The focus of this week was to record data on different platforms or sites I visited during the week, the purpose of my visit, and the mode of engagement. The data was recorded over a six days period to be able to capture as many entries as were possible.
In order to enhance the readability of the visualization, I was selective of the platforms from which I recorded data, and I also grouped the purpose for visiting the platform in a way that I can be able to capture as many as possible and finally I recorded the mode of engagement as this can inform teaching plans for especially teachers using digital technologies and tools during teaching.

The visualization above shows a summary of what my data recording looks like for the week. Despite the growing debate on the willingness of teachers to adopt digital technologies and tools for teaching, several institutions still largely require faculty to make use of data dashboards provided by these tools to inform their teaching (Brown.2020).
From the visualization, I realized that my learning engagement was not just fixated on the course learning site (Module) and blogs but was transferred to unconventional learning platforms to further enhance my learning. From this observation, I deduced that one major challenge of data dashboards to teachers is that they are unable to capture every element of student learning thereby giving the teacher an incomplete representation of the entire student learning experience (Williamson et al. 2020). An important element of this visualization in the hand of a teacher in the virtual space is the mode of engagement. Course materials provided in the learning management system can take multiple forms or expressed in different modalities. Therefore record the mode of engagement will support the teacher and designer in choosing the most suitable materials to enhance student learning.
Additionally, most data-driven technologies are programmed to only report certain elements of the learning and this also affects what ends up in the hand of the teacher. Hence, the burden falls on the teacher to determine how much more data is needed to make an informed decision about a student otherwise the lack of data literacy might affect the conclusions about a student immensely. (Williamson et.al 2020).
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.
Tsai, Y., Perrotta, C. and Gašević, D., 2019. Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics. Assessment & Evaluation in Higher Education, 45(4), pp.554-567
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.