Personal Analytics Explorations to Support Youth Learning
Summary (2 min read)
BACKGROUND
- Many who have documented self-tracking and personal analytics practices identify 2007 as the year in which a community and sociotechnical movement (the “Quantified Self”) emerged in the Silicon Valley, with Wired magazine editors Kevin Kelly and Gary Wolf being attributed as primary initiators (Choe et al., 2014; Lee, 2014).
- The two cases are also presented with an eye toward diversity to show the different kinds of data that could be obtained by youth and to demonstrate that personal analytics learning activities are usable with youth at different grade levels, with different levels of technology, and in different participation structures (i.e., working individually vs as a whole class).
- Immediately following, she began to consider a number of sleep variables that could impact her mood.
- While other girls participating in the project shared their own observations, experiences, and difficulties with the devices that they had tried, Melissa proceeded to pay partial attention to them and also entering the new values she obtained from the Up device into the spreadsheet she had started the week prior.
Commentary on the Playground Walking Case
- This in-class discussion about steps taken to get across the playground at school was not originally planned.
- The authors had expected that students would walk normally and produce normally distributed data through this activity.
- In the classroom, where there is a larger social setting in which multiple people are examining the data, gaining insight can come about by noticing what others can see and hearing how they explain the data.
- This represents an important consideration for how personal analytics activities could be sensibly implemented in schools and other learning environments.
- Thus, the authors suggest from this case that the combination of personal connection to the data, along with the software tool and the teacher’s facilitation of the discussion all jointly enabled the realization of a substantive learning opportunity.
CONCLUSION
- The overarching aim of this chapter was to suggest that personal analytics approaches represent an important area of personalized learning in today’s digital ecosystem.
- In contrast to other forms of personalized learning experience represented in this volume, the emphasis when using personal analytics for learning is to have the learner create data from their own activities and then be in the position of examining their own data.
- What is to be taught and learned by students is not automatically determined nor recommended to them; it is encountered in the process of making sense of what the data say about the students and their experiences.
- The first was of a high school student who participated in an afterschool program where she used a wearable device to explore possible relationships between her sleep and her mood.
- According to their records, she seemed to get there.
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Citations
32 citations
Cites background from "Personal Analytics Explorations to ..."
...in the data based on their own experience” [23, 24, 41, 47, 63, 64]....
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...This has also been called personal analytics (Lee, 2018) or personal informatics (Li, Dey, & Forlizzi, 2010)....
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References
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"Personal Analytics Explorations to ..." refers background in this paper
..., Kopp, 1988; Lee & Drake, 2013; Wallace, 1977, Wheeler & Reis, 1991), the widespread availability of consumer-level mobile and wearable devices and automated data collection systems (such as clickstream recording) has reduced some of the initial barriers associated with analyzing data about one’s self (Lee, 2013) and popularized this approach....
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...…Wheeler & Reis, 1991), the widespread availability of consumer-level mobile and wearable devices and automated data collection systems (such as clickstream recording) has reduced some of the initial barriers associated with analyzing data about one’s self (Lee, 2013) and popularized this approach....
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55 citations
"Personal Analytics Explorations to ..." refers background in this paper
...While data collection and inquiry about one’s own self has been practiced for several years in a range of communities (e.g., Kopp, 1988; Lee & Drake, 2013; Wallace, 1977, Wheeler & Reis, 1991), the widespread availability of consumer-level mobile and wearable devices and automated data collection…...
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54 citations
"Personal Analytics Explorations to ..." refers background in this paper
...However, in both the cases presented here, and elsewhere in the gradually accumulating literature (Lee, 2015), there is evidence of learning and growth by building upon experiences that youth can have when they examine data they have obtained about themselves....
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Frequently Asked Questions (6)
Q2. What have the authors stated for future works in "Digital technologies and instructional design for personalized learning" ?
In looking toward the future, learning activities for individual novices and youth doing personal analytics work should incorporate supports to help students notice patterns and understand more about the phenomenon that they are quantifying through their projects. Still, they were able to use those records in productive ways, suggesting that while personal analytics is gaining in prominence because of increased availability of individual tracking devices, those are not absolutely essential for this sort of instructional approach to work. Thus, the prospects for personal analytics learning explorations to become an option for those who want to support personalized learning are promising.
Q3. What was the commonly used data visualization software in their studies?
The most commonly used data visualization software in their studies was TinkerPlots, a novice-friendly data visualization tool developed to enable elementary students and above to use drag and drop interactions to produce dynamic data visualizations (Konold & Miller, 2005).
Q4. What was the first case of a student who used a wearable device to explore possible?
While each student had a wearable device that would track their steps and the students examined data from those throughout the larger unit, the specific activity discussed here involved analytics on manually collected data.
Q5. What are the prospects for personal analytics learning explorations?
There are questions that remain for us as a field to examine, such as what conditions promote an initial desire from students to look at their own data, how to support learning with different software tools, and what kinds of social configurations around a personal analytics learning activity enables broader participation and deep inspection of data.
Q6. What did the girl think of the exceptions?
It took another girl’s comment that even though those exceptions existed, there did appear to be some upward152Personal Analytics Explorations to Support Youth Learningtrend with the “Dragging” bin having much lower values for minutes of sleep and the “Pumped Up” bin having the second highest number minutes of sleep.