The 4th industrial revolution is just around the corner, but will the next generation workforce be ready for it?

Why Learn Computer Science and Machine Learning in K12?

The next generation will face the most rapid changes in jobs ever seen. Even though it is hard to forecast what exactly they will be challenged with, we can tell that most technological shifts will surround Computer Science (CS), Artificial Intelligence (AI), and Machine Learning (ML). Having CS and AI as a core part of K12 education is crucial for preparing tomorrow’s workforce.

The next generation will face the most rapid changes in jobs ever seen.

How are Teachers Handling these Rapid Changes?

Our current workforce has dramatically changed over the last few decades, giving us a taste of how hard it may be to keep up with future technology changes. It is uniquely hard for a teacher to keep up with modern technology in their classrooms. Moreover, teachers who can keep up are typically hired by tech companies; thus, they stop teaching in favor of jobs which can pay 2x a teacher’s salary.

With that in mind, our case study presented below reflects on how teachers are able to successfully facilitate and learn the same subjects as their students.

We will further look into how students took advantage of the AutoAuto project-based learning platform and self-driving cars to master applications of CS, AI, and ML.

Lessons Learned Helping Schools Teach CS and AI

In the case study below, 150 6th graders used AutoAuto cars and learning platform during ⅓ of their STEM semester. Six classrooms worked every-other-day for a period of 1.5 hours each session.

How many learning sessions did the teacher complete in comparison to the students?

The teacher completed 68 learning sessions. The students (on average) completed 35 learning sessions. Indeed, the teacher was able to keep up with the students!

Did the teacher complete each learning session before her students?

The teacher was FAST. She completed 91% of the learning sessions within the first 9 days of the semester (counting weekends). Therefore, she was very equipped to facilitate her students throughout the entire semester.

The chart below gives a detailed timeline, showing when the teacher completed each lesson compared to all her students. By the end of the semester, she was able to facilitate over thirty different lessons within a single day – this makes for a robust learning environment where students can work through content at their own pace, favoring mastery over speed.

Other Takeaways

Advanced students reached advanced topics. The topics at the top of the chart (“Default parameters”, “Math functions and returning information”, …) are topics which were not scheduled for that semester. Nonetheless, students who moved quickly were able to reach and learn these topics. This is important for keeping them engaged.

Some students worked over spring break! There was no school during the third week of March, but you see some students logged in and completed lessons during this time anyway! (This was not mandatory.)

We are excited to be contributing to this needed paradigm shift in K12 education, and we look forward to equipping more and more teachers and their students.