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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.

References 

Bulger, M., 2016. Personalized Learning: The Conversations We’re Not Having. Data & Society, [online] pp.1-29. Available at: <https://datasociety.net/pubs/ecl/PersonalizedLearning_primer_2016.pdf>

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.

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Week 4 Visualization

Introduction 

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?

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Week1 Visualization

The Legend of the visualization.

My data visualization for Week 1.

The process of collecting data for my visualization this week was an interesting one. At first, I was uncertain as to what element of my learning to capture, but as I pondered and engaged with the readings I wanted to have a better understanding of my learning style and mechanism. I considered the two main elements of my daily activities namely: Academic and Work Engagements.

I used sticky notes to record all the times I was engaged in either a work or an academic engagement. As illustrated in the legend, I recorded data on the types of activities I was engaged in, the modality of the activity, the time of the day that activity occurred, and space I was in during the activity.

After creating my data visualization I made a couple of observations about my learning style and mode of working. In terms of learning space, I realized that over 80% of my work engagements took place in the home study however, my academic engagements were not confined to any one particular space but I was able to learn in a flexible and variety of spaces. In terms of learning style, I noticed that even though most of the course materials were text-based articles, I still opted for more visual materials or in some cases the audio versions of the articles. Another important point I noticed from the visualization was that I was very comfortable working during late nights on my academic engagements and the morning and afternoon hours were mostly dedicated to work based engagements.

Despite these intriguing revelations, I also realized that there were other metrics I was unable to measure based on how the data was collected or what data was actually collected. Hence, I would be more deliberate during my date collection process in the coming weeks.

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My Understanding of the 200 words blog explanation

I am having a bit of unclarity on what exactly the 200 words explanation should entail.

In my understanding I am considering structuring it in this way:

1. The process of data collection (How was the data collected)

2. What is being measured in the visualization. (What am I trying to find out)

3. Explanation of the visualization itself (Symbols and colors etc.)

4. What did I find out and what does it tell me about my own learning?

Am I missing anything here or adding some information that’s not needed?

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