For this block’s data visualization exercises, it was harder to collect data from a “teaching with data” perspective. My aim was to approach the block from three angles: role of a teacher, student performance measurement – dashboards and learning platforms.

For the first visualization, my focus was to reflect on one of the questions from the block’s overview “What are the implications of increased data tracking on the role of teachers in educational institutions?”  It was difficult to answer this question from “My Teaching Role” visualization alone; however, what I learnt from the data collected, is that measuring performance through specific data points can be misleading and they do not reflect accurate assessment without contextual information. For example, measuring time spent in activities, other than teaching, during working or “class” hours might lead to the conclusion that the teacher is not performing well or is wasting student teaching time. As outlined by Williamson and Shay (2020) regarding technologies that use “data-based” measurement which dictates what is visible to others and “impact how decisions are being made through automation and it affect the ways people feel, act and behave”.

This impact on people’s – teachers’ and students’ behavior I noticed during the second data visualization exercise “A week of Performance Tracking”, when my performance was being measured based on data captured against a predefined benchmark or target. In the middle of that week, I noticed that some activities were under performing so I acted on them and changed behavior to algin to the expected targets. Although, it was my own data and targets but visualizing the data allowed me to change course. With lack of in-depth understanding of where the data is coming from and how the targets are being defined, behavioral changes could have a positive or negative connotation from a teaching with data standpoint depending on what is being displayed and measured.  

This leads me to teaching dashboards and also the question regarding: “how do ideas such as “data-driven decision-making’ shape teaching practices, and professional responsibilities?” and how would data-driven teaching dashboards improve the learning process? In Brown’s (2020) case study, teachers had little knowledge of how dashboards can assist in teaching and/or academic planning. The answer could be in the lack of understanding of the information being displayed or how are data being captured and organized. 

To make proper decisions or effectively use teaching dashboards, teachers should be able to configure, define and manage targets and build their own practices in the learning dashboards (Brown 2020). This is based on the assumption that teachers have the required ‘data literacy’ to create proper decisions and interventions (Raffaghelli & Stewart 2020). The data literacy here is not related to technical skills and data science capabilities only, but a “broader epistemological frameworks than a technical, instrumentalist focus on performance management, efficiencies, or evidence can offer.” 

Learning platforms are generating large amounts of data (van Dijck et. al. 2018) leading to economic and commercial decision making taking little account the role of the teacher and specific learners’ needs. My third visualization assesses the various platforms for reading recourses, the question is how adaptable these platforms are to allow teacher’s intervention to revise and personalise individual learnings resources and inject new / revised material. 

In conclusion, the teaching dashboards and data-driven educational technologies are impacting teaching and the role of the teacher as he/she become increasingly “datafied subjects” (Williamson and Shay 2020).  I will close from the article by (van Dijck et. al. 2018): 

The changing role of teachers from classroom directors to dashboard controllers, mediated by numbers and analytical instruments, is a major issue; professionals may feel that the core of educational activities—assessment and personalized attention—gets outsourced to algorithms and engineers.

References 

• 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
• Raffaghelli, J.E. & Stewart, B. 2020. Centering complexity in ‘educators’ data literacy’ to support future practices in faculty development: a systematic review of the literature, Teaching in Higher Education, 25:4, 435-455, DOI: 10.1080/13562517.2019.1696301
• 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.
• van Dijck, J., Poell, T., & de Waal, M. 2018. Chapter 6: Education, In The Platform Society, Oxford University Press