How Education Can Take A Page From Tesla’s Book

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People usually don’t talk about car design and higher education in the same breath so, understandably, it may sound a bit odd to suggest that educational institutions have something to learn from Tesla. As the CEO of an online coding bootcamp, however, I’ve discovered that Tesla’s approach to driver experience can be applied to the student experience. In fact, I am a firm believer that all educational institutions have something to gain from taking a serious look at Tesla’s approach.

Tesla makes great vehicles, but most Tesla owners agree that the firmware that gets downloaded to the vehicles every few months makes the driving experience even better over time. Owners come to anticipate those firmware upgrades and typically view the changes — sometimes significant and sometimes incremental — as a huge asset. If you’ve been driving a Tesla for two years and take a snapshot of your driving experience at the end of the second year and compare it to the driving experience you had when you first bought the vehicle, the improvements are obvious and often remarkable.

Other car manufacturers also roll out upgrades, but typically, the upgrades only happen as new models are released. Improvements may benefit future owners, but they don’t benefit drivers who have already invested in a current model. Tesla addressed this common problem by building an effective feedback loop into their car design. As more educational programs move online and analyze data to improve their programs, these institutions now have a unique opportunity to emulate Tesla’s approach.

Improving student experience is an evergreen challenge for educational institutions, and the reason is simple: Schools, colleges and universities have historically lacked the tools and, at times, the motivation needed, to acquire relevant data on how students learn best. Certainly limited data existed, but it was generally collected after the fact — in the form of end-of-course evaluations or instructor surveys. While institutions sometimes benefited from these post hocinsights, students generally did not. After all, if you discover that students haven’t grasped the course materials when a course is already over, the insight holds no direct value for the students who were on the receiving end of yet another substandard educational experience.

Now, for the first time, we have tools to collect and mine massive amounts of data on student learning in real time. For example, it is possible to track the progress of entire cohorts of students over time to better understand how students learn and how long it takes them to master specific skills under specific conditions. Armed with such insights, curricular and pedagogical decisions don’t need to be based on intuition or guesswork but can be based on best practices backed up by empirical evidence. There are indications that educators and administrators increasingly recognize the value of such metrics, but are we making the most of these insights to improve student experience when it truly matters, during the course of a student’s program of study?

One of the limits of traditional college and university programs is that change tends to happen very slowly. Once a course is underway, it can be nearly impossible to modify the curriculum or format, alter assignments to meet a specific learners’ needs or identify instructors who aren’t performing adequately. As a result, there is a lack of accountability for cost and outcomes. This is where I believe that the coding boot camp industry can have — and in some cases already is having — a huge impact on student experience.

Here are just three ways traditional academic institutions can take a page from coding schools and leverage data to create relevant insights a la Tesla:

1. The learning experience: With software to collect real-time data about learners’ progress, it is now possible to offer programs that respond to individual student’s needs — not just the perceived needs of an entire class or cohort.

2. Curriculum: Granular changes can be made to the curriculum on a daily basis (e.g., to respond to new demands from the market or defuse any proven sticking points for students).

3. Instruction: As data is used to refine and personalize the learning experience, it can also ensure that instructors and mentors provide the highest quality of support at all times. After all, why wait until a course ends to determine whether an instructor or mentor has done his or her job effectively?

At Bloc, the coding boot camp I lead, we’ve attempted to emulate Tesla’s way of doing things in our approach to the student experience. Indeed, if a Bloc student takes a snapshot of the student experience they expected upon enrolling and compares it to the experience they actually had during their program, there is a big difference — because our team is constantly improving the student experience. While most coding academies recognize the value of data, 100% online programs such as those offered at Bloc, K2 Data Science and Infosec Institute are especially well-positioned to use data to make swift, continuous changes to programs on an ongoing basis.

The bottom line is that the more data we collect and analyze about the student experience, the better positioned we are to lower and even remove barriers to their success. We do this by ensuring students have the knowledge and skills needed to proceed to the next step and by constantly improving our educational programs with the use of data. This approach dramatically improves the odds that students’ investment will yield the returns and outcomes they seek. It is also why educational institutions just may have something to learn from a car manufacturer after all.

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