Block 2: Week 7

The focus of this week was to record data on different platforms or sites I visited during the week, the purpose of my visit, and the mode of engagement. The data was recorded over a six days period to be able to capture as many entries as were possible.

In order to enhance the readability of the visualization, I was selective of the platforms from which I recorded data, and I also grouped the purpose for visiting the platform in a way that I can be able to capture as many as possible and finally I recorded the mode of engagement as this can inform teaching plans for especially teachers using digital technologies and tools during teaching.

The visualization above shows a summary of what my data recording looks like for the week. Despite the growing debate on the willingness of teachers to adopt digital technologies and tools for teaching, several institutions still largely require faculty to make use of data dashboards provided by these tools to inform their teaching (Brown.2020). 

From the visualization, I realized that my learning engagement was not just fixated on the course learning site (Module) and blogs but was transferred to unconventional learning platforms to further enhance my learning. From this observation, I deduced that one major challenge of data dashboards to teachers is that they are unable to capture every element of student learning thereby giving the teacher an incomplete representation of the entire student learning experience (Williamson et al. 2020). An important element of this visualization in the hand of a teacher in the virtual space is the mode of engagement. Course materials provided in the learning management system can take multiple forms or expressed in different modalities. Therefore record the mode of engagement will support the teacher and designer in choosing the most suitable materials to enhance student learning.

Additionally, most data-driven technologies are programmed to only report certain elements of the learning and this also affects what ends up in the hand of the teacher. Hence, the burden falls on the teacher to determine how much more data is needed to make an informed decision about a student otherwise the lack of data literacy might affect the conclusions about a student immensely. (Williamson 2020). 


Brown, M., 2020. Seeing students at scale: how faculty in large lecture courses act upon learning analytics dashboard data. Teaching in Higher Education, 25(4), pp.384-400.

Tsai, Y., Perrotta, C. and Gašević, D., 2019. Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics. Assessment & Evaluation in Higher Education, 45(4), pp.554-567

Williamson, B., Bayne, S. and Shay, S., 2020. The datafication of teaching in Higher Education: critical issues and perspectives. Teaching in Higher Education, 25(4), pp.351-365.


Block 2: Week 6 Visualization

As we transition into the block of teaching with data, I am yet again thrilled to see how data influences the perspective of the teacher about the learner and how data-driven decision-making impacts Higher Education. To set the ball rolling, I sought to gather data around student engagements and investigate how much the knowledge of such data in the hand of a teacher will influence teaching on a more significant scale (Williamson et al. 2020). 

In order to achieve this, I decided to record all the times I shared my learning during the week. I divided my community into three namely: Learning, professional, and family & friends. Additionally, I recorded the elements of my learning that I shared and the trigger for sharing.  The data was gathered over a period of five days and my findings are as follows.

About 50% of my total learning engagement was focused on sharing about my blog activities and about 70% of the time it was with friends and family. This data in the hands of the teacher can help her know what elements of learning students tend to engage with more and this can inform her planning. However, in some cases, the reason for these numbers might be because am mostly around friends and family when recording data and doing my visualization and they tend to ask me a lot of questions about my drawings and paintings. This is why it is important that teachers are data literate to be able to decipher the reliability and accuracy of the data produced and not take it at phase value. (Williamson et al. 2020)

About 80% of the times I shared my learning on the assigned reading and pedagogy was triggered by either teaching someone something new or learning something new myself. Such data can help teachers know how to classify students in terms of personalized learning. This data can also help in empowering students who might need more support to engage with the content (Tsai et al. 2019).
I was also curious about what triggered the sharing of knowledge and how it relates to my learning. I realized that most of my knowledge sharing was triggered by conversations or my desire to know more about a subject matter. This data in the hand of a teacher can help identify different student personalities and know how to design to shape their learning. 


Williamson, B., Bayne, S., and Shay, S., 2020. The datafication of teaching in Higher Education: critical issues and perspectives. Teaching in Higher Education, 25(4), pp.351-365.

Tsai, Y., Perrotta, C. and Gašević, D., 2019. Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics. Assessment & Evaluation in Higher Education, 45(4), pp.554-567.


Week 5 Data Visualization


This week I decided to test how different events impacted my learning. I knew from the beginning that it was going to be a very eventful week, hence I capitalized on that and tried to do my analysis around this fact. Considering the fact that this was my birthday week, my sibling’s graduation, and my relocation to a new house I knew it was definitely going to have a toll on my learning. Hence I decided to record data to investigate how the different events and my emotions at different times will impact my productivity. Most of the events were towards the end of the week and others spread across the week so it was easy to have valuable data to record all through.


To properly understand what was going on I tracked my academic engagements and what event was distracting me during my engagement in that particular event. I decided to represent the activities as follows:

Downward and upward cone to represent the learning activities 

Sun moon and star represent birthday, relocating, and graduation respectively.

The greater than, less than and, equal to signs represent high, low, and moderate productivity respectively.

I used different emojis to represent different emotions. This was an attempt to datafy and personalize my learning to a degree.


  • The visualization did not give an explicit correlation between my emotions and my productivity is given a particular event.
  • If it was hard for me to link my emotions and their impact on learning I wonder how a machine will achieve this
  • I had assumed that if am tired and frustrated I would definitely perform low but I was surprised that it was not the case. This shows me that sometimes not so positive emotions do not necessarily have a negative impact on learning 
  • Also, I found that just because one is happy or calm does not mean they will have high productivity 
  • In relation to emotions and events, I realized that positive emotions were mostly linked with my birthday events. This showed the value of the individual 

End of Block Reflection

Over the last three weeks, I have been engaged in a series of data collection and analysis to make sense of how I learn and the different factors and behaviors that might have influenced my learning dynamics. The goal was to use this data tracking approach to understand my learning style in a more personalized way keeping privacy in mind. (Bulger, 2016).

Self data tracking to generate insights about learning was both exciting and foreign to me in the beginning. Data-driven technologies have been at the forefront of such procedures to determine student learning and now putting this task in the hands of the human as opposed to nudging human decision was fascination (Knox et al. 2019). The process of observing my learning with the use of self-tracked data made me feel empowered and trusted to understand insights about my learning and was not just an emotional irrational subject with actions and behaviors readable and modifiable by machines and algorithmic processes (Knox et al. 2019). However, I realized this supposed empowerment also came with a certain level of responsibility on the part of the student.

 Over the past weeks, I realized that the burden to decided the type of data to collect, how to collect it and what analysis to make rested solely on me and it was reassuring that I had a level of control over what gets out thereby exploring my agency (Yi -Shan Tsai et al. 2019). However, I needed to demonstrate a level of discipline and self-management to be able to attain the laid out goals. Pondering upon this now begs the question at what level of their learning process should students really be trusted to take full control of their own learning? 

This block also gave me the opportunity to learn a lot about myself through the quantified learning approach. I tracked data on various elements of my learning such as how my emotional state influence my productivity, how much time I generally spend on a task, my preferred times of working, the dynamics of my learning spaces, and how external factors influence my learning. Quantifying all of this information gave me a sneak peek into who I am and how that has influenced my learning approach (Eynon, 2015).

 I  am clearly aware of the complex nature of learning and reducing it to a set of quantities was something I grappled with all through the weeks. I thought about the flaws in my data collection process or the consciousness to leave out certain data because I dimmed them too personal to be revealed or my inability to measure certain elements of my learning. All of these made me understand that for every analysis there was this tension of reliability over richness as long as the tracking was manual (Eynon, 2015). 

Finally,  I came to learn over these weeks that sometimes the analysis made might be in contradiction with an already existing perspective about learning. When this happens I was always in a dilemma as to what elements to believe the data insights or my highly positive perception of my learning. In any case, I understand there are many circumstances influencing data that are not taken into consideration or just unmeasurable and all of this can influence the result but when the data and perception work together one can definitely have some insight about their learning.


Bulger, M., 2016. Personalized Learning: The Conversations We’re Not Having. Data & Society, [online] pp.1-29. Available at: <>

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

Knox, J., Williamson, B., and Bayne, S., 2019. Machine behaviorism: future visions of ‘verification’ and ‘datafication’ across humans and digital technologies. Learning, Media and Technology, 45(1), pp.31-45.

Tsai, Y., Perrotta, C. and Gašević, D., 2019. Empowering learners with personalized learning approaches? Agency, equity, and transparency in the context of learning analytics. Assessment & Evaluation in Higher Education, 45(4), pp.554-567.


Week 4 Visualization


The data visualization for week 4 was aimed at recording and keeping track of all learning activities engaged in this week. I also wanted to understand how much time I spent on an activity, and how that time was influenced by the preceding activity, and my motivating factor.

During the data collection process, I realized tracking the exact duration of activity was challenging due to work and family distraction hence I categorized the duration into three as demonstrated by the legend.

Objectification of the elements of the visualization

I decided to use three main colors to represent the kind of motivation that led me to engage in the activity and to some extent how I felt about that particular activity 

Green Color – This indicated that it was self-initiated activity and I was in high spirit and ready to engage with it.

Red Color – This indicated that I saw the activity more like a requirement and it was a burden to engage with it 

Blue Color – This indicated that I was calm and encourage to participate in the activity. I was relaxed and felt I could do it at my own pace.

For the preceding activities, I decided to use images that could best visualize the specific act.

Pot: To illustrates that I had just spent some time in the kitchen cooking a meal 

Pillow: To illustrates that I had just spent some time resting

Shower: To illustrates that I was bathing prior to engaging with my school activity 

Soccer Ball: Illustrates engagement in some sporting activity in the garden 

 Findings from the visualization 

From the visualization, I observed that over 80% of my activities were either perceived as self-initiated or encouraged. This says a lot about my learning in the sense that I tend to work better in a high spirit and relaxed space of mind. Also in terms of engagement duration, It was interesting to note that despite my high spirit, calm, and encouraging space for learning, the duration of activity was mostly under two hours.  I only went over when it was extremely vital. I am curious as to whether this is as a result of my concentration span or level of satisfaction at the time of completion of an activity.

Possible metrics to measure in future

What are my emotional state at the beginning and the end of an activity?  What is my level of satisfaction upon completion of a task and how my emotions influence this? How much of my learning activity is internet dependent?