A Week of Jet Lag

This week I was traveling to the USA on a personal vacation and no one can imagine the amount of Data available for collecting but at the same time I was trying to get some quality time visiting my son at his University. To me this trip was a 12-hour timezone difference and jet lag was creeping especially at night when my body is used to be fully awake.

I started logging my wake up hours and estimated my sleeping time to a proximity of +/-10 minutes. The data gathered was simple time of sleep, time of waking and the depth of the sleep. The thicker the line the deeper the sleep or the more alert my waking. The data was collected Saturday to Friday from 7pm to 7am.

The Legend

Here is my sleeping / waking-up data visualisation chart during a week of jet lag.

After looking at the chart, I was trying to make sense of it or finding a pattern but it was really hard. I was not getting any day time sleeping as we were out and about. By 7pm, I started the sleep struggle cycle. Tried to push my sleeping time at different time blogs like the second day still I was not able to sleep through the night. My deep sleep average time is about 2-3 hours only and not always in a continuous stretch.

Going to the science, a “typical sleep cycle” includes five stages of sleep with stages 1-2 as light sleep, 3-4 as deep sleep, and the fifth stage as REM (rapid eye movement) sleep according to sleepcycle.com.


Comparing my chart to the typical sleep cycle that I might have had the same deep sleep number of hours but I have not been able to maintain the cyclic approach especially when I get wide awake in between the sleep periods which made it harder for me to get back to sleep and continue the cycle.

Most of my hours are either at light sleeping or not fully awake which is a sign of being in a jet lag and also getting distracted with phone / messages at home country time zones.

Of course, there are many environmental factors affecting sleep like the bed, pillows, noise level and temperature which I have experienced during my hotel stay. Also, if I compare my non jet lag sleeping patterns; I’m usually a light sleeper, waking up at night more frequently and also age affects sleeping patterns especially being a women in 40ies. According to Psychology Today: “about 31 percent of women say they have trouble staying asleep at least four nights a week and wake in the morning feeling tired, rather than rested”.

How can I take this to a governing with data angle? I just finished reading Anagnostopoulos & Jacobsen 2013 reading and I would like to reflect the sentence: “Rather than empowering the people, the data may constrain what people know and how they think about their schools” in reference to how information that test-based infrastructures make available can only provide partial views of schools, teachers, and students. The challenge is that by setting an ideal performance / testing standards that could be used for building educational policies / regulations, we are looking at numerical ratings that are only saying: “little about the causes or failures” and how can measure qualitative teaching and learning data like: “creativity, and commitment, social capabilities” and etc. We keep going back to “one size fits all” – one standard sleeping cycle – governing by data approach. The question how can we design and construct information infrastructures that measures contextual learning and teaching data to provide true value and influence inclusive policy development.


  1. 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.
  2. The sleeping cycle website and app – https://www.sleepcycle.com/how-sleep-cycle-works/
  3. Psychology Today website – https://www.psychologytoday.com/us/blog/sleep-newzzz/201910/what-your-sleep-is-in-your-40s-and-50s

A Week of Rules

This week I tried to set few simple rules for work, study and personal aspects of my day and the idea is to measure how was the execution of those rules. I didn’t gather any additional data of why a certain rule was not followed. The following is the legend identifies the 6 rules to be monitored

  • Need to stop working after 6pm – Work Rule
  • Camera must be always on for work calls – Work Rule
  • Stop using the phone at least 1hr before sleeping – Personal Rule
  • No carbohydrates eating for a week – Personal Rule
  • Coffee and tea should be limited to 4 or less with 4 considered border line – Personal Rule
  • Study for at least 1hr a day – University Rule

I used the traffic light data visualisation to demonstrate: Green – 100% following the rules, Red – 100% missing the rule and Amber for in between. The data was collected from Sunday to Thursday as my complete working week (no weekends).

Other than the rule regarding camera on for work (on Thursday it was a group call and camera was not mandatory so I had it off), I have not been following any of the other rules ! For work, I had a heavy start considering that I’m going on leave the following week so I was motivated to work late hours to finish more pending work. Coffee I was mostly Amber – 4 cups a day exactly. For University studies I fell behind especially that I’m working more. Wednesday was a good day for me since I was able to catch up with work and studies and but cheated on food.

We use the traffic light indicator visualisation a lot at my work to provide dashboard performance view on sales, revenues and work related KPI’s. It is an oldish system that is good in giving a quick single measure update but does not really tell the story. One can sometimes notice trends in traffic light systems for example: in beginning of the week I was more stressed with work so I drank more coffee than towards the end when I was relatively more relaxed and the week is over. We can also link late hours of work to lack of studies.

From governing with data point of view, I wanted to approach this from the learning or educational institutes administration aiming to govern through similar dashboards and passing judgement on teachers and students using a singular view of data. Many rules of governance models regarding compliance or adherence to learning objectives could be formulated without looking deeper or understanding the story behind the results.

There are many factors that can have significant impact on the results and the way they are interpreted. For my data collection, on purpose I didn’t collect any additional information around why each target is met or not met, time of the day, mood or emotional status, level of stress, external factors, environment (being home all the time), etc. Contextual information and knowledge are important in Education to drive meaningful and relevant learning polices other than “quick” and maybe “cost effective” mass policy formation and governance model dependent on massive single points of data. I found the following paragraph from Ogza 2016 relevant for this topic.

Statistical data reduce the complexities of new national and local education practices through their selection of key indicators on the basis of which schools may be compared, and these ‘thin descriptions’, stripped of contextual complexity, make statistical data a key governing device (Ozga et al., 2011). Furthermore, because there is such a strong emphasis from policy-makers on ensuring that these data enable comparisons to be made (whether of pupil performance, teachers, pupil types), the knowledge claims that are most powerful are those that are de-contextualised, trans-historical and trans-situational, indeed:

“…the decline or loss of the context-specificity of a knowledge claim is widely seen as adding to thevalidity, if not the truthfulness, of the claim. (Grundmann and Stehr, 2012: 3)”

Ozga, J. 2016. Trust in numbers? Digital Education Governance and the inspection process. European Educational Research Journal, 15(1) pp.69-81ADD citation

A Week of Communication

Visualisation of personal communications by platform vs source

This week I captured direct communication data points. Direct means, I’m communicating with someone or I’m involved in a communication direct to me only. Thus, mass messages and emails are removed from work emails and WhatsApp/messages/emails groups. I focused on 4 elements of the data captured.

The legend

  • Communication source: who I am communicating with. I identified 3 main groups : Family ( Husband, Kids, and siblings), Work, Friends, others (in this week it was some Unv communications and one face to face communication with a retailer.
  • Communication platform: Face 2 face, video call, voice call and messages
  • Communication impact : answering the question if this particular communication had a positive, negative impact of how I felt from before the communication to after.
  • Timing of the communication : morning and afternoon (12pm is the time breaker)

I developed the visualisation on focus areas. One with the Sources of the information as the main pillars and another one on the Platforms being the pillars.

Reflection points:

  • Family is the most source of my communications with face to face and voice call occupying the highest interactions. Impact of communication is almost equally split with many with “no impact” communications – catching up and status updates communication
  • Work is very focused on video calls which is expected with working from home. Also the impacts tends to be equally split however more positive communications with video calls compared to messages.
  • For friends, there is a lot of positive communications especially the face to face ones.

Reflection points:

  • Face to face communications has more positive effect as a percentage from all other platforms
  • Video calls has no impact when it came to work and more positive when it came to family.
  • Voice calls are the least of all focusing mainly non family and friends.
  • Messages remains the highest contributor especially with friends and then comes work and family.
  • I need to be calling friends more often instead of depending on the messages as messages tend to create negative impact while calls always give a positive impact.
  • Messages tend to have more negative or no impact across all sources especially friends

Reflecting on Governing with Data Blog and one of the “Tweetorial’ questions: ” What policy problems might big data be used to address in education, or what new problems might governing with data generate?” I wanted to extract from my visualisations some communications policies and measuring validity as possible.

A communication policy that can be deducted from this data set is: in order to improve motivation at work, messages should be eliminated as a mean of communication and face to face and/or voice calls communications should be introduced.

This could be a sample policy deduction looking at the communication sources and platforms from a single attribute of creating a positive impact on the communicator. However, the policy didn’t take into account the condition that, currently there are no face to face data to support this argument in the work environment. It has also ignored completely that many “no impact” communications of messages which could be more than sufficient in the work place. The policy is build on a specific set of data collected and ignored other sources and validations into what could be behind the impact on communication.

This is a basic sample of policy development based on a set of data where little contextual and additional information / data are considered. We can also see that technology platforms (in this case phone, video conferencing and messaging apps) are impacting policy development and measurement. According to Williamson’s concept of “Data Instrumentation” “

Understood as digital policy instruments, data processing technologies can therefore be seen as a digital policy instruments that reproduce and reenforce existing.

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

From an educational perspective, what would be the best communication platform that would improve learning and teaching experiences especially in this increased dependencies on digital education and hybrid learning environment? What data we need to collect and analyse to drive policies related to communication platform to govern the new learning environments?

A Week of Reading Platforms

This week I decided to record my various reading times on different media and platforms for both my courses: Critical Data and Education (Data Course) and Introduction to Social Research Methods (Research Method Course). The idea is to measure effectiveness and level of engagement of each platform in respect to the devise or media used e.g. a physical book, computer or iPad (a tablet).

The Data captured was the reading type: articles, books, blogs, Moodle…etc for each course and using which platform. Each data entry represents around a 15 minutes of reading engagement. For example if I read for 30 minutes I would addd two data entries. The readings I recorded this week were only related to my University studies.

The Legend

I used a library and shelves like design for the representation inspired by the reading element of this data collection. The Shelves represent the level of engagement and comprehension of the reading platform with the highest shelf representing a high engagement.

Observations from the data visualisation :

  • Articles and Blogs are the platforms of highest usage and highest effectiveness in terms of engaging with the reading material and comprehension. I was primarily using the computer as the media of choice and the iPad which was less effective at times.
  • The least effective and used platform is email and I used it once to read a response from a teacher. I saw the email on the phone but I preferred to read it further on the computer.
  • I added MS teams because for Research Methods, my group we are using it heavily to discuss assignments so there is a good level of reading and engaging in learning activities not only for chatting. The platform would be better leveraged if blogs and discussion forums are used in the same space.
  • Moodle was used mainly to read the weeks overview and activities text and summary for, mainly, the Research Methods course. The effectiveness really depends on the topic or how reading is flowing. It is more effective when there are videos and links that allows the reader to stay within the Moodle platform and not jumping between websites or external links. Since Moodle is not being used much for the Data course
  • WordPress is only used for reading weekly overview and engaging with blogs and forums. It is definitely a more engaging platform primarily on the computer.
  • Books are very effective and engaging for me. I can sense a bit of bias here for physical books.
  • Although I consider myself a heavy user of the phone but I rarely use it for educational or reading purposes. Even for emails or word press, the engagement is primarily viewing and scan reading then engaged reading.

Looking at this visualisation from a teaching with data perspective, if a teacher is monitoring these platforms and receiving analytical information about how each is being used, frequency, comprehension levels and overall how students are engaging with the course reading resources and platform, he/she can make interventions or be more critical of how to use the data to adapt learning environments to a specific educational needs or learner’s requirements. As mentioned by van Dijck et. al. 2018:

Personalized data allegedly provide unprecedented insights into how individual students learn and what kind of tutoring they need. 

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

But this flexibility and adaptability offered to teachers needs to be coupled with the assumption that teachers have the required ‘data literacy’ and “skills and knowledge to engage ethically and pedagogically with learning analytic”(Raffaghelli & Stewart 2020). In educational technology, the role of the teacher would then change from being seen as dashboard controller or “datafied” subjects (Williamson and Shay 2020) to a decision making and educational authority.


  • 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

A week of Performance tracking

For this week’s data tracking, I decided to track the total hours I spend per day on various activities of the day and visualise them against a benchmark, standard, statistical data and self targets to measure my performance against these targets on a daily basis for each of the selected activities. I captured most of the data using Apple’s Screen time that aggregates application access data from my phone, Mac and iPad as I use all three simultaneously. The idea here is to simulate how students’ data that are captured through the various learning systems they use.

The data captured are total hours for : Work, Study, Social Media, Entertainment, playing Games on the phone, online Shopping and Exercise. All activities were done at home with online access except exercise of course but it was tracked on my phone. The benchmarks and targets are either self imposed (like a teacher would specify a learning target) or I used statistical data. The following table summaries these targets and respective reference.

The hours were summed per day per activity and then each hour/activity is assessed from a performance perspective against the target : “Exceed Target” if performance is better than target, “Met Objective” if within performance target, and “Under Performance” if below expectations. For example: for social media time the higher the hours the poorer the performance while for working hours it is the opposite.

My work week is Sunday to Thursday. For this visualisation, I included the weekend to capture most of my studying time.

WorkThe benchmark was a boundary of 6-8 hours/day. A typical working day is 8 hours however productive hours a day are less than 8 Hrs. According to inc.com the total productive time can be as low as 3hrs/day. According to the Economist:People are working longer hours during the pandemic”. So, I decided to keep the benchmark between 6-8 hrs/day of productive work.
Study According to the MSc in Digital Education handbooks, the total expected workload is between 7-10 hrs/week for most courses. As I am taking two courses, I set the target to be 1 -2 Hrs/day as I allocate more time on the weekends.
Social MediaAccording to Statista.com the daily social media usage worldwide in 2020 was 145 min/day – 2.5 hrs/day. I set the benchmark to be 2 hrs / day as I would like to reduce the time spent. I also use social media for work especially Twitter and WhatsApp.
EntertainmentThis activity is for watching online TV/shows. According to comparitech.com, Netflix users watched an average of 3.2 hours of video per day. For me I put a range for 1-2 hrs/day as a target. Its my unwinding time before I sleep.
Online GamesThis is also my unwinding time playing Candy Crash and similar games on my phone. Usually, this activity happens in parallel when watching online streaming shows / conference calls where I’m a passive listener. I set a target for myself at 30 min/day as I know I can spend more time on it.
ShoppingThis is for online shopping as we are still in a Pandemic stage and almost everything we buy is online. No targets has been set as this is something I have to do in most cases and usually it overlaps with other activities.
ExerciseA target of 30 min/day is self set measure on my Apple Watch
Benchmarks and Targets
The Data Visualisation

Some observations:

  • Many of the activities don’t have a specific block in the day. For example, I study for 20min then work for 1hr then do something else. Especially being at home, I don’t dedicate time blocks of for each activity.
  • Work activity is the most scattered during the day. If I compare this to a full-time student, then this is the measurement of study time. If a teacher looks at the spread of time the immediate judgement would be lack of focus or motivation to study. However tasks could be completed on time and overall performance is high.
  • There are parallel activities specially during passive conference calls (listening mood, or large company calls), watching online TV and social media checking.
  • Social media interactions are also spread all over the day. If I go back to my week of distraction we can see the same there too. Social media is used for work and study too.
  • During the first few days I noticed that I’m exceeding the target for Social Media and games so I started being more aware of the time I spend and I adjusted during the last few days.
  • Some of the applications I use are for multiple activities. For example I used MS Excel for the Dashboard DIY assignment while I use Excel a lot for my work too. Hard to make the clean cut split of time. Same for some social media platforms that I use for work too like: WhatsApp, Twitter and Linkedin

Being monitored can have a positive impact depending on the target value / objective and the audience of the performance measurements or how it is being measured. In the middle of the week I noticed that my Entertainment and Games activity was under performing so I tried to limit myself. Being self aware of your learning objectives and how it is measured may reduce unfair judgement or discrimination against students.

student performance track records, depending on their use, may lead to better personalized attention by teachers but may also enhance discrimination or limit accessibility.

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

I knew I was being monitored and I understood where the targets came from and I even set self targets. In a learning environment, students can be monitored and measured against benchmarks or targets they are either not aware of or are not reflective of their learning needs, environment and objectives. Measuring and monitoring performance is important for teachers to learn about their students but the questions are how it is being done, how and what are the targets, to read what’s behind the measure and and their learning objective.

As mentioned by van Dijck et. al (2018):

in the context of user-data collection and predictive analytics, it means that continuous individual monitoring and tailored didactics become integral to the pedagogical model 

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

My “Teaching” Roles

I started this week not clear what data I will gather from a “teaching with data” point of view especially that I do not work in the educational sector. After some thoughts, I wanted to collect data about myself being a “teacher” capturing my various everyday roles as a mother, friend, work leader and student. After I gathered the data, I decided to use a dial-shaped visualisation to resemble teaching dashboards inspired from this week’s themes. The following is the legend I developed for the visualisation.

Each dial represent a role where I assumed a “teaching” responsibility. The low, medium and high aspects refer to the difficulty of the teaching activity. The main data elements captured are the following :

  • The medium: Face to face, voice call or text/emails.
  • The location of the taught audience “students”.
  • Repetition: capturing if the teaching required repeating (more than once)

The data was captured from Tuesday to Friday of this week. Some of the activities overlapped or they were completed during different hours of the day.

The following is the outcome of the data visualisation.

Teaching Roles Visualisation

This week overlapped with a major assignment in Introduction to Social Research Methods course where I spent significant time chatting and on video calls with colleagues to actually learn from each other; thus, it was a co-teaching role.

Reflecting on the data from this week, it is obvious that the major data points came from my work as a leader of a regional team and most activities are about teaching or revising some work, why we do certain requirements/tasks and how to do these tasks. I considered my work activities as a learning exercise from the teams’ perspective since new tasks were discussed, taught, explained and trained on. Another default teaching role is being a mother. There were few interactions and most of them are do’s and don’ts as my kids are older and one is already in the USA studying – that explains the text based teaching/mothering! The funny observation is that my hardest and most repetitive tasks for this week are related to my dog. Teaching a pet certain tasks can be harder than corporate international business!!! Being a mentor on work and relationship matters is a role I cherish and the entries for that role included coaching and advising data points to my friends with one data point exception – one was on teaching how to cook a certain dish.

To conclude, I would like to reflect on Williamson et. al. (2020) core reading for this week regarding the concept of “data double”:

The construction of data doubles in education is especially consequential since anything that is modelled inside the database then affects the potentially life-changing experience of teaching and learning.

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

If my data were seen by my supervisors at work, would they assume that I have wasted time doing other “tasks” not related to my main job – as there are many overlapping and during “working hours” data? This is a basic reflection of how datafication of the education is also measuring and assessing teachers and conclusions taken towards them might not be accurate and they find themselves defined by data just like technologies / teachers do towards students.

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?

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