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Final Reflection

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.

My first impression after being introduced to the data visualization task was that it will be a ride in the park. However, after the first few weeks, I quickly realized being placed at the center of the data analysis process from determining what data to capture to how the data will be represented whilst maintaining a high level of privacy was both critical and challenging (Bulger, 2016). The blog visualization activities gave me insight into the use of data under the three themes of learning, teaching, and governing which I found to be quite interrelated after several weeks of visualization. In this final reflection, I will discuss the key takeaways from each block and explain how the course impacted my learning.

Learning with Data 

Learning with data block was the first opportunity to self-track my learning in order to make an informed decision. The process of observing my learning with the use of self-tracked data made me feel empowered and trusted to understand insights about my learning and was not just an emotional irrational subject with actions and behaviors readable and modifiable by machines and algorithmic processes (Knox et al. 2019). One of the things that stood out for me was the datafication of higher education and how institutions try to explain learners through visualizations and dashboards (Williamson et al. 2020). The use of platforms and digital technologies continue to make strides in education and their relevance is seen in multiple ways. However, the continuous use of these technologies has equally reduced learners to data points on a dashboard, limited teacher’s pedagogical practices as well as reduce the complex nature of learners and learning to set of quantities in order to enforce performance measurement (Enyon, 2015).

In spite of the benefits gained from the use of digital technologies in education, it is imperative to know that data in itself is not objective, truthful, and neutral as is often assumed (Kitchin, 2014) ut rather it is partial, selective, and representative. Thus, in order to use it in a sound concise manner, data infrastructure needs to be in place that governs the design of digital technologies for education,  as well as the stakeholders involved and their individual roles clearly spelled out. This way even if machines are tasked with learning proper attention will be given to how learning itself is theorized and encoded (Knox et al. 2020)

Teaching with Data.

The teaching block opened up some interesting conversations in my mind about the issue of control when it comes to data-based technologies and the use of data for teaching. How much control does the teacher have over what data is collected about the learners? What does the teacher do if the learning platforms provide data that might seem irrelevant to the teacher or does not align with his pedagogical practices? These and many others are some of the challenges that the teacher faces when teaching with data. 

In my visualization for week 6, I noticed there are elements of teaching which are otherwise overlooked that could greatly impact teaching and learning. Hence I tracked student engagement beyond performance and engagements on digital platforms but on social and interpersonal aspects of learning and teaching (Williamson et al. 2020). This social and interpersonal relationship-based aspect of student learning can provoke great reforms in education if properly explored and harnessed.  Through this lens, I realized the limitation of most digital technologies for education as they focus on only quantified elements of learning and ignore aspects that can foster more efficient personalized learning (Tsai et al. 2019).  

As much as we encourage the use of digital technologies and data dashboards for teaching, it is imperative that teachers understand how to manage and use the tools to interpret results presented and thereby benefiting from the platforms (Brown, 2020). Van Dijck, argues further that the use of digital platforms is meant to improve the learning system and not have teachers focusing on dashboards instead of classroom activities that might give more insight into student learning (Van Dijck, Poell and De Waal, 2018). They further alluded that different platforms encroach into the educational domain bringing their own values which may not be aligned with educational values.  Since most platforms are designed to enrich the platform owners and not necessarily to improve education, they are gradually changing education from a public good meant to develop society to a more business profit gain concept actualized at the expense of the individual consumer. (Van Dijck, 2018).

Governing with Data

Crowning the journey with the use of data for policymaking which affects both learners and teachers was just amazing. It showed the interrelatedness of the data across all themes. Key insights from governing with data are the idea that most times data does not tell the entire store and therefore needs to be further explored to avoid giving a thin description to what could have thick qualitative complexity.

 (Ozga, 2016). There are benefits to using data for policymaking because it can enhance fast policy-making and curb the slow-paced political approach (Williamson, 2017). Although data has made it possible for policymakers to rationalize decisions and actions based on numbers, it also should be done with a high sense of accountability knowing that they owe a duty to all stakeholders involved (Anagnostopoulos, 2013).

In conclusion, the data visualization blog activities have been a great avenue for learning about myself and most importantly about the nature and use of data in our daily lives. Through the blog activities, I have come to see that there are always multiple perspectives to data and should not be viewed in one dimension. Also, it has inspired me to embraced the criticality of data and how it can be presented in more fun and creative ways to tell a great story about learning, teaching, and governing.

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.

Bulger, M. 2016. Personalized Learning: The Conversations We’re Not Having. Data & Society working paper. Available: https://datasociety.net/pubs/ecl/PersonalizedLearning_primer_2016.pdf

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

Fontaine, C. 2016. The Myth of Accountability: How Data (Mis)Use is Reinforcing the Problems of Public Education, Data and Society Working Paper 08.08.2016.

Kitchin, R. 2014. The Data Revolution: Big data, Open Data, Data Infrastructures and their Consequences. London: Sage.

Knox, J., Williamson, B. and Bayne, S., 2020. Machine behaviourism: Future visions of ‘learnification’ and ‘datafication’ across humans and digital technologies. Learning, Media and Technology, 45(1), pp.31-45.

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

van Dijck, J., Poell, T., & de Waal, M. 2018. Chapter 6: Education, In The Platform Society, Oxford University Press

Williamson, B. 2017. Digital Education Governance: political analytics, performativity and accountability. Chapter 4 in Big Data in Education: The digital future of learning, policy and practice. Sage.

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.

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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. 

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.