End-of-course Dashboard & Final Commentary

Here is a final ‘learning dashboard’ to summarise my learning experience during this course. I would recommend to open the dashboard on a separate page, to see all the interactive elements. Link to see the Dashboard

Block 1: Learning with Data

In the block ‘learning with data’ I learned more about the key issues surrounding ‘data analytics’ and ‘personalisation in education’. Through the readings, I explored how new technologies offer the opportunity of ‘democratizing information and instruction’, where the classroom is a space where students can be creative and ‘pursuit their own paths’. (Bulger, 2016) The ‘myths of e-learning’ were explored by Friesen (2020), where personalised education can be better understood as a ‘dream’ that is still far from being achieved.

Through the creation of my data visualisations, I was able to understand more clearly, from the ‘data producer’ perspective, how data-centred educational software can constrain the way we think about learning. From my teaching experience, the learning-analytics features of many educational tools used in schools now a days, are embedded within the system, not always evident to its users. The use of students and teacher dashboards is often not questioned, but just accepted as an innate part of the digital tools.

After creating my first data visualisation for this block, one based on a dashboard we (partially) use at my school, was that I noticed more clearly how learning-analytics dashboards and data visualizations like mine do a lot of pre-interpretation of data for the users. From the choice of colours used to represent the data, to the questions asked, to the views and perspectives implied in the design of the system.

In this first block I learned how data can lead to very different accounts of human behaviour than what we might notice at first. On one hand, data-analytics might offer an unbiased view of what human behaviour (and condition) is, free from the personal perceptions and judgements of third parties collecting or processing the data. On the other hand, it might be argued that there is an implied bias in the design of the data-analytic tools, in the algorithms used to analyse the data, and even on the choices of what type of data is considered ‘valuable’ or ‘desirable’. If I had more time, I would have liked to continue exploring how teacher’s perception of the students’ might be shaped (and reshaped) through the use of learning dashboards and other forms of learning analytics, as well as the possible long-term consequences of these interventions.

Block 2: Teaching with Data

The data visualisations I created for this block involved the themes of digital life, screen time, and communication in digital environments. Through these visualisations I continued to explore the process of data creation, selection and analysis, and how the way data is collected, and what data gets collected, affect the way the data is interpreted and interacted with. Additionally, while creating a simple teacher dashboard and exploring existing dashboards, I was able to understand more clearly how the internal process of data creation and collection shape my ‘view’ of the data and the data subjects it represents. Although dashboards do not necessarily hide students from teacher, they do highlight a particular aspect of the student’s activity and learning. (Williamson, Bayne and Shay, 2020)

This block was particularly interesting for me, as it changed the way I see, know and understand the world through data, at least from the learning and teaching perspective. By creating a basic teacher dashboard and tracking some of my own behaviour in a dashboard format, I recognized how dashboards present its users in particular forms to generate particular responses. The behaviour of teachers and students might be shaped through the use of dashboards. The student and teacher’s attention is drawn to a particular (and measurable) aspect of the learning and teaching process, where a higher ‘value’ is placed in some aspects of the learning. While reviewing the data I tracked during this block, I noticed how I felt some sort of pressure to ‘please’ the algorithm and obtain a ‘good’ overall score in the system. I can imagine it would be a similar experience for students. 

This block I also attempted to explore how digitally-mediated communication in modern education, such as instant messages and video-calls, create a sense of instantaneity and encourage its users to be ‘always online’. 

Block 3: Governing with Data

This block we explored the topic of ‘governing with data’, the interdependence between government and knowledge (production and use), as well as different ways of digital governance and practice. According to Ozga (2016), there has been a drastic shift in terms of how human activity is understood and measured, and the ease by which vast amount of data can be collected and process sin all aspects of human activity. The rise of ‘data-led’ practices where ‘actionable data’ is privileged, pose the risk of reducing ‘creative thinking’ and limit the possibility of understanding the ‘fundamental problems and possibilities’ of human activity. In terms of digital governance in education, data-led government policies have lead to a more flexible and fast responding approach to educational policy. 

I reflected on the ‘pressure points’ I experience in my role as a ‘learning technologist’ or ‘digital coach’, especially between IT experts and the pedagogical experts. These two very different world views on education and technology, often struggle to understand each other’s perspectives, and seem to work and live in different worlds. The divide between those who ‘do’ tech and does who ‘do’ teaching, is still visible in many of the discourses surrounding education, and is reflected in the way the government, policymakers and educational technology companies treat and view education.

One of the key learning experiences for me in this block was reflecting on what my data visualisations say about my own views and perspectives on learning, teaching and governing with data. I was able to identify more clearly any biases I have in relation to teaching and learning with data, I questioned the way I consciously or unconsciously chose to represent data visually, and reflected on the data I decided to collect in the first place. I realised that sometimes my data collection and interpretation methods were rigidi and structured, similar to the systems I’ve used in the past to analyse data. In a way, my ability to collect, measure and interpret data has been shaped in some ways by the software I’ve used in the past. My way of thinking is ‘governed’ by the algorithms I’m surrounded by. 

Finally, I identified how me (and my students), might have internalized the ‘pressures’ of the software we use. This could be seen in different ways, from the perceived pressure to be ‘online’, the forms of instantaneous communication, to the many different digital tools I use in a ‘normal’ online lesson with my class.


References Bulger, M. (2016) ‘Personalized Learning: The Conversation We Need’, Data & Society.

Friesen, N. (2020) ‘The Technological Imaginary in Education: Myth and Enlightenment in ‘Personalized Learning’’, in Matteo, S. (ed.) The Digital Age and Its Discontents: Helsinki University Press, pp. 141-141.

Ozga, J. (2016) ‘Trust in numbers? Digital Education Governance and the inspection process’, European Educational Research Journal, 15(1), pp. 69-81.

Williamson, B., Bayne, S. and Shay, S. (2020) ‘The datafication of teaching in Higher Education: critical issues and perspectives’, Teaching in higher education, 25(4), pp. 351-365.

Leave a Reply

Your email address will not be published. Required fields are marked *