The problem with education is that is it is often abstract and does not relate directly to the lives of the students that it aims to help. This results in a lack of engagement from students because they do not see the relationship to the content that is being taught and how it applies to their own experience. As a result, I am passionate about connecting concepts and developing experiences using rowing data that provide students with the chance to develop life-long competencies in a context that also provides health and wellness benefits.
I have spent the last couple of years developing an app that functions with the PM5 monitor on the Concept2 rowing machine. Rowing STEM is currently available for free on the iOS app store. It contains all of the curriculum used in the gROW Tulsa outreach program and data collection functionality when the rowing machine is being used. Consequently, it provides a sandbox that allows middle school and high school students the chance to experience what data means and subsequently analyze it using extensive graphing functionality.
All of the following experiments can be accomplished by using Rowing STEM by choosing the Collect Data button and pairing the machine monitor with the app via Bluetooth.
Each experiment has a protocol and then the data can be analyzed using the Access Data From Labs option and choosing the data that the student has created.
Included in each experiment in the Data Analysis section are Instructions for processing the data on the data analysis screen with the scatter plot.
Ensure that the students are well warmed up before each experiment.
Rowing Data: Time/Distance Graph Experiment
Protocol
Instruct the students to collect data using the following rowing protocol:
1-minute medium pace rowing (5 out of 10 effort)
1-minute really slow rowing (2 out of 10 effort)
1-minute fast pace rowing (8 or 9 out of 10 effort)
Data Analysis
Access Data From Labs → select data. Then select TIME for the x-axis and DISTANCE for the y axis.
Discussion
Discuss with the students why the slope of the line is less steep in the middle and is steepest for the last section. The slope of the line is distance divided by time which equals speed. Students should be able to connect their experience of rowing faster and slower on the machine with the slope of the graph.
Example Plot
Rowing Data: Accuracy and Precision Experiment
Instruct the students to collect data using the following rowing protocol:
3 minutes of rowing at a particular stroke rate of your choosing. (for example 20 strokes per minute). Instruct the students to row as consistently as possible to ensure that their pace is as consistent as possible and their stroke or drive length is as consistent as possible.
Data Analysis
Access Data From Labs → select data. Choose the scatterplot (will be the default graph)Then select DRIVE LENGTH for the x-axis and STROKE RATE for the y axis.
Discussion
Discuss with the students the spread of the points on the scatterplot. How close are they together (precision)? How close are they to the chosen rate? (20 spm for example – this is the accuracy)
| Accuracy: The darts or dots are located within the bullseye of the dartboard. In other words, a measurement is an actual amount being measured. Precise: Each dart or dot is clustered close together. In other words, measurements are repeatable and consistent. |  |
If your rowing stroke data points are close together then this is an example of precision.
If your rowing stroke data points (in this case stroke cadence) are close to the selected rate you choose for them, then that is an example of accuracy.
Extension
Organize a competition to see who can get the points closest to the desired rate and who can get their data points as close together as a group as possible. To progress the activity, try to row for a longer period of time, which should make the difficulty harder.
Example Plot
Rowing Data: Stroke Length and Power
Instruct the students to collect data using the following rowing protocol:
1 minute of rowing with just legs
1 minute of rowing with legs and back but no arms
1 minute of rowing with the whole body
Data Analysis
Access Data From Labs → select data. Then select DRIVE LENGTH for the x-axis and POWER for the y-axis.
Discussion
Ideally, as long as the students are rowing consistently, they should see that the longer the stroke is the more power that they create. Point out the general trend, which should suggest a linear relationship. Discuss how closely the data points make a straight line and involve the students in a conversation. If the student data is not looking like a straight line, discuss ways to make their rowing more consistent and try the experiment again.
Definitions
A linear relationship is when if one variable increases by a factor then the variable that is related to increases or decreases by a factor in proportion. For example, if you increase the size of a square shape, the area inside the square will increase in proportion to the increase in the side of the square.
Example Plot
Rowing Data: The Relationship Between Power and Pace
Instruct the students to collect data using the following rowing protocol:
1-minute of light rowing (think 2 out of 10 effort)
1-minute of light/medium rowing (think 4 out of 10 effort)
1-minute of medium effort rowing (think 6 out of 10 effort)
1-minute of hard effort rowing (think 8 out of 10 effort)
Data Analysis
Access Data From Labs → select data. Then select 500M PACE (SPLIT) for the x-axis and POWER for the y-axis.
Discussion
Ideally, the students should observe a relationship that is not linear. It takes increasingly more power to create the same decrease in pace (which is actually rowing faster!). The point here is that the relationship is an example of an inversely proportional relationship rather than a linear relationship. It might appear to the students initially that this is a linear relationship, but engage them in conversation about the shape of a line or curve in this case that would fit the points best. Essentially, as one variable increases the other variable decreases.
The actual formula for this relationship is power = 2.80/pace3
Therefore power is inversely proportional to the cube of the pace.
As a result, the faster you the row, the amount of power needed increases at a faster rate for each increase in pace (or decrease in numeric value).
Definition
An inversely proportional relationship is one in when an increase in one variable results in a decrease in another variable. For example, the time taken to complete a certain task will decrease when the number of people involved in completing the task increases.
Example Plot
In conclusion, these are just four examples of the direction we can go with this app and the integration of rowing data and academic education.
As stated previously, RowingSTEM for iOS provides a sandbox type environment to access many of the different metrics that the Concept2 rower collects or derives.
If you see areas of improvement, have any feedback or would like to use the platform for your own club or classroom, please do let me know. You can contact me at neil@coachbergenroth.com.
