The Role of STEM Ed in Preparing Future Global Talent
Presenter: Anders Hedberg, Global Leader in STEM Education Policy
When is the best time to begin preparing individuals for a global future largely characterized by its unforeseeable job landscape? While the exact shape of the future of work may be uncertain, one thing is for sure: STEM education will be a necessary component to navigating an undoubtedly technology-laden workplace. It is crucial, therefore, that STEM education—comprised of lessons in science, technology, engineering, and math—is taught from an early age and across all educational systems in order to best prepare students for an ambiguous future.
Innovation is crucial to economic development and wellbeing.
The 2017 Global Innovation Index, published by Cornell University, INSEAD and WIPO, revealed that there is a direct positive correlation between national innovation capacity and the economic development and wellbeing of a country. In order to improve a country’s wellbeing, therefore, it is necessary to focus on innovation capacity from within the country.
Innovation capacity can be used as a global equalizer. As revealed in the Global Innovation Index, the national economic development and wellbeing of a country is directly tied to its capacity to innovate, and therefore can help countries with marginal national wellbeing catch up to those with higher levels of wellbeing—as long as they can create innovative talent from within.
Innovation skills cannot be packaged or shipped. In order for countries with marginal wellbeing to increase their economic power, however, it is crucial to develop innovative capacities from within through the education of the younger generation—something that can only be done through strategic education that includes STEM-centered education from an early age.
STEM education needs to be infused into formal education systems.
STEM education encompasses not only science, technology, engineering and math but also concepts such as innovation, creativity, problem solving, critical thinking, and respect for evidence—all of which can be taught and nurtured in a school setting, and should be encouraged at the earliest level.
Empower teachers to be effective role models. Educators are the backbone of the education system and as such, it is important to support programs in which teachers are trained to help learners explore topics using inquiry-based instruction instead of the traditional system of simply providing mass amounts of information.
Introduce programs that help teachers become more familiar with different career paths. Similar to changing focus from traditional models of education to inquiry-based education, programs that educate instructors on different workplace environments and career practices are necessary in order to allow educators to more effectively give students real-world knowledge about the different career path options available to them, as well as provide classroom-based experience dealing with these different fields of work.
Be an advocate for an effective K-12 STEM education. It is important for all members of the community to engage with legislative and community leaders about the need for more effective STEM programs in today’s education system, as well as advocate for more partnerships between the education sector and workplace sector in order to create a more efficient talent pipeline.
“It is really about how we use the knowledge and how we contribute to society, the community and our profession that is the important outcome of education.” –Anders Hedberg
Productive Failure: Principles for Developing Talent for the Future
Presenter: Manu Kapur, Professor of Learning Sciences & Higher Education, ETH Zurich
With new and emerging technologies spurring rapid change throughout the world, there is much anxiety over whether or not our current educational infrastructure will be able to develop the talent necessary to keep pace with this evolution. Indeed, experts from across industries agree that the traditional, top-down approach most educational institutions use today will ultimately be rendered inadequate in preparing students for the unknown future landscape of work—and therefore, a new, more flexible model focused on creativity, innovation and learning how to learn is critical to developing the talent necessary to tackling future careers.
There are four critical principles derived from the science of learning.
Rather than focusing on traditional forms of learning such as rote memorization and pure data consumption, it is crucial for learning scientists, educators and students alike to learn how to learn in an effort to increase their ability to solve novel problems through creativity and innovation. Therefore, learning scientists have identified four critical principles that separate learning from simple information gathering:
Seeing vs. Encoding. There is a drastic difference in simply seeing information versus encoding it effectively. In a football match, for example, while both the coach and spectators receive the same visual information from the match, the coach (an expert) would view the game much differently than a crowd of novices. If you want to teach somebody about something you have expertise in, therefore, the first step is not to teach—it is to mentally prepare and prime them, and then to begin teaching them once the groundwork has been laid.
Knowing vs. Doing. While many jobs, such as carpentry, traditionally relied on learning through doing—for example, you wouldn’t expect a carpenter’s apprentice to have taken classes in physics, mathematics and business before beginning their training, despite the fact those are essential components of the carpentry trade—many of today’s educational systems have divorced learning from doing, where we expect students to learn the knowledge first and gather experience later. However, studies show that the best way to learn is to let the “knowing” be situated in the “doing”—in other words, to allow students to learn through experience.
Basic Knowledge vs. Creativity. In order to be creative and innovative, it is critical to first gather a base of knowledge upon which one can later play around with. Indeed, when knowledge is used as a conceptual toy, true creativity and innovation begin to occur more frequently.
Learning vs. Performance. While we intuitively understand that low learning and low performance result in unproductive failure—and that high learning and high performance result in productive success—the most dangerous form is low learning coupled with high performance, which results in a form of unproductive success. It is this illusion of success with very little actual deep learning that has become common throughout school systems, and has ultimately resulted in poor learning habits.
Productive failure is the most effective way of learning.
Productive failure—which is the result of high learning and low performance—is perhaps the most effective way to not only learn things on a deeper level, but also simultaneously encourage more creativity and innovation.
Productive failure can be simulated in a math classroom. In learning the concept of standard deviation, for example, an experiment asking students to calculate the most consistent striker from a set of football data was given to students in an effort to test their creativity and prime them for the eventual “correct” way to solve for the data. The experiment ultimately revealed that students most often used previous math concepts they had already learned—such as finding the average, calculating year-to-year difference, and taking the sum—in an attempt to solve this purposely-unsolvable problem. Eventually, certain students managed to “solve” the problem through graphing the changes in scores and stretching the results into a straight line using trigonometry.
Productive failure in the classroom setting can lead to deep learning. Because students were forced to think creatively before learning the concept of standard deviation, their minds were primed to better understand new math concepts due to their knowledge of how not to solve the problem. It is through this active doing of math—rather than simply learning about math—and productively failing that students are ultimately able to learn new concepts deeply and effectively.
“You have to design for creative practices while learning even very simple concepts.” –Manu Kapur
Why the Liberal Arts Matter in an Algorithmic World
Presenter: Scott Hartley, Venture Capitalist and Author of “The Fuzzy and The Techie”
While the nurturing of STEM-related skills in both K-12 and higher education is an undeniable necessity for students in today’s technology-centered landscape, the development of a liberal arts mindset may be just as fundamental in order to not only discover what it means to be human in contrast to technology, but to also learn how we can combine our humanity with technology to solve the world’s biggest problems. In fact, many of the founders of some of the best companies in Silicon Valley boast a solid liberal arts background in fields such as philosophy and linguistics and use these skills to not only think about the world from a human perspective, but to also apply technology meaningfully to problems they see in the world. Ultimately, it is this combination of humanity and technology found in these tech founders that demonstrates how it will take skill from both science and liberal arts to tackle the world’s biggest problems.
The way automation affects the future of jobs is up to us.
While technology will undoubtedly alter the landscape of jobs in both the near and far future, it is important to remember that jobs aren’t monolithic by nature but are instead comprised of a plethora of tasks of varying levels of complexity—and while many of these tasks will eventually be performed by much more efficient technologies, the most complex tasks will always require a human element.
There is a growing chasm between science and the humanities. While many are beginning to understand the significance of both science and liberal arts working in conjunction with each other, the decades-old dichotomy between the two remains persistent throughout educational institutions, despite evidence that it is only through the combination of science and humanities that we can successfully navigate the future and ensure humans remain part of the equation.
Oxford University has found that 47% of US jobs are at high risk of being affected by automation. Similarly, the McKinsey Global Institute has found that around 30% of tasks in 60% of jobs are tasks that could be technologically automated in the coming years. These tasks will most likely be comprised of more simple, routine and manual responsibilities associated with low-skilled duties, however.
Humans will be in charge of more complex tasks. Because automation will be able to efficiently accomplish the simple-but-necessary low-skilled tasks, humans will be freed to focus on more complex tasks, including those those that require a lot of cognitive thought and a break from routine—such as improvisation, collaboration and empathy. And because the liberal arts places heavy emphasis on these aspects, it suffices to say that liberal arts should remain a necessary part of any student’s education.
We need to know how to ask the right questions.
There is a lot of talk about data science, but not enough about data literacy. While we live in a world of overwhelming amounts of data, figuring out how to interpret this data and ask the right questions is a difficulty necessary to understanding technology—and by extension, ourselves—on a deeper level.
People from all different backgrounds need to participate in answering hard-pressing questions facing the technology sector. Analytics company Kaggle is one such example of how innovative question-asking can lead to real answers. Having found that a large portion of data is locked up in silos, Kaggle decided to partner with NASA, the European Space Agency and the Royal Astronomical Society in an effort to release previously private data on dark matter and create a competition around this data that asked the question of how best to quantify the amount dark matter in the universe. Within two weeks, a glaciology student from Cambridge was able to use their background in glacier sciences to accurately quantify the amount of dark matter in the universe, a task that had taken each of the agencies years to find a solution to.
It is about IA (intelligence augmentation), not AI (artificial intelligence).
While we often view algorithms as standing for truth and objectivity, at their core they are merely a series of determinations and questions asked by the coder. Viewing technology as an extension of humanity rather than an objective truth, therefore, has the ability to reveal new insights into how humans can design technology to better our businesses and better ourselves.
Data science can be used to mitigate human bias. Stitch Fix—a Netflix-like clothing subscription company—is one such example of how machine learning and humans can work together to create a successful business. Using data science to help machine learning determine the clothing preferences of their consumers, Stich Fix’s 3,400 stylists are able to de-mitigate any biases—such as the age and location of their consumers—that would affect the stylist’s ability to objectively suggest clothing preferences.
Questions of ethics need to be posed in technology.
In today’s attention economy, many technologies are being optimized for a lean-back world where users scroll through services that are monetizing based on engagement and time spent within the app. However, technology should be something that enables us to perform better, and therefore we need to tackle technology from a more philosophical and social perspective that focuses on the betterment of our individual selves and the human species as a whole.
We need to blend philosophers with engineers. For example, employees at Quora, a question and answer website, were faced with an important question early on in the development of their company: should their website allow users to post writ-large, or should a moderating system be enforced? With similar questions found throughout tech and non-tech companies alike, it is important to have employees with experience in fields such as philosophy, ethics and even constitutional rights available to provide more human perspectives on the less-technical aspects of a company.
“While we think about the importance of STEM, we can’t disentangle that from the need for broader context as well.” – Scott Hartley
Practical Solutions to Closing the Tech Skills Gap
Presenter: Anand Chopra-McGowan, Head of Europe, Global Head of Consumer Practice, General Assembly
With technology advancing at an unprecedented rate, the tech skills gap is bigger than ever before. The General Assembly, which is an organization created by the United Nations in an effort to pave transformative career pathways for people in exactly the areas employers desperately need, is one such institute that is tackling this problem through the creation of campuses and individualized programs for companies struggling with the technology skills gap. Fortunately, with over $110 million in venture funding, the General Assembly and other similar venture funds may just have enough capacity to begin closing the tech skills gap—and many are already well on their way to doing so.
There are several forces affecting the tech skills gap.
While there are many forces affecting the tech skills gap both negatively and positively, the near future will see the biggest impact from these three forces:
Force One: Automation. Because companies are beginning to implement advanced software and artificial intelligence that is often more capable at effectively accomplishing tasks than their human counterparts, many companies are starting to reconsider the ways in which they deploy their talent and human resources—including how many human employees are actually necessary.
Force Two: Digital revenue streams. Digital revenue streams are not only increasing dramatically within tech companies, but across all sectors as well—especially the industrial, retail, cosmetics and packaging industries. Because businesses from all sectors are digitizing many of their services, therefore, data scientists and software engineers will continue to be in high demand in virtually every company.
Force Three: Talent with digital skills is lacking. In current and future talent pools, workers with sufficient digital skills are failing to keep pace with the increasing demand for their expertise, despite efforts in education and talent development to alleviate this problem.
There are a plethora of potential solutions to closing the tech skills gap.
Solution One: Expand the talent pool. Rather than looking for new talent through traditional avenues, look in unconventional places—such as graduates with humanities degrees or even your own employees. In the insurance industry, for example, many large insurance companies are pushing for new data-driven insurance policies and are consequently in need of data scientists. Rather than hiring from an empty talent pool, however, some companies have turned to their internal actuaries, whose duties as risk profilers and data analyzers have already given them many of the skills data scientists require. Similarly, companies like Capital One have begun tackling their lack of data scientists through the creation of 6-month long developer academies that can turn liberal arts graduates into software engineers—many of whom often out-perform traditionally trained software engineers.
Solution Two: Follow the money. While many companies’ talent acquisition budgets account for as much as $25,000 per person in hiring new talent, their learning and development budget often only puts aside around $1,000 per employee per year. By allocating the talent acquisition budget into the learning and development budget, however, companies can actually begin to save money and time by re-training current employees to take on any new skills necessary to expanding their company’s technology capabilities instead of hiring new workers.
Solution Three: Bring data to the conversation. Whether or not an employee is well versed in data, every employee should be encouraged to come up with new solutions, ideas and products based on data and facts. In order to increase digital literacy, therefore, it is important for companies to train a large portion of their employees in different data-oriented skills. The General Assembly’s work with Booz Allen, for example, allowed employees of varying skill levels to participate in learning courses best suited to their already existing skillset in an effort to dramatically shift the skills of existing employees to fill the data skills gap the company was experiencing.
Solution Four: Rethink outplacement programs. As companies are forced to lay off employees as they change their talent pipelines, existing severance packages and outplacement training programs are often ineffective in preparing former employees for a new job. However, Liberty Mutual, an American insurance company, decided to design a 3-month course in partnership with General Assembly in an effort to not only prepare former employees for the skillset they will need to successfully navigate any new job, but to also provide them the opportunity to re-apply for their jobs back at the company if they are able to successfully complete the course.
“We’re seeing that companies are taking matters into their own hands—not necessarily sitting back and complaining about the impact of automation and the changes on their workforce, but instead coming up with creative solutions and new ways of closing that talent skills gap and making it work for them so that people can pursue the work they love.” –Anand Chopra-McGowan
