The nine weeks data visualization journey has been an insightful and exciting opportunity to explore different activities made possible through the use of data. The data visualization activities open me up to huge learning opportunities from the process of thinking about what data to measure down to the analysis and representation of what has been measured. I have done a lot of work with data prior to this course but my greatest inspiration came from the use of hand-drawn visualizations to represent data in a creative and less conventional way. It gave me the opportunity to explore and present the complexities of data in a more friendly and easy-to-understand manner.
The governing with data block was rather insightful as it opened up the conversation on the use of data for policymaking in different spheres of life. What was especially interesting to me was the fact that although the reflections and visualization were made from everyday activities, they still proved relevant to governance in effect. During this block, I tracked activities on student wellbeing, faculty-student interaction, and finally some hospital staff performance. The introduction of big data and advanced technology tools has had a significant effect on the use of data for policymaking (Williamson, 2017). This phenomenon has thus moved policymaking from the hands of more traditional political actors and open it up to multifaceted global actors both in the public and private domains. The continuous access to data or data-producing facilities leads to the increasing growth of data-based policy solutions.
This week was a rather challenging one because I spent most of it in the hospital due to ill health, however this did not stop me from capturing data for my blog activity. As I lay on my hospital bed and wondered what data to capture to be able to still add value to my reading I noticed the visits of different medical practitioners in the ward to look after patients’ needs. There it was. I decided to track every time a medical personnel came into the ward, the duration of their visit and the time of day they visited. Since I was weak and asleep most of the time I told my kid sister who was mostly with me to do the tracking.
One of the major elements of governing with data is the ability of institutions to use tracked data to improve institutional management. In order to further harness this reality, this week I decided to track my engagements with learners in a course I facilitate. The rise and prevalence of big data have given governments and other data management organizations the ability to continuously track and monitor individuals and use such data for governance (Williamson, 2017). Hence, I wanted to identify patterns from my visualizations that can be used to inform some elements of policy around student engagements with faculty in the educational setting.
One of the most basic struggles of any student enrolled in higher education is maintaining a balance between work, recreation/fun, and sleep. There was a saying in college that one can never have all three and that we always have to sacrifice one to ensure productivity in the other two. This week I decided to tract these three activities of a regular student and see if a certain pattern can be observed from it and used to create a form of guidance or policy for students enrolling in university programs.
The transition from learning with data to teaching with data was not smooth for me. Having taught in higher education for a couple of years I believed it would be like a ride in the park but it was far from my expectations.
One of the most interesting concepts I encountered during my readings in this block is the idea of “Liquid Surveillance”. Normalizing the datafication of the different elements of our lives thereby generating a constant flow of data about individuals (Williamson et al. 2020). I am conscious that most of this data collection is enforced by digital technologies, however, I wanted to explore the intentionality of data sharing from an individual perspective.
This week I decided to track all the times I intentionally withheld information in different spaces of my life namely: work, study, personal, and social life. This was important to track because of the increasing concerns of data privacy and how it affects the willingness to share personal data. The image below shows my visualization and some observations I picked from it.
Findings from my visualization
The first thing I realized was how unwilling I was to share personal data irrespective of the engagement space. It was interesting that whenever providing personal data was optional I quickly took the opportunity to skip especially when using digital technologies. It didn’t matter whether I was completing a recommendation for a student or I trying to create a profile for a social media platform (Raffaghelli and Stewart, 2020).
Another observation from my activity this week was that I was less trusting with sharing data online than in face-to-face interaction. However, given how datafied all our systems of learning and teaching have become, it is almost impossible to avoid sharing information since most times we are not given the option to do otherwise (Sarikakis and Winter, 2017). Also, this idea that the data recorded is stored and can be used in the future to the benefit or the detriment of the individual raises an even bigger concern about data privacy beyond just the collection and use of data but the ethical implications of data sharing and management (Raffaghelli and Stewart, 2020).
Reflection on this data in teaching
Teachers should understand that given the option, students are not readily open to sharing personal information because of the raising awareness of data privacy and its effects on the students in the future.
Teachers are mostly in possession of individual student data collected either through the admission platforms or just learning activity. It is important to understand how improper management of said data can have a detrimental effect on students in the future. Hence the pressing need for teachers to undergo new forms of data literacy beyond the collection and interpretation of data (Raffaghelli and Stewart, 2020)
Finally, teachers should understand that since individuals are not willingly open to sharing personal data, dashboards and other learning management tools might create analyses that are false due to manipulated data from learners.
References
Raffaghelli, J. and 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), pp.435-455.
Sarikakis, K. and Winter, L., 2017. Social Media Users’ Legal Consciousness About Privacy. Social Media + Society, 3(1), p.205630511769532.
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.
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.
This week I decided to test how different events impacted my learning. I knew from the beginning that it was going to be a very eventful week, hence I capitalized on that and tried to do my analysis around this fact. Considering the fact that this was my birthday week, my sibling’s graduation, and my relocation to a new house I knew it was definitely going to have a toll on my learning. Hence I decided to record data to investigate how the different events and my emotions at different times will impact my productivity. Most of the events were towards the end of the week and others spread across the week so it was easy to have valuable data to record all through situs slot gacor.
Objectivation
To properly understand what was going on I tracked my academic engagements and what event was distracting me during my engagement in that particular event. I decided to represent the activities as follows:
Downward and upward cone to represent the learning activities
Sun moon and star represent birthday, relocating, and graduation respectively.
The greater than, less than and, equal to signs represent high, low, and moderate productivity respectively.
I used different emojis to represent different emotions. This was an attempt to datafy and personalize my learning to a degree.
Findings
The visualization did not give an explicit correlation between my emotions and my productivity is given a particular event.
If it was hard for me to link my emotions and their impact on learning I wonder how a machine will achieve this
I had assumed that if am tired and frustrated I would definitely perform low but I was surprised that it was not the case. This shows me that sometimes not so positive emotions do not necessarily have a negative impact on learning
Also, I found that just because one is happy or calm does not mean they will have high productivity
In relation to emotions and events, I realized that positive emotions were mostly linked with my birthday events. This showed the value of the individual
