Vilius Slapelis's Data Visualisation Blog

This course helped me to think about data in education from different sides. I understood the relationships between data & learning, data & teaching and data & governing.

Data & Learning. Key findings:

1. Most time outside, I spend on Thursdays and Saturdays. A limitation has occurred because the intensity of activities was not measured. This can be seen as a black box in the analysis (Tsai et al., 2020).
2. The intensity of physical activities does not have a clear impact on my willingness to study. However, the collected data was only about me, and it is not neutral. Data collector imprint needs to be considered (Knox et al., 2019).
3. Around 25% of the time during the week, I spend learning.

Data related to personal activities can help think about learning (Eynon, 2015, p. 407). This can encourage students and facilitators to have conversations together (Stocchetti, 2020) and provide useful feedback (Ifenthaler & Erlandson, 2016, p.1). However, not all data can be used when thinking about learning. Therefore, what factors are taken need to be carefully assessed.

It is important to consider how learning can be represented quantitatively? How can motivation or enjoyment be represented in numbers? Learning is a complex subject that can be challenging to do it. Black-boxes (Tsai et al., 2020) can not be left because they can also result in various consequences for individuals. For instance, it can shift learners attention from learning to data (Wise et al., 2013, cited by Eynon, 2015).

On the other hand, data can provide various improvements in the learning process too. It can have motivation, meta-cognition, informed learning choices and other support (Eynon, 2015).

Data & Teaching. Key findings:

1. Time spent preparing for lessons does not have a clear effect on performance. This type of personal data can help teachers to evaluate their performance and make necessary conclusions.
2. On average, I receive 2 cookie requests a day. Most common cookies are related to data used for security, personalized ads and performance.
3. The website recommended by teachers to students can impose various student privacy related threats.

Data can show ‘truths’ about teaching (Beer 2019, cited by Williamson et al. 2020). However, data needs to be collected by teachers themselves because otherwise, it can also be considered surveillance (Brown, 2020). Often learning analytics can negatively intervene in the learning process. Tools need to be aligned based on practitioners values and views. Collecting data myself about my preparation time helped me to feel more comfortable and reveal realistic results. If my workplace tracked me, I would have consciously or even unconsciously would spend more time on lesson preparation.

Data literacy is an underrated skill crucial in today’s world (Knox et al., 2019). This can help teachers to find unknown unknowns. It is essential that an individual would have in mind various societal, individual and institutional contexts. However, some can argue that critical education understanding and technical skills can hardly fit together. I have not managed to find much analysis in the existing literature. Therefore, further research is necessary.

Cookies can be useful for various educational purposes. It is widely used in the online education platform ‘Coursera’. It focuses on various datafication and personalisation principles  (van Dijck et al., 2018). According to my findings, ‘Coursera’ collects more student data than any other online platform I have encountered. Data should be used ethically, foundations of privacy and data security should be followed (van Dijck et al. 2018), which can often result in less profitable decisions. It is essential that data provided by students would be analysed by educators who concentrate on learning rather than profit. Teachers would consider the analysis and various societal, institutional and individual issues (Raffaghelli & Stewart, 2020).

Data & Governing. Key findings:

1. 66% of my lessons are interactive. However, I am not sure if my educational institution is satisfied with this result. Data that I collected was personal to me, and I presented myself in an intimate account (Anagnostopoulos et al., 2013). I used various colours to represent the logos of platforms to make easier associations to readers.
2. 75% of my personal data go overseas. It contradicts the fact that most personal data go to North American companies (Prinsloo, 2020). In data visualisation, different colours were used to represent flags of various countries.

Governance can often influence datafication by encouraging students to use various platforms that could collect data. It becomes more probabilistic due to the quantitative approach that is used when analysing data. Many educators think that data does not need a high degree of interpretation and can speak for itself (Anderson, 2008 cited by Ozga, 2016). This can help to implement ‘fast policies’ (Williamson, 2017). Big data means massive participation in policymaking. The quantitative approach often did not show various details in my visualisations, and various black-boxes were left (Tsai et al., 2020).

Datafication in governance is often used to increase performativity. Governance often sees data as a solution to various problems (Ozga, 2016). However, it also increases accountability (Williamson, 2017). A higher degree of data collected in institutions means that more data needs to be revealed, and they need to present themselves in an ‘intimate account’ (Anagnostopoulos et al., 2013). It can often discourage educational institutions and teachers from datafication. I would not be very enthusiastic about my educational institution collecting personal data about my educational decisions.

Educational governments can collaborate more together and come up with conclusions (Anagnostopoulos et al., 2013). This can help to make more informed educational decisions. For instance, the Lithuanian government can collaborate more with other governments or tech-giants and make better decisions.

Visualisations design

I often used inspirations from ‘Dear Data’. However, I tried to combine other ideas and my own. I tried to use many different colours and shapes to make it interactive and appealing to the eye. For instance, I have drawn actual cookies to represent online cookies. Also, I tried to use particular lines to symbolise the intensity of things that I tried to visualise. I made visualisations simple and user friendly (Anagnostopoulos et al., 2013) that a reader can understand right away what is shown. However, this often encouraged simplicity (Williamson, 2017) resulted in many black boxes (Tsai et al., 2020).

Conclusion

Data visualisations helped me to learn a lot of facts about myself and my behaviour. Collecting, analysing and visualising data helped me to see datafication process as a whole. Overall, data has many advantages and disadvantages and its non-neutrality need to be remembered (Fontaine, 2016). Therefore, critical thinking and evaluation of data and visualisations are crucial.

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.

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.

Eynon R. (2015). The quantified self for learning: critical questions for education, Learning, Media and Technology, 40:4, 407-411, DOI: 10.1080/17439884.2015.1100797

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.

Ifenthaler, D. & Erlandson, B.E. 2016. Learning with Data: Visualization to Support Teaching, Learning, and Assessment, Technology, Knowledge and Learning, vol. 21, no. 1, pp. 1-3.

Knox, J., Williamson, B. & Bayne, S. (2019). Machine behaviourism: future visions of ‘learnification’ and ‘datafication’ across humans and digital technologies, Learning, Media and Technology, 45(1), pp. 1-15.

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.

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.

Stocchetti, M. (Ed.). (2020). The Digital Age and Its Discontents: Critical Reflections in Education. Helsinki University Press. doi:10.2307/j.ctv16c9hdw

Tsai, Y., Perrotta, C., & Gašević, D.. (2020). Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics. Assessment and Evaluation in Higher Education, 45(4), 554-567.

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. In Big Data in Education: The digital future of learning, policy and practice. 55 City Road: SAGE Publications Ltd, p. 65.

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.

Time spent preparing for a lesson and my performance in a lesson.

The data that I have collected is subjective, and many other factors need to be considered when evaluating this data and taking further actions. For instance, I gave a grade for my personal performance in each lesson. However, multiple factors could have influenced my decision. Why did I evaluate one class as a six and another one as a seven? Is there a clear distinction? I have not thought thoroughly about the meaning of a specific grade before collecting data. This needs to be improved in the future, and clear criteria for each specific grade mean need to be established.

Key finding: Does time spent preparing for a lesson influence my performance in a lesson? No

A small face represents each lesson that I had this week. Every speech bubble represents how much time did I spend preparing for a lesson. There is no clear relationship between these two factors. It seems that lesson success involves more things than just spending time getting ready for it.

Visualization design choices: faces were chosen to illustrate how I evaluated my performance. Smile illustrated high performance, while sad faces low. The colour of the lines of speech bubbles showed how much time do I spend preparing. The green colour represented that I spent a high amount of time preparing when red showed the opposite. Also, I was trying to represent my data, not as a dashboard, because it can often harm education (Brown, 2020).

This type of data might be useful for every teacher to evaluate how much time they spend preparing for lessons and whether it really influences their performance. It can reveal ‘truths’ about education (Beer 2019, cited by Williamson et al. 2020). Maybe spending their time developing other things would be more beneficial? Such as deploying new technologies and becoming ‘data literate’ (Williamson et al. 2020)?

However, it is important that such information would be collected by instructors themselves because otherwise, it can seem like surveillance (Brown, 2020). Often learning analytics intervene in education in counterproductive ways. Tools need to be aligned with practitioners’ values and views.

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.

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.

Visualization 3. How do I spend my time?

This visualization tries to assess the amount of time I spend learning. It helped me to learn about my own learning.

More than 50% of the time I spend teaching

It is a key way how I receive my income. However, data is not neutral, and my personal imprint needs to be considered (Knox et al., 2019). I collected data from Wednesday to Sunday. If I had gathered it from Monday to Sunday, the percentage would have been probably even higher.

Almost 25% of the time I spend learning

I expected a lower percentage of my time. It surprised me. Learning is highly important to innovate and make sophisticated decisions (Wiggins, 2018, cited by Eynon, 2015).

Visualization design choices: bars were chosen to show a clear difference in time spent on different activities. Colours were chosen for bars based on how much do I enjoy while doing a particular activity. Enjoyment plays a crucial role in the continuation of activity (Stevens, 2000, p. 601).

Key finding: fourth of my time, I spend learning. This is a sufficient percentage, and my concentration might need to be shifted towards physical activities or personal needs.

Learning with data

Collecting data about personal activities can help find necessary data related to learning (Eynon, 2015, p. 407) and encourage conversation between students and facilitators, which is crucial in learning (Stocchetti, 2020). It can provide the necessary feedback (Ifenthaler & Erlandson, 2016, p.1). My third visualization shows the necessary feedback to me about where I spend my time.

Not all findings from data can be linked to learning. What parts of experiences, processes, and outcomes can be related to learning need to be carefully assessed (Eynon, 2015, p. 408). For instance, my own physical activities analysis showed that it has little or no influence on learning.

Using numbers in a very complex field such as learning can be challenging. It can result in many different consequences for individuals. For example, it can diminish creativity (Beach & Dovemark, 2009), or encourage learners to concentrate on data rather than learning itself (Wise et al. 2013, cited by Eynon, 2015). Therefore, various factors need to be carefully considered (Eynon, 2015). For instance, how motivation or enjoyment can get a specific value in machine learning models? Black-boxes (Tsai et al., 2020) can not be left. Various legal and ethical issues need to be addressed too (Eynon, 2015). It was not an issue for me because I was analyzing my own data.

There are multiple advantages from data that can be used for better learning, such as enhanced motivation, additional support, informed learning choices, and enhanced meta-cognition (Eynon, 2015). The past three weeks’ visualizations helped me to realize that physical activities do not have a significant effect on my learning and that I spend a sufficient amount of time learning.

However, there are multiple disadvantages too: closing down creativity or alternative ways of learning, changing self-concept, shaping educational opportunities. Various stakeholders need to be aware of it (Tsai et al., 2020). I have experienced changing self-concept regarding the relationship between physical activities and learning. I do trust data, however, a critical evaluation of data and its visualizations are essential! Data literacy is an important skill (Knox et al., 2019).

References

Beach, D. & Dovemark, M. (2009). Making ‘right’ choices? An ethnographic account of creativity, performativity and personalised learning policy, concepts and practices. Oxford Review of Education, 35(6), 689-704.

Eynon R. (2015). The quantified self for learning: critical questions for education, Learning, Media and Technology, 40:4, 407-411, DOI: 10.1080/17439884.2015.1100797

Ifenthaler, D. & Erlandson, B.E. 2016. Learning with Data: Visualization to Support Teaching, Learning, and Assessment, Technology, Knowledge and Learning, vol. 21, no. 1, pp. 1-3.

Knox, J., Williamson, B. & Bayne, S. (2019). Machine behaviourism: future visions of ‘learnification’ and ‘datafication’ across humans and digital technologies, Learning, Media and Technology, 45(1), pp. 1-15.

Perry D Wiggins. (2018). Metric of the Month: Learning Days Per Employee. CFO.com, CFO.com, 2018-09-10.

Stevens, M et al., (2000). The Groningen Enjoyment Questionnaire: A measure of enjoyment in leisure-time physical activity. Perceptual and motor skills, 90(2), pp.601–604.

Stocchetti, M. (Ed.). (2020). The Digital Age and Its Discontents: Critical Reflections in Education. Helsinki University Press. doi:10.2307/j.ctv16c9hdw

Tsai, Y., Perrotta, C., & Gašević, D.. (2020). Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics. Assessment and Evaluation in Higher Education, 45(4), 554-567.

In this week’s visualisation, I tried to go deeper into the relationship between physical activity outside and learning. I tried to assess the influence of physical activities’ intensity on the relationship.

Does the intensity of physical activities outside influence my willingness to study? Not clear

It is hard to tell due to the fact that some days I felt more motivated to study after a hard workout, while during other days it even had a negative effect and my motivation went down due to physical fatigue (Yu et al., 2006, p.331).

Does the intensity of physical activities outside influence my engagement in course activities? No

It clearly can be seen that the physical activity intensity does vary between Wed/Thur/Fri. However, engagement varies. Various factors need to be considered. Data is not neutral, and the imprint of producer needs to be assessed (Knox et al., 2019). For instance, I leave my course activities at the end of the week. Most of my high-intensity physical activities are at night.

Visualisation design choices: lines where chosen to clearly show the intensity. The green colour was chosen to show a positive relationship between physical activities and learning. Green is often associated with positivity (Akers et al., 2012).

Physical intensity has little/or no influence on my learning.

References

Akers, A., Barton, J., Cossey, R., Gainsford, P., Griffin, M., & Micklewright, D. (2012). Visual Color Perception in Green Exercise: Positive Effects on Mood and Perceived Exertion. Environmental Science & Technology, 46(16), 8661-8666.

Knox, J., Williamson, B. & Bayne, S. (2019). Machine behaviourism: future visions of ‘learnification’ and ‘datafication’ across humans and digital technologies, Learning, Media and Technology, 45(1), pp. 1-15.

Yu, C. C. W, Chan, Scarlet, Cheng, Frances, Sung, R. Y. T, & Hau, Kit-Tai. (2006). Are physical activity and academic performance compatible? Academic achievement, conduct, physical activity and self-esteem of Hong Kong Chinese primary school children. Educational Studies, 32(4), 331-341.

This data visualisation shows my physical activities outside throughout the week. Data was collected from Monday to Saturday, and each line represents 1 minute spent physically exercising outside (clearing snow, running, walking). The table on the right side shows the exact time I did it.

Key findings: most of the time outside was spent on Thursday and Saturday. It was more than 1:20h. Least time outside was on Tuesday with under 30min. Most of the times I went outside at 2 pm and 9 pm.

Many studies show that physical activity positively influences learning (Bueno et al., 2021, p.54). On the other hand, too much physical activity can also drain students’ energy (Yu et al., 2006, p.331). Therefore, further investigations are necessary. This data can be linked to educational achievements and analysed how physical exercises can influence it.

Another factor that I was trying to assess the importance of spending time outside for learning. However, personally, I have not felt any significant influence, neither I found the evidence in the literature.

However, my own data analysis has its own limitations. I have not recorded the intensity of my physical activities that can influence academic performance (Sember et al., 2020, p.1). It is a black box that was not taken into account (Tsai et al., 2020).

References

Bueno, M., Zambrin, L., Panchoni, C., Werneck, A., Fernandes, R., Serassuelo, H. et al. (2021). Association Between Device-Measured Moderate-to-Vigorous Physical Activity and Academic Performance in Adolescents. Health Education & Behavior, 48, 54-62. https://doi.org/10.1177/1090198120954390

Sember, V., Jurak, G., KovaÄ, M., Morrison, S.A. & Starc, G. (2020) Children’s Physical Activity, Academic Performance, and Cognitive Functioning: A Systematic Review and Meta-Analysis. Frontiers in Public Health, 14 Jul, NA, available: https://link.gale.com/apps/doc/A629441333/AONE?u=ed_itw&sid=AONE&xid=566002d3 [accessed 31 Jan 2021].

Tsai, Y., Perrotta, C., & Gašević, D.. (2020). Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics. Assessment and Evaluation in Higher Education, 45(4), 554-567.

Yu, C. C. W, Chan, Scarlet, Cheng, Frances, Sung, R. Y. T, & Hau, Kit-Tai. (2006). Are physical activity and academic performance compatible? Academic achievement, conduct, physical activity and self-esteem of Hong Kong Chinese primary school children. Educational Studies, 32(4), 331-341.