In the last block, we focused on teaching with data. My goal was to consider the data collection and visualisation through the perspective of an educator because my professional life is devoted to platform selling and creating dashboards for platform users. I also wanted to understand a bit more about the perspective of educators on what data is important and why.
In this block, the themes that emerged for me include:
- “Some data was better than no data – sometimes” (Brown, 2020)
- It’s important to know who the big players are, and dig deeper into why they may want to play in the education space (van Dijck et al, 2018)
- The data points collected are often behavioral and can be used for adaptive learning, but they may not always be directly correlated to learning (van Dijck et al, 2018)
- The data going in affects the outcome of the algorithms. Do educators have the skills and knowledge to determine how biased the algorithm may be and how to adapt their dashboards to it Brown, 2020)?
- The trend towards more datafication and commodification of education is changing the role of the teacher and perhaps how they are evaluated (Williamson et al, 2020).
In my own visualisations, I tracked sleep, emotions, and distractions. These, I believed, were things the teacher on the other side of a Zoom screen may want to be aware of as they could impact student engagement. The data points may give insight into the students well being, but the data points themselves may infringe upon the students data privacy as highlighted by van Dijck et al (2018) because they would require a minute by minute tracking of the students.
Working in technology, I have an assumed trust of certain players and distrust of others. In reflection, I asked myself:
If I was an educator, would I need these data points to influence my lesson plan, would I see them as superfluous, or would they change the way that I teach and ‘know’ my students?
The behavioral data points may assist from a gamification standpoint and lead to personalised, or adaptive learning, as highlighted by the education examples by van Dijck et al (2018); however, we have to think critically about what behavioral data points actually correlate with engagement and ultimately learning. In understanding how the majority of platforms are configured, I can attest that how we track data is limited. For example, engagement is likely being tracked from a simple mouse click. You can track that a video’s play button was clicked on and at what time the video was stopped, but unless you are video taping the user’s face as they watch the video, you don’t know what happened after they hit ‘play’.
Taking this a step further, why would we want to track every action? Well, from the technology perspective, you need to find a way to keep selling software. More data equals product enhancements and new technologies, which equals more revenue and happy shareholders. The data collected can also be sold for profit. At first glance, it is easy to trust the ‘Big Five tech companies’ (van Dijck et al, 2018). The marketing places a positive spin on the data collection, product enhancements and new solutions. It’s only when the data becomes creepy (i.e. speaking about something only for it to show up as an ad on Facebook two minutes later), or there’s a breach, that most become aware they’re being tracked and have a problem with it. Do we want children to be tracked every minute from the moment they enter the school system? Personally, I would hate it. Having old pictures and memories show up on Facebook is already more than enough. The readings and visualisations this week have made me reconsider if I would want this tracked by anyone other than myself.
Lastly, we should consider how the data collected and dashboards impact the role of the teacher and how they are evaluated (Williamson et al, 2020). As highlighted by Williamson et all (2020), the data points can become “proxy measures of the performance of staff, courses, schools, and institutions as a whole”. But is education a place where we should focus on customer satisfaction? Few K-12 students would be able to distinguish their anger at a bad grade due to possibly their own lack of preparation from their customer satisfaction of how the teacher taught the material and their skill set as an educator.
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), pp. 384-400
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.