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 situs slot gacor.
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
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).
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?
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.Kaynak : antalya haber
I am having a bit of unclarity on what exactly the 200 esenyurt escort 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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