Month: March 2021

The core subject that I have been researching in the past few weeks is ‘online cookies’. In this week’s visualisation, I am also showing various cookies that I have received in the past few weeks. I was trying to analyse where my data goes. Is it really just North America (Prinsloo, 2020)? The circle represents countries in which websites that I provided my data through cookies are based. Countries are represented by colours in their national flags to make it easier for a reader to distinguish countries.

The circle represents 12 occurrences of ‘cookies’ notifications on various websites.

Findings

1. Most of my data go to Lithuanian companies. It probably makes sense because I am Lithuanian, and often I read the news in my native language.
2. Only 25% of my data goes to Lithuanian companies, 75% goes to other countries. Is it fair to build profit for companies from other countries?
3. Less than 17% of my data goes directly to North American companies. Therefore, I would not agree with Prinsloo’s (2020) claim that the vast majority of data goes to North American companies.

The Lithuanian Educational Ministry can use this data by thinking of ways that data would be available for Lithuanian companies and collaborating with them (Anagnostopoulos et al., 2013). This way, more informed educational decisions in Lithuania could be made. This would empower to make data producers themselves able to use their data, rather than only tech giants (Beer, 2019, cited by Prinsloo, 2020).

In the past few weeks, data visualisation practice helped me realise how many responsibilities an individual collects, analyses, and visualises data. The process involves various factors, and it needs a high responsibility because future decisions based on data are made. Therefore, the wrong implementation of the process can lead to various detrimental effects. Also, individuals involved in data-related processes are often pressured to simplify provided data (Williamson, 2017) and make it user friendly (Anagnostopoulos et al., 2013). This can even increase the negative effects.

Data and accountability are closely related (Williamson, 2017). It shifts control of education to policymakers and bureaucrats over educators (Fontaine, 2016). Higher data collection results in higher accountability (Anagnostopoulos et al., 2013). Educational institutions need to show themselves in an intimate account. This is what I have done in Week 10 of my data visualisations were presented the interactiveness of my lessons. It can often discourage educational institutions from collecting data and demotivate teachers who can feel surveillance.

Data shift governance from qualitative to quantitative (Williamson, 2017). It enables fast policy creation. I had experienced it myself when in Week 10, I needed to represent qualitative data quantitatively. How can quantitatively be represented experiential learning (John Locke, cited by Fontaine, 2016) or learning pictured in Platonic epistemology? These days educational systems highly rely on various testing systems. However, even testing systems themselves encourage not solely rely on tests (Fair Test, 2007, cited by Fontaine, 2016). Various schools highly focus their curriculum on what is necessary for tests (Anagnostopoulos et al., 2013), even though these tests often do not represent the whole range of skills students need to develop.

Data can help to govern. However, it needs to be considered with slight scepticism and remembrance of its non-neutrality (Fontaine, 2016). Policymakers also need to evaluate various risks when they highly rely on data (Anagnostopoulos et al., 2013).

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.

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.

Ozga, J. (2016). Trust in numbers? Digital Education Governance and the inspection process. European educational research journal EERJ, 15(1), pp.69–81.

Prinsloo, P. (2020). Data frontiers and frontiers of power in (higher) education: a view of/from the Global South. Teaching in higher education, 25(4), pp.366–383.

Williamson, B. (2017). Digital education governance: Political analytics, performativity and accountability. In Big Data in Education: The digital future of learning, policy and practice. 55 City Road: SAGE Publications Ltd, p. 65.

Interactivity is essential in various domains (Capriotti & Moreno, 2017).

I have collected data related to my lessons and how interactive were they. I consider it is presenting myself in an ‘intimate account’ (Anagnostopoulos et al., 2013) with high accountability (Williamson, 2017) because my workplace highly encourages interactivity. Such data can help make governance-related decisions. Educational institutions often see data as a solution to problems (Ozga, 2016).

Each circle represents a lesson. They were chosen instead of squares because of smoother looks. Circles are separated into 2 age groups (children & adults). Children are until 14 years old, and adults are 14 years old and older. An interactive lesson is defined as one where various online interactive educational resources are used, such as ESLBrains, Kahoot!, Wordwall & slides provided by my workplace. The colours of each interactive activity were chosen based on the materials logo or most occurring colour. If a circle was empty, it means that nothing that can be considered as an ‘interactive resource’ was used.

Key finding: 66% of the lessons included interactive material. 25% of the lessons included 2 resources. Various platforms were used a similar amount of times. I have used fewer games with adults than with children.

I did not consider the overall amount of time in the class that it was interactive. That would have probably been even more effective. Many other qualitative factors could have been considered. However, a quantitative approach was chosen when presenting data. A clear shift from qualitative to quantitative governance is seen among educational institutions (Williamson, 2017). Many educators believe that data can speak for itself (Anderson, 2008 cited by Ozga, 2016).

This visualisation can be used by the governance at my school. It can show how interactive my lessons are. Global North tech giants such as Google or Microsoft can use it too. These companies already see African countries as ‘data frontiers’ (Beer, 2019, cited by Prinsloo, 2020). They are highly influential in the Lithuanian market as well. Collected data can create a high profit (Ozga, 2016). Who has the power to use the collected data? Tech giants or data producers themselves (Beer, 2019, cited by Prinsloo, 2020)?

In my case, Google & Microsoft can find out about my preferences by using these platforms. However, not only educational data can be collected but also behavioural or nutritional that can be even more personal (Prinsloo, 2020).

Conclusion: I can improve interactivity in my lessons. This ‘made me up’ not only by discovering this gap in my teaching but also by making me think about what can be changed (Williamson, 2014, cited by Ozga, 2016).

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.

Capriotti, P. & Moreno, Á. (2007). Corporate citizenship and public relations: The importance and interactivity of social responsibility issues on corporate websites. Public relations review, 33(1), pp.84–91.

Ozga, J. (2016). Trust in numbers? Digital Education Governance and the inspection process. European educational research journal EERJ, 15(1), pp.69–81.

Prinsloo, P. (2020). Data frontiers and frontiers of power in (higher) education: a view of/from the Global South. Teaching in higher education, 25(4), pp.366–383.

Williamson, B. (2017). Digital education governance: Political analytics, performativity and accountability. In Big Data in Education: The digital future of learning, policy and practice. 55 City Road: SAGE Publications Ltd, p. 65.

This week I tried to think from a policymaking perspective and how governance influences datafication and datafication influences governance in return. Governance can influence datafication by encouraging students to use various platforms. Information collected in these platforms can be used to make decisions. Governance often uses data to increase performativity. However, it increases accountability, as well (Williamson, 2017).

I received many cookies. Some of the websites I used because I was encouraged by my education provider. Data visualisation was simplified. It is one of the key goals of data visualizations (Williamson, 2017).

1. Institutions can increase performativity by collaborating with other platforms and receiving data about their students. Data can not make a good policy in itself, but it can help make a more informed decision (Williamson, 2017). Profit is often seen as one of the key drivers in educational improvement.
2. If data would provide data for institutions, it would also result in higher accountability.

Nowaday’s educational governance is shifting from a qualitative approach to a quantitative one (Williamson, 2017). Governance becomes more probabilistic. Third-party platforms can provide accurate information about users, ‘constant auditing of behaviour’ is undertaken. For instance, Coursera can provide real-time information about a users device, traffic sources, movement on the site, location (can be accurate within 2 meters). This way, ‘fast policies’ can be implemented (Williamson, 2017). Big data can mean massive participation in policymaking.

Larger data collection often results in higher accountability (Anagnostopoulos et al., 2013). Educational institutions need to present themselves in an ‘intimate account’. It can discourage some organizations from implementing data-driven strategies.

Therefore, organizations can encourage students to use various resources, and this way, collect various data. However, it would result in high accountability. Also, would it really be ethical?

Reference:

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.

Williamson, B. (2017). Digital education governance: Political analytics, performativity and accountability. In Big Data in Education: The digital future of learning, policy and practice. 55 City Road: SAGE Publications Ltd, p. 65.

This week I tried to analyse what data various platforms try to collect from users through cookies. Cookies are often described as the main source of profit for companies (van Dijck et al., 2018). What kind of software teachers might use in their teaching? Teachers should protect their students (McKinney de Royston et al., 2021). How teachers can think not only of platform effectiveness but of privacy issues too.

Visualization design choices: a teacher is in the middle of the graph. Different colours show what data about students can be provided to platforms by choosing to use one or another platform.

Finding 1: Teachers need to carefully assess what third-party platforms are used in the learning process. For example, if Coursera is used and promoted to students, a lot of personal data about students will be collected. Does a teacher who is often viewed in our society as a protector of students (McKinney de Royston et al., 2021) can do that?

Finding 2: educational platform ‘Coursera’ collects most of the personal data. It applies various principles of datafication and personalisation (van Dijck et al., 2018). Teachers do not need to fully rely on various educational online resources and need to assess the privacy regulations in each chosen platform.

The relationship between data and ‘teaching’.

This block helped me realise the relationship between lesson preparation and overall performance. Moreover, the number of various cookies I receive online from different websites. Besides, it helped me think about the responsibility I carry by offering students various online websites.

Furthermore, various creative data representations such as Internet cookies represented as simple eatable cookies are necessary because dashboards often impose limits (Williamson et al., 2020). Also, how each data visualisation choice can have a big impact on teaching. To make datafication effective teachers should collect data about their practices themselves because otherwise it can be associated with surveillance (Brown, 2020). Data and teaching values need to be aligned to make it efficient.

This block also helped me to realize the importance of platformization and data literacy in the relationship between data and ‘teaching’.

Platformization is becoming more and more popular (van Dijck et al., 2018). It worries a lot of students and parents due to focus on profit rather than educational achievements. At first glance, various free online resources seem appealing. However, the amount of personal data which is collected on online platforms is can be harmful. Datafication and personalisation should be followed by surveillance, privacy, data security, ethical and pedagogical foundations. This can be hardly found in nowadays online educational giants, such as Coursera.

Data literacy among teachers is undermined. Strong data literacy is essential and should be developed among educators (Williamson et al., 2020). Teachers need to know how to process and understand data (Raffaghelli & Stewart, 2020). Data literacy can help to reduce unknown unknowns. It needs to have in mind various societal, institutional, individual and contextual practices. To make data literacy learning to appeal, it needs to involve various visualisations, interactive, constructive and insightful suggestions for useful tools (Sander, 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), 384-400.

McKinney de Royston, M. et al. (2021). “I’m a Teacher, I’m Gonna Always Protect You”: Understanding Black Educators’ Protection of Black Children. American educational research journal, 58(1), pp.68–106.

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), pp.435–455.

Sander, I. (2020). Critical big data literacy tools—Engaging citizens and promoting empowered internet usage. Data & Policy, 2, pp.Data & Policy, 2020, Vol.2.

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

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.

This week I have collected data related to platformization (van Dijck et al. 2018). Parents and students are worried about data collected by various online platforms. Questions raised before data collection.

1. How many times in 5 days am I going to receive cookie requests?
2. What do I accept?
3. What do websites/apps ask me to accept?

Findings: 1. I have received 9 cookie requests.

I drew 9 ‘cookies’. Every ‘cookie’ represents one cookie request. The number of cookie requests surprised me. On average, I have received 2 requests a day.

2. I always tried to reduce to the minimum the amount of data I would provide but still be able to use a website/app.

3. Most common: Data use for security, performance & personalised ads purposes.

Various ‘candies’ were drawn inside the ‘cookies’ to represent different data types that I was asked by the websites/apps. Different ‘candies’ show differences between various data purposes.

Visualization design choices: digital ‘cookies’ were drawn as eatable cookies to show what I want to represent clearly. ‘I chose candies’ with different colours to represent differences between data purposes. ‘Cookies’ themselves are not fully coloured and more like curved lines to represent a type of suffering that data provider can receive from providing personal data. This type of data representation can be useful to teachers because dashboards often impose limits (Williamson et al. 2020).

Data that cookies can provide can be useful to teachers. Datafication and personalisation should be followed by privacy, surveillance and data security and pedagogical foundations (van Dijck et al. 2018). It is essential the vast majority of learners data would be analysed by educators who concentrate on learning-related loops of experimentations, reflections, and change rather than profit. Teachers would not need to focus only on analysis but consider broader societal, institutional goals & individual and contextual practices (Raffaghelli & Stewart, 2020). Strong data literacy skills would need to be developed among teachers (Williamson et al., 2020).

However, teachers should also analyse provided data ethically because it can transform into student surveillance (Brown, 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), 384-400.

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), pp.435–455.

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

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.