Part 3: Growth and Achievements

The importance of STEM (Science, Technology, Engineering, and Mathematics) education cannot be overstated in our rapidly evolving world. Our partnership between the International STEM League and Rooted School Vancouver, funded by the Innovation Grant, implements the 5E Inquiry-Based Instructional Model in our STEM Fridays program to bridge the gap between theoretical knowledge and practical application. This framework, grounded in cognitive psychology, constructivist learning theory, and best practices in STEM education, engages students in a comprehensive learning experience. Here’s a look at how our program fosters positive math mindsets and growth through the STEM Fridays program.

Where Are Students Regarding Goals at the Beginning of Week 5?

A. Students self-started by gathering materials as they entered the room and assigning tasks to each team member. (6/6 groups)

B. Students carefully measured their tracks. (6/6 groups)

C. Students checked the cumulative data to determine what three data points they would gather and report. (2/6 groups)

D. When data was entered into the Excel spreadsheet, students noted how the new data changed the patterns on the graph and altered their decisions based on the new trendline. (2/6 groups)

E. The data sheets are filled out with three different weights for each team's cars; the average times and speeds are calculated, and speed is multiplied by weight for the Power Factor Score. (4/6 groups)

"At the conclusion of the activity, all groups acknowledged the utility of the Excel sheet extended beyond mere calculation verification. Following the 5E lesson format, the class transitioned into an EXPLAIN discussion. Two groups briefed others on the rationale behind their adjustment in investigation plans, prompted by the revelation from accumulated data that low-weight cars did not yield high scores. Despite their high speeds, the scoring system's multiplication of speed by weight favored heavier cars. A student inquiry regarding potential outliers sparked a discourse on possible explanations. The day concluded with accolades for teams whose cars demonstrated the highest efficiency, as reflected by their scores.

Week 6 Progress

A. Students self-started by gathering materials as they entered the room and assigning tasks to each team member. (6/6 groups)

B. Students carefully measured their tracks. (6/6 groups)

C. Students checked the cumulative data to determine what three data points they would gather and report. (5/6 groups)

D. When data was entered into the Excel spreadsheet, students noted how the new data added to the patterns and altered their decisions based on the new trendline. (4/6 groups)

E. The data sheets are filled out with three different weights for each team's cars; the average times and speeds are calculated, and speed is multiplied by weight for the Score. (6/6 groups)

Throughout the week, we delved into the Goldilocks problem they grappled with. There exists no definitive answer to this puzzle. The data hinged on a single variable under their control: the car's weight. As they affixed weights onto the vehicle for each timed test run, they gradually realized that the swiftest cars weren't necessarily the victors due to the scoring system's intricacies. The descriptive formula began to resonate with them, unveiling its significance.

Weeks 7-8: Achieving Goals

By Weeks 7 and 8, all six groups had achieved the following goals:

A. Students self-started by gathering materials as they entered the room and assigning tasks to each team member.

B. Students carefully measured their tracks.

C. Students checked the cumulative data to determine what three data points they would gather and report.

D. When data was entered into the Excel spreadsheet, students noted how the new data added to the patterns and altered their decisions based on the new trendline.

E. The data sheets are filled out with three different weights for each team's cars; the average times and speeds are calculated, and speed is multiplied by weight for the Score.

Students asked to use the cars for an entire period, and we changed the schedule to make it the first item of the day. As groups came in, they immediately began to collect data at different weights and enter their times into the class data sheet. We created a new table together, assuming the latest data may be more precise than some collected in the program's first weeks. The Excel sheet calculates the average times as they entered up to three times for each run. It calculates speed and multiplies it by weight to get the Score. Together, we looked at the data with different trendlines, settling on the exponential one. We used this opportunity to review various data visualizations and which charts are most valuable for various data sets.

Groups started to play with the data and predict where the weight would become high enough to reach the highest turnaround point on the graph. They tested cars at various weights that fell exactly on the trendline to test how the actual Score related to the predicted one. Several teams decided to work together to find what weight was so heavy the car would move so slowly it would barely make it to the finish line. By the end of the period, students had bracketed the weights that could be used to win the challenge.

Weeks 8-9: Advanced Data Analysis

We added an equation to the trendline, and students recognized it as a polynomial, similar to the ones they were trying to solve in math class. The difference was that they had generated this one and understood that any point "y" on that trendline represented a prediction of the power factor of their car at the corresponding weight on the "x" axis. The teams had become very competitive, and we noticed they were checking batteries to be sure they had a full charge before the first run. They were not driving except on the track and wasted no battery power. As a class, we took time to generate data for a different scale car and one with a different set of wheels. It was interesting to see that while the range of scores differed, the shape of the curve remained the same and offered the same insights. We discussed the power of controlling variables and precision measurements as we added Pearson's r to our graph as a way to measure the strength of the relationship between two variables. R² = .6769.

We played. There was one early outlier in which the time had been recorded as 15.6 seconds, and rather than having three different times to average, the students had just repeated that number on their datasheet. It was early in the program. We decided to look at what would happen if we changed the time to 5.6 seconds, which was probably more accurate. As we changed the first entered time, the r factor adjusted toward 1. It did so again when we corrected the second and third cells. This change was a clear visual of the importance of precision measurements.

Week 10: Final Competition and Reflection

This week, we had the final competition for this first challenge. We created a new spreadsheet for the day while keeping the historical data on the monitor. Teams were given new batteries and diligently checked them with multimeters to be sure they had full charges. They were given 30 minutes to get as many data points as they could add to the graph, but today, only their highest score would count toward the win.

It was amazing to watch this group that two months prior was only interested in "playing with the cars" perform careful test sets with varying weights and adjust their plans as their data and the class data gave them new insights. One team had the highest Score, though many were close. Awards were made for the top two places and others for varying skills demonstrated by teams showing progress toward mathematical reasoning.

Fostering Student Motivation and Self-Management

A key component of our approach is fostering student motivation and self-management in learning math. By integrating competitive elements, such as weekly awards and group challenges, students are encouraged to take ownership of their learning. The RC car challenges, in particular, provide tangible goals that students can work towards, making math more engaging and relevant to their interests.

The variety of activities offered in the Tech & Engineering and T.E.A.M.S. stations also helps to cater to diverse interests, ensuring that all students find something that motivates them. By linking math skills to real-world applications and creative projects, we help students see the value of math beyond the classroom.

Through these strategies, we aim to develop not only students' mathematical skills but also their confidence and enthusiasm for learning. As students become more engaged and motivated, they are more likely to take responsibility for their progress, leading to better outcomes and a deeper understanding of math.

Looking Ahead

We look forward to the next challenge, The Driving STEM U-Turn Challenge©. There are more variables to consider, not the least of which is driving skill. Students will receive the iNSL Problem Solving Certificate© at the end of this series. They will be encouraged to use data-driven problem-solving skills to maximize their performance in an Esports and Gaming club, an iNSL iRacing Innovation Challenge, or an International STEM Challenge using tenth-scale RC cars. Following STEM Fridays, iNSL facilitates a student gaming club with competitive, data-driven Esports, including iRacing and Online Chess.

The journey continues…