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Governing with Data

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. 

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As I carried out my first visualization for the week on gathering data about student daily activities in order to build a policy on students’ wellbeing, I realized how much personal data is sometimes required by policy agents in order to achieve their goal. It also begs the question of how reliable is the information being used for policymaking and how much can we trust it if policymaking is now solely dependent on numbers instead of expert knowledge (Ozga. 2016). In addition to the unreliability of the data being collected and the questions raised about the data collection process, access to such data can further infringe on the student’s privacy. There is a high level of intrusion into the student’s personal lives and if data agents are also using the policy solution for profit gains then it is difficult to trust giving out such data even if it was for a desirable outcome such as tracking the daily activities of college students to create a balance and boost performance (Williamson, 2017).

There is an increased need to track faculty-student activities by institutions to understand and create policies that boost the performance of students and thereby boost institutional credibility. In the second week, I surveyed interaction platforms between students and faculty in order to create a soft policy that enhances better communication. Through my analysis of this section, I further explored the changes that data processing software has had in educational governance. The notion that all educational problems were measurable, calculable, and knowable, and thus solvable (Williamson, 2017) has drifted the focus of solving educational problems with concise expert knowledge analysis to a much faster data-based solution which sometimes may not be the best approach to attaining desired results, bearing in mind that data collection and analysis can be sometimes subjective and limiting  (Ozga, 2015).

Furthermore, it is quite obvious the strides that big data and data mining have introduced into educational governance. In the words of Jenny Ozga, data mining has supposedly enhanced efficiency, increase transparency, enable greater competitiveness, and made easy the evaluations of schools and teachers (Ozga, 2015). It has also helped in creating faster policies that would otherwise be locked up in bureaucratic political slow-paced systems (Williamson, 2017). However, in the third week, I explored further what Jenny termed “thin description” the temptation to lean towards the more accessible and available data for policymaking as opposed to including the qualitative contextual complexities of data in order to paint the entire picture (Ozga, 2015).

In conclusion, the use of data in governing has been useful to the different stakeholders, from students, parents to the school administrations. Data continues to be a great compliment to expert knowledge in decision-making if properly handled. Data has made it possible for policymakers to rationalize some decisions based on numbers (Anagnostopoulos, 2013). It should also be understood that using data for decision-making is not an escape of accountability but rather a call to accountability to all stakeholders involved.

References

Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. 2013. Introduction: Mapping the Information Infrastructure of Accountability. In, Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. (Eds.) The Infrastructure of Accountability: Data use and the transformation of American education.

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.

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