9 Weeks of Data Visualisations

The course requirement of selecting, recording, visualizing and reflecting discrete data points on a weekly basis for 9 weeks was definitely a huge learning curve with differentiated and interactive learning experiences. Thinking about what data to capture and how to represent them was a “learning with data” approach in itself. Lupi and Posavec’s ‘Dear Data’ project was an eye opener on hand-recorded data visualizations, but setting a high bar in terms of what data is available and how to generate interesting and creative ones while in a Pandemic lockdown. The exercises made me appreciate data more and realize the contemplations of data collection and visualization to what I am familiar to. In the first half of this blog, I would like to reflect on the data capturing and visualization learning experience; and in the second half, I will focus on how data visualization helped me comprehend the course objectives.

Data Visualization Exercise, Findings and Reflections

For each week, I adopted a process focusing first on building a plan of the data set to be collected and what would be a likely linkage to the theme of that week/block. The presentation took few iterations but then drawing it and reflecting on it became the creative and interesting part of all. Alongside my process of plan, define, collect, represent and reflect on the data here are some of the findings from the weekly visualizations.

  • Scope definition: I started each week with a question in my mind for the data collection and, at certain times, the data took me in other directions. It is important to keep the objectives in mind but equally important is to look at the data with a fresh eye and to adjust the scope as needed.

“First, the purpose of learning analytics is not tracking. Second, learning analytics does not stop at data collection and analysis but rather aims at ‘understanding and optimizing learning and learning environments. Instead, there is a clear focus on improving learning.” (Selwyn & Gašević 2020)

An example was Week 4, “My Teaching Roles”, as I started the week with general data about what I do on a daily basis and then I shifted towards a “teaching” category of my role and I was able to reflect on the data visualisations not only from a role perspective but what does it mean to be monitored as a teacher. 

“Teachers, too, are increasingly known, evaluated and judged through data, and come to know themselves as datafied teacher subjects.” (Williamson et. al. 2020)

  • Iterative process: in many cases I have either added or changed data attributes during the collection process. This was either impacted by the lack of depth to allow for a better visualization or to improve the messaging on educational themes. Going back to the drawing board makes is interesting but was only feasible being hand-drawn. The implications of an iterative process from data systems point of view would not be that easy or flexible.

“Data and metrics do not just reflect what they are designed to measure, but actively loop back into action that can change the very thing that was measured in the first place.” (Williamson et al 2020)

  • Data reliability: Being the sole producer/owner of the data, made me believe that the transparency and openness conditions in producing authentic learning data are addressed (Tsai et. al 2020). However, I noticed that it was extremely hard to be fully inclusive of all data while capturing and tracking data accurately and without bias. How reliable is the data being presented each week? Is a tough question to answer. I reflected on these in more details in the learning with data blog. 

‘There is likely to be a kind of trade-off between the reliability of the data that can be collected and the richness of what can be measured.” Eynon (2015)

  • Learning from others: the most fascinating part was looking into other’s data visualizations and reflections. In many cases, we are collecting the same data points e.g., drinking coffee, distractions, spaces, study material and etc., however, the vast differences in the approach, depth and artwork were insightful and demonstrated how similar data can be visualized in many different perspectives. A real testimony that data is not just data but hold personal preferences/biases, environments, locations and many other external factors impacting a data point like number of coffee cups a day.

“Data do not exist independently of the ideas, instruments, practices, contexts and knowledges used to generate, process and analyse them.” (Kitchin 2014)

Learning, Teaching and Governing with Data 

Although every week/block had specific theme/readings, at many instances, I found that one can use the same data sets to interpret and tackle multiple themes. This came more into effect during the Teaching and Governing with Data blocks. At the end of the 9 weeks, I can easily say that the three themes are interlinked and interdependent and focusing on one without understanding the implications on the other two would impact how we approach data in the educational sector. 

Looking into week 7 data visualization, A Week of Communication,  it could be replicated for all three themes. From a learning with data perspective, the data can be used to define how a student understand his/her learning communications to determine effective methods to priorities and manage learning objectives. From a teaching with data perspective, the same data can be used to generate understanding of what are the effective means of communication and how students respond to each method. The same data to decide on the right communication method for each student. Finally, from a governing with data angle, the data can be used to govern learning and teaching communication platforms and set some policies on learning environments and communication methods.

Each block presented a set of questions related how data is defined, produced and analysed from educational perceptions in attempt to understand how current data-driven technologies and systems/platforms are impacting the overall educational governance including teaching and learning. The analysis and interpretation of data could be subject to different objectives and motivations not necessarily pedagogical ones, especially, when considering predictive and AI based modelling of educational data. 

“Machines are tasked with learning, attention needs to be given to the ways learning itself is theorised, modelled, encoded, and exchanged between students and progressively more ‘intelligent’, ‘affective’, and interventionist educational technologies.” (Knox et al 2020)

There are benefits gained from the “datafication of Higher Education” when analysing educational data and gaining insightful knowledge/information. However, here are some persisting questions: what instruments are being used? what are the design principles? what educational expertise and knowledge used to design/build these technologies? What are the underpinning infrastructures? And Who are the actors? 

These are comprehensive questions to further analyze in this blog but will conclude with the following from Williamson et al 2020: 

“Datafication brings the risk of pedagogic reductionism as only that learning that can be datafied is considered valuabe […] There is a clear risk here that pedagogy may be reshaped to ensure it ‘fits’ on the digital platforms that are required to generate the data demanded to assess students’ ongoing learning.”

Here is a video of all my data visualisations.


  • Ben Williamson , Sian Bayne & Suellen Shay (2020) The datafication of teaching in Higher Education: critical issues and perspectives, Teaching in Higher Education, 25:4, 351-365, DOI: 10.1080/13562517.2020.1748811
  • Jeremy Knox, Ben Williamson & Sian Bayne (2020) Machine behaviourism: future visions of ‘learnification’ and ‘datafication’ across humans and digital technologies, Learning, Media and Technology, 45:1, 31-45, DOI: 10.1080/17439884.2019.1623251
  • Kitchin, Rob. The Data Revolution: Big Data, Open Data, Data Infrastructures & Their Consequences. Sage, 2014.
  • Lupi, Giorgia, et al. Dear Data. Flow Press Media, 2018.
  • Neil Selwyn & Dragan Gašević (2020) The datafication of higher education: discussing the promises and problems, Teaching in Higher Education, 25:4, 527-540, DOI:10.1080/13562517.2019.1689388
  • Rebecca Eynon (2015) The quantified self for learning: critical questions for education, Learning, Media and Technology, 40:4, 407-411, DOI: 10.1080/17439884.2015.1100797
  • Tsai, Y-S. Perrotta, C. & Gašević, D. 2020. Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics, Assessment & Evaluation in Higher Education, 45:4, 554-567, DOI: 10.1080/02602938.2019.1676396

Learning with Data – Block Post

During the “Learning with Data” block, I focused my data visualization assignments on capturing data that would be relevant to a student. I captured three elements: distractions, learning spaces and emotions, with the intention to build a holistic view of physical, digital and emotional conditions of learning. For sure, more data should be captured to enable me to develop more realistic observations and findings and to construct more critical analysis regarding learning with data.  Being the designer, the producer and the recorder of the data, made me believe that the transparency and openness conditions in producing authentic learning data (Tsai et. al 2020) are addressed. However, during the data collection phase, I noticed that it was extremely hard to be fully inclusive of all data while capturing and tracking data accurately and without bias. I’m reflecting on these concerns, hereafter. 

Inclusive Data

The question here: how you ensure that all the needed data are captured? Although, it was a manual process, but there were data elements that were not captured, forgotten or neglected. With an automated data capturing system/technology, this issue could be resolved however designing the data collection triggers might not be as inclusive or well-defined. During personal data tracking, there were some automated data capturing, but there were many opportunities to change others or skip others. To build a learning opportunity from data-driven technologies, it’s important to capture comprehensive data.

Data may restrict the kinds of questions we can ask and the analysis and recommendation generated (Eynon 2015). 


This is accuracy at all levels of data capturing, recording and analysis. The captured data might not reflect the real situation and could be subjective to the time, location, external factors and other factors. I noticed that my collected data captured had elements of intentional and non-intentional errors.

According to Eynon (2015): ‘there is likely to be a kind of trade-off between the reliability of the data that can be collected and the richness of what can be measured.”

This impacts the learning process and aspired benefits of learning analytics. The risk that it might be the opposite; jeopardizing learning outcomes and putting the learner at a disadvantage. 

Biased Data

Bias was also infused in my data selection. It’s strange that one is biased to his/her own self, but the bias here as in the learning activities selection and choices I made before and after data capture. The question would be if I was using a specific technology to capture the same data, would it be the same? The answer is no!

From learning with data perspective, bias could be built within algorithms and predictive analytics which are designed to impact and shape the learning process and behaviours. So

“what constitutes the ‘correct’, ‘preferable’, or ‘desirable’ behaviours for learning” (Knox et al. 2019)? 

I believe that these concerns could be solved by data-driven learning technologies. However, the question is how these technologies are shaping the learning process and what are the embedded design principles? As Bulger (2016) highlighted, the goal is to actually demand transparency, openness and accuracy of what is being collected and to understand the built-in assumptions and specifications to support students’ learning opportunities. 


Emotions & Learning

Tracking emotions before and after learning activities

For this week, I decided to collect my emotional status before and after doing any university work be it reading course material, participating in blogs / discussion forums or working on assignment for both courses that I’m taking Research Methods and Critical Data. The question(s) I wanted to investigate is :

How the emotions change after engaging in a learning activity? 
Is the emotional change linked to the emotional status before 
studying or the learning activity itself?

I have to say that this week was an emotionally stressful week for me with some personal stress and sad feelings triggers that I carried through out my working week. My week starts on Sunday; so, I captured data from Sunday to Friday logging the before and after emotional status of any learning activity. The following are the data captured and the legend for the data visitation thereafter.

After collecting the data, I tried to group some emotions under the one to minimise the disparity of the data. The grouping presented in the color coding:

  • Sad and guilty
  • Impressed, motivated and engaged
  • Tired and confused
  • Frustrated and angry
  • Stressed and anxious
  • Happy, content and relaxed

My inspiration of the circular representation of the data visualisation was the expression “emotional roller-coaster”. According to the Collin Dictionary, emotional roller-coaster is defined as “a situation or experience that alternates between making you feel excited, exhilarated, or happy and making you feel sad, disappointed or desperate.”

Studying, while working and taking care of my family in a confined home within a pandemic situation, does feels like a roller-coaster. I tried to differentiate between the two courses and the type of study. I definitely needed more data and more emotionally variable weeks, but the following is the visualisation outcome.

My emotional roller coaster.

My reflections of the data:

I’m definitely having a stressful week. It was a starting emotion for many activity this week and I can see that the Research Method assignment was not making it any easier. In many instances, I ended up being more tired / confused or anxious.
Working on the Critical Data course in general improved my emotional status as most of the “after” status were Green and Pink.
Working on something I liked definitely improved my emotional status and motivated me to do more work.
The days that I felt sad, studying didn’t make it any easier.
Working with Blogs in general improved my emotional status. I linked this to the feeling of being engaged and belonging to others. In general, being with people does improve my mood and emotional status with one exception.
If I had a low mood, studying can help me get distracted and lift my mood depending on studying type and activity.

It was hard for me to measure my own emotions as it was not a black and white data collection activity and in many cases was dependent on my own interpretations of how I feel and state of mind. Emotions can have a direct impact on learning whether positive or negative impact but it is not as straight forward. Many factors come in the equation. According to a UNESCO publishing from the International Academy of Education titled Emotions and Learning (Pekrun, 2014), and I quote:

“Positive emotions do not always benefit learning, and unpleasant emotions do not  always  impede  learning. However,  for  the  vast  majority  of students  and  academic  learning  tasks,  enjoyment  of  learning  is beneficial.”

If it was hard to identify or influence my own emotional status, how would machine learning or learning analytics help influence learners towards improving earning capabilities and outcomes? I will conclude with a quote from Knox et al. (2019) of what could be a claimed feature of learning technologies:

The ‘learner’ is now an irrational and emotional subject whose behaviours and actions are understood to be both machine-readable by learning algorithms and modifiable by digital hyper -nudge platforms.


Where to Study ?

Learning Spaces Data Visualisation

This week I decided to track my studying and learning spaces around the house for a period of 6 days. As I’m taking two course at this term, I decided to track the data also per course as much as I can. In certain cases, especially when I’m checking Moodle and the blogs the line is blurry a bit.

The question I’m trying to answer this week is :

Which studying space offers more focus and completion of planned task ?

The legend

Data Captured :

  • Space type
  • Course (Research Methods and Data)
  • Time (am/pm)
  • Focus Level
  • Task Completion

The size of the circle reflects the number of times I used the space for studying. The choice of the space was truly random and based on what I was doing before I started to study or shifted my focus to my studies. It is basically what I felt like ! Usually I study or work outside the house in collaborative public spaces but lately the Covid-19 cases are on the rise so the spaces are within my home.

Week 4 Data Visualisation

I decided to capture the different types of “university” related work and per course. I categorised the work as : assignments, blog reading and responding and reading suggested material. My own blog writing like this one is categorised under assignment work. Here are some reflections of what I learnt about my learning spaces.

  • I do almost as much studying in bed as I do at my home office / desk. They are in two separate locations in the house.
  • I tend to complete more tasks with the highest focus in the office than any other space.
  • In the afternoons, I sit on the couch (TV room) and do some work but level of focus and completion is definitely less given the distractions from the TV, family members, dog and being tired after a long day sitting at the office desk.
  • I did more “assignment” word for the Critical Data course vs. more “reading” for Research Methods given that we had two assignments this week in Critical Data while an optional one for Research methods.
  • In general, I’m not able to finish the reading work needed for research method and that’s inline with my lack of focus in the course readings – I’m finding the reading more abstract and too much to complete
  • Most of the studying is in the evening and that’s inline with my schedule of last week data capturing and also because I do my day job in between.
  • I don’t do much reading at home office. I guess this is related to being sitting down and I personally prefer to be more relaxed when I’m reading.

Overall this was an interesting reflection to me because I thought I don’t like to study much at my home office as I wanted to separate my studying from work. However, it seems it is the most efficient learning space.

What I didn’t captured during this week was how I was feeling or additional information like: noise level, stress, distractions and over all mood. I thought about capturing that data but I felt it will be too much information and I was worried about the actual visualisation exercise more.

The question that comes to mind here, how much we design our data capturing tools and methods based on a real understanding of the question we are trying to answer or on how complex the data sets can be which will definitely impact not only the outcome but on how we understand and analyse the data?

5-Day Meals Visual Log

Software based Personal Data Recording

For this assignment I used a mobile app called Ate (link below) which allows the user to take a picture of the food and the app automatically records the time and date. The data that can be added to the pictures are the following :

  • On path or off path (planned meal and quality/quantity)
  • Why you did you eat ?
  • How are you ?
  • Who did you eat with ?
  • How was it?
  • Where did you eat?
  • How was it made?
  • How did it make you feel ?

The information is captured on the pictures when one select the day cap option. The following is a visual log of my meals for 5 days with tags of the answers provided on each meal.

Meal Visual Log

The app also provides data visualisation option in various charts and more reporting could have been obtained if you I purchased the full app version. I went for the basic free one which gave a lot of information. It also measured the fasting time based on the my data logging.

Some reflection points on the app data and what I learnt about my eating habits :

  • 50% of my meals are out of hunger and rest varies. Being in home office and not being able to go out I believe that’s a good representation. I eat of many other reasons than pure hunger
  • most of my eating is while I’m standing. This is usually at the kitchen counter because I’m either eating between calls or during calls!
  • Food is most of the time making me satisfied. Although I describe myself a food adventurer and I enjoy a good meal or cooking something new. With the Covid-19 situation food is more than a routine than to make me happy or just stress eating with my guilty and stuffed ratings
  • I tend to be on path more during the week and then towards the weekend I loose course!
  • I’m impressed that I’m maintaining intermittent fasting without really focusing or counting the hours. I started more than a year ago monitoring my fasting … I guess it is now part of my routine
  • Number of meals a day really vary… and it seems when I’m in a lower mood I eat more frequently and more meals.

Using an application / software for data capturing this week was fun but it had certain limitation compared to the hand-based data recording for the data visualisation assignments. I can also summarise the advantages and disadvantages of having an app/software in the below

Time and date recorded automatically the type of data captured is based on the app design
Capturing pictures and using the phone as a devise made a lot easier No flexibility to capture additional data except in free text that is not part of the automated reports
the automatic reports and statistics The reports are not customisable or you can’t combine two / three types of data to do more analysis
Sharing content The options / feature are based the cost you are willing to invest
Data visiualtion presentations are neat and easy to readthere is no food content analysis / portions / healthy features etc

The question is: would I use this app on a regular basis beyond this assignment / task ? I doubt I would because of it is not linking it to my health or weight management and it doesn’t offer recommendations to improve quality or quantity.

Ate Food Diary: mindful eating by Piqniq Inc.

Visualizing a Week of Distraction

This week I decided to capture different distractions during my awake time from 7 in the morning till about midnight. Every morning or the night before, I plot my calendar for the day and then track the distraction and changes in my calendar. The questions I was testing for this week were:

How am I distracted to follow my daily schedule ? and What type of distractions?
What can I learn about my distractions to reflect on what students may be distracted from during digital learning environments ? 

The types of collected data as soon in the legend are the following :

– The schedule type : working, studying, driving ..etc

– The distractions type and number of distractions per type

– any deviations from the schedule

The Data Visualization Outcome

I noticed from the data that most of my distractions are from the phone be it text messages, WhatsApp, Instagram and calls. I have been doing some browsing and watching TV while studying with occasional distractions from my dog and delivery visitors. I tried to capture the impact of the distractions on my schedule from task completion point of view, however, nothing was there. As I ended up achieving my daily activities and maintaining a healthy study schedule provided that I’m doing two courses this term. The main challenge in the data collections is capturing the frequency of each distraction. Did I check WhatsApp message 2 times of 4 times ? Especially if the distraction was during a call from work or reading course material.

The only distraction that I didn’t capture was the distraction of data capturing itself. I logged my data in my notebook as quick as possible so I can assume that the distraction of the data gathering equals the total of my complete distraction.

The attempt to answer the first question was completed and I will reflect more in the end of the block post, meanwhile, I can start deducting that having a mobile or smart phone next to you when you work or study or even drive ( was being distracted by WhatsApp messages !) is the main source of distractions and many of our students or at least I can see in my kids having the same problem. Whether is that affecting their studies or focus during learning and doing assignment needs to different captured data.

For reference only, the following are the Dear Data week of distraction outcome… different indeed of what I delivered above.

Week 44 – A week of Distractions