.
van Dijck, J., Poell, T., & de Waal, M. (2018). Chapter 6: Education, In The Platform Society, Oxford University Press.
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.