Artifact
The Nike+ app, which at the end of my run the other day, looked like this:
(editor’s note: yes, I’m slow. Thank you for noticing. Also, along with some encouragement in data format, I had Tim Tebow give me words of encouragement for bettering my pace.)
Now, there are a lot of numbers here, but I’m primarily interested in that last piece.
“You ran 0.10 mi more and 0’56″/mi faster than the average of your past 7 runs”
Why did Nike+ choose my past 7 runs? Was there some sort of algorithm to maximize how good I feel about myself?
Sadly, I think not. Witness my previous run:
So it looks like it just takes your past 7 runs and compares your mean distance and pace. That’s not very good motivation, now is it Nike+? Can we improve (at least, in my opinion it would be an improvement) Nike+’s distance and pace comparison to help the runner feel better about his or her progress?
Guiding Questions
- Would a different measure of central tendency lead to a different, and perhaps more encouraging, data capture?
- Would averaging a different number of past runs lead to a different, and perhaps more encouraging data capture?
- Could we write an IF…THEN or other type of algorithm to encourage the runner?
Suggested Activities
- Give some runner data (either fabricated or authentically generated; shoot, you can use my data if you want) and ask students to describe after each run “what should the app say in order to give the runner a sense of accomplishment?”
- Once students have done that with individual data points, have students sketch out an algorithm or decision tree.
- Test that algorithm or decision tree against a new set of runner data.
- Compare decision trees and algorithms to see who’s is the “positivest”(?).
- Turn into algebraic expressions if you want, presumably to help out the coders.
There are few things more discouraging than seeing that I’m actually running slower than my seven previous runs averaged out. At least package the data so I don’t feel like I’m out of shape.
emergent math 