During the “Learning with Data” block, I focused my data visualization assignments on capturing data that would be relevant to a student. I captured three elements: distractions, learning spaces and emotions, with the intention to build a holistic view of physical, digital and emotional conditions of learning. For sure, more data should be captured to enable me to develop more realistic observations and findings and to construct more critical analysis regarding learning with data. Being the designer, the producer and the recorder of the data, made me believe that the transparency and openness conditions in producing authentic learning data (Tsai et. al 2020) are addressed. However, during the data collection phase, I noticed that it was extremely hard to be fully inclusive of all data while capturing and tracking data accurately and without bias. I’m reflecting on these concerns, hereafter.
The question here: how you ensure that all the needed data are captured? Although, it was a manual process, but there were data elements that were not captured, forgotten or neglected. With an automated data capturing system/technology, this issue could be resolved however designing the data collection triggers might not be as inclusive or well-defined. During personal data tracking, there were some automated data capturing, but there were many opportunities to change others or skip others. To build a learning opportunity from data-driven technologies, it’s important to capture comprehensive data.
Data may restrict the kinds of questions we can ask and the analysis and recommendation generated (Eynon 2015).
This is accuracy at all levels of data capturing, recording and analysis. The captured data might not reflect the real situation and could be subjective to the time, location, external factors and other factors. I noticed that my collected data captured had elements of intentional and non-intentional errors.
According to Eynon (2015): ‘there is likely to be a kind of trade-off between the reliability of the data that can be collected and the richness of what can be measured.”
This impacts the learning process and aspired benefits of learning analytics. The risk that it might be the opposite; jeopardizing learning outcomes and putting the learner at a disadvantage.
Bias was also infused in my data selection. It’s strange that one is biased to his/her own self, but the bias here as in the learning activities selection and choices I made before and after data capture. The question would be if I was using a specific technology to capture the same data, would it be the same? The answer is no!
From learning with data perspective, bias could be built within algorithms and predictive analytics which are designed to impact and shape the learning process and behaviours. So
“what constitutes the ‘correct’, ‘preferable’, or ‘desirable’ behaviours for learning” (Knox et al. 2019)?
I believe that these concerns could be solved by data-driven learning technologies. However, the question is how these technologies are shaping the learning process and what are the embedded design principles? As Bulger (2016) highlighted, the goal is to actually demand transparency, openness and accuracy of what is being collected and to understand the built-in assumptions and specifications to support students’ learning opportunities.
- Tsai, Y-S. Perrotta, C. & Gašević, D. 2020. Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics, Assessment & Evaluation in Higher Education, 45:4, 554-567, DOI: 10.1080/02602938.2019.1676396
- Eynon, R. (2015) The quantified self for learning: critical questions for education, Learning, Media and Technology, 40:4, 407-411, DOI: 10.1080/17439884.2015.1100797
- 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.
- Knox, J, Williamson, B & Bayne, S 2019, ‘Machine behaviourism: Future visions of “learnification” and “datafication” across humans and digital technologies‘, Learning, Media and Technology, 45(1), pp. 1-15.
- Bulger, M. 2016. Personalized Learning: The Conversations We’re Not Having. Data & Society working paper.