Reflecting these past three weeks, I noticed that the data visualisation task gave me much space to explore and examine different ways of ‚learning’ with data. For the first time since the IDEL course, I felt the liberty of expressing my ideas freely, which was a bit absent from other courses.
Working through the readings of the past weeks and looking into data visualisation, one thought emerged over and over again: We have to question what enhancement through learning with data and learning analytics really means: improvement, growth, better performance, more self-confidence in learning skills?
What Knox (2017) describes as “Black Boxing” of learning analytics became increasingly apparent over the course of block 1 to me. I made a conscious decision on what data to gather, how to visualise it and how to interpret these data. However, when we talk about big data and learning analytics, this process is hardly discernible. Which leads to the “tensions between increasing student agency in making learning-related decisions and ‘datafying’ students “(Tsai et al. 2020, p.554) and whether students are still able to make data-driven decisions.
After the second week and the insights into my fellow students’ data collection, I realised how diverse data can be (Kitchin, 2014) and what influences our decisions derived from data collection and interpretation.
I recognised the importance of agency, mentioned by Tsai et al. (2020), especially towards the end of the first block. Building a conscience for data gathering helped me differentiate between data that I share voluntarily (such as my heart rate), and data gathered continuously (such as where and when I access my devices), with or without my consent or knowledge. Being aware of data collecting can and will influence my behaviour and activities. For example, it increased my motivation to work out because I knew I tracked my heart rate. Likewise, with this knowledge in mind, I believe that my studying habits and performance can change due to learning analytics when I am aware that data about my student persona and my activities online are observed and collected.
Moreover, the past three weeks expanded my knowledge of conveying and visualising information. Colour, layout and space all carry meaning and should therefore be carefully considered in data visualisation!
An exciting area that emerged from these weeks of my data visualisation is the importance of “‘ bodily’ and ’emotional’ data” (Knox et al., 2020, p.42) and how they influence and change my perception of learning. Capturing the social-emotional learning, as described by Knox et al. (2020), could therefore become of growing significance and can help “teachers in fusing the social dimensions of learning with data-driven recommendations” (Burger, 2016, p.14). However, the level of surveillance interlinked with this personalised data-driven learning is concerning and worth researching further!
Friesen (2019, p.146) identifies face-to-face communication and dialogue as essential for a shared vision, and Tsai et al. (2020) highlight the value of questions for possibilities of personalisation. Against this background, I am looking forward to exploring more of what Biesta describes as ‘learnification’ and the “disappearance of the teacher” (2012, p.35) over the following three weeks in Block 2 ‘teaching’ with data.
References:
Biesta, G. (2012). Giving teaching back to education: responding to the disappearance of the teacher. Phenomenology & Practice, 6(2), pp. 35-49. doi.org/10.29173/pandpr19860
Bulger, M. (2016) Personalized Learning: The Conversations We’re Not Having. Data & Society working paper. Available: datasociety.net/pubs/ecl/PersonalizedLearning_primer_2016.pdf
Friesen, N. (2019) “The technological imaginary in education, or: Myth and enlightenment in ‘Personalised Learning.” In M. Stocchetti (Ed.), The digital age and its discontents. University of Helsinki Press.
Kitchin, R. (2014). ‘Conceptualising Data’. In The data revolution: big data, open data, data infrastructures and their consequences. Los Angeles: SAGE
Knox, J. (2017) “Data Power in Education: Exploring Critical Awareness with the “Learning Analytics Report Card.” Television & New Media 18: 734–752. doi:10.1177/1527476417690029.
Knox, J., Williamson, B. & Bayne , S. (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
This thoughtful and engaged commentary demonstrates your close attention to key issues emerging from your reading of the course texts, and your reflective approach to the dataviz tasks. I agree that issues of bodily and emotional data are really significant, and it would be great to see your critical thoughts further develop in the next block. How could it affect teachers to receive data about students’ ’emotional states’? How are such embodied data possible to capture? You’ve already shown in your weekly reflections and the block commentary that you are attuned to the cascade of decision-making that goes into producing even a quite simple data visualization. I’m also impressed with the way you are approaching all these issues. Nothing here is simple — data may provide valuable insights, but also become surveillant. Access to online resources may be essential for education, but also demands personal identification and consent to share information. These are the kinds of tensions, often hard to reconcile, that make the subject of data in education so important.