Personal Analytics Explorations to Support Youth Learning
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Citations
Re-Shape: A Method to Teach Data Ethics for Data Science Education
Remembering What Produced the Data: Individual and Social Reconstruction in the Context of a Quantified Self Elementary Data and Statistics Unit
On researching activity tracking to support learning: a retrospective
What is Data? - Exploring the Meaning of Data in Data Physicalisation Teaching
Personalization With Digital Technology: A Deep Cognitive Processing Perspective
References
Physical Activity Data Use by Technoathletes: Examples of Collection, Inscription, and Identification
Related Papers (5)
Teachers as Producers of Data Analytics: A Case Study of a Teacher-Focused Educational Data Science Program
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.