Over the last three weeks, I have been engaged in a series of data collection and analysis to make sense of how I learn and the different factors and behaviors that might have influenced my learning dynamics. The goal was to use this data tracking approach to understand my learning style in a more personalized way keeping privacy in mind. (Bulger, 2016).
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Self data tracking to generate insights about learning was both exciting and foreign to me in the beginning. Data-driven technologies have been at the forefront of such procedures to determine student learning and now putting this task in the hands of the human as opposed to nudging human decision was fascination (Knox et al. 2019). The process of observing my learning with the use of self-tracked data made me feel empowered and trusted to understand insights about my learning and was not just an emotional irrational subject with actions and behaviors readable and modifiable by machines and algorithmic processes (Knox et al. 2019). However, I realized this supposed empowerment also came with a certain level of responsibility on the part of the student.
Over the past weeks, I realized that the burden to decided the type of data to collect, how to collect it and what analysis to make rested solely on me and it was reassuring that I had a level of control over what gets out thereby exploring my agency (Yi -Shan Tsai et al. 2019). However, I needed to demonstrate a level of discipline and self-management to be able to attain the laid out goals. Pondering upon this now begs the question at what level of their learning process should students really be trusted to take full control of their own learning?
This block also gave me the opportunity to learn a lot about myself through the quantified learning approach. I tracked data on various elements of my learning such as how my emotional state influence my productivity, how much time I generally spend on a task, my preferred times of working, the dynamics of my learning spaces, and how external factors influence my learning. Quantifying all of this information gave me a sneak peek into who I am and how that has influenced my learning approach (Eynon, 2015).
I am clearly aware of the complex nature of learning and reducing it to a set of quantities was something I grappled with all through the weeks. I thought about the flaws in my data collection process or the consciousness to leave out certain data because I dimmed them too personal to be revealed or my inability to measure certain elements of my learning. All of these made me understand that for every analysis there was this tension of reliability over richness as long as the tracking was manual (Eynon, 2015).
Finally, I came to learn over these weeks that sometimes the analysis made might be in contradiction with an already existing perspective about learning. When this happens I was always in a dilemma as to what elements to believe the data insights or my highly positive perception of my learning. In any case, I understand there are many circumstances influencing data that are not taken into consideration or just unmeasurable and all of this can influence the result but when the data and perception work together one can definitely have some insight about their learning.
Bulger, M., 2016. Personalized Learning: The Conversations We’re Not Having. Data & Society, [online] pp.1-29. Available at: <https://datasociety.net/pubs/ecl/PersonalizedLearning_primer_2016.pdf>
Eynon, R., 2015. The quantified self for learning: critical questions for education. Learning, Media and Technology, 40(4), pp.407-411.
Knox, J., Williamson, B., and Bayne, S., 2019. Machine behaviorism: future visions of ‘verification’ and ‘datafication’ across humans and digital technologies. Learning, Media and Technology, 45(1), pp.31-45.
Tsai, Y., Perrotta, C. and Gašević, D., 2019. Empowering learners with personalized learning approaches? Agency, equity, and transparency in the context of learning analytics. Assessment & Evaluation in Higher Education, 45(4), pp.554-567.