Block 3: Week 10

Week of Engagement

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.

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The visualization below represents and captures the different platforms on which I engaged with the students, I also tracked the different issues that prompted the engagements, the time of day it happened, and whether or not my response was immediate or delayed.

Findings from Visualization

Upon observation, I realized that over 60% of my engagements with students were via email and google hangout. The question I had in mind was whether or not this outcome was based on the efficiency of the platforms or because it was our organizational culture and engagement structure. Just as argued by Ozga, sometimes the use of data for policymaking is not sufficient when compared to expert knowledge because there could be several hidden information that the data might not communicate and thus decisions might be taken that further harm students and institutions. (Ozga,2015). Although using only expert knowledge to create policy has its limitation, the use of a combined system of data as well as expert knowledge can bring about a shift in the accuracy and efficiency of policy created by governments.

From my visualization, I also realized that engagements during the morning and afternoon hours of the day tend to get an immediate response as opposed to requests made during the night hours. Such data in the hand of an administration can help inform policy around faculty students communications timings and efficient platforms. I might not have captured enough information from which to build a policy but high volume of data collection in this regard can go a long way in helping institutions observe patterns that can help to build systems that will boost performativity and enhance governance (Williamson, 2017).


Ozga, J., 2015. Trust in numbers? Digital Education Governance and the inspection process. European Educational Research Journal, 15(1), pp.69-81.

Williamson, B., 2017. Big data in education. London: SAGE Publications.

One reply on “Block 3: Week 10”

Another really effective dataviz. It is very clear why having some of this data could be useful to a teacher, or perhaps also to a school leader. But I like the way you have drawn on Ozga to acknowledge the limitations of this kind of data, particularly if it is used to assess “performance”. As you prepare for your final dataviz and reflections, perhaps you could look again at the Ozga piece and what she says about “thin description”, and the ways simplified, accessible data get used for making policy decisions or to decide about how to govern schools/teachers. You’ve acknowledged that “expert knowledge” is required for consequential decision-making, but increasingly it seems as though “expert knowledge” and contextualized explanations are squeezed out by thin data descriptions. I look forward to your final dataviz and reflections next time.

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