Skills for the future and future of work are fraught with pervasive uncertainties. According to World Economic Forum predictions made in 2020, by 2025, as high as 50 percent of all employees will need reskilling. Does it mean that all these people should go for formal training? Besides, 60 percent of future jobs for which current nursery-age children will be looking have not been created. Hence, skill requirements for the future job market are unknown. Does it mean that contemporary education is not relevant to preparing the future workforce? Surprisingly, the answers are counterintuitive. As opposed to going for formal training, employees vastly rely on self-learning. And to enhance their self-learning ability, they need high-quality education to develop a strong foundation in theorization.
The focus should not be on training for skill development. Instead, we should focus on education to create a sound theorization framework among the future workforce. Consequentially, they will be adapting to the future skill demand through self-learning.
Although we cite the fourth industrial revolution, rapid technology change, and many more as the reason for upskilling, it’s not new. The underlying cause of changing skill requirements has been the role of technology in work. For example, farmers in developing countries have been using manual machines to spray pesticides. Once they switch to a power sprayer, their skill requirements will change. As opposed to keep manually pumping, they will turn on and off switches. For this upskilling, do they need formal training? Perhaps, no. Through learning by seeing and a bit of experimentation, most of them will acquire the skills by themselves. But is self-learning good enough in other more complex jobs? Even in high technology-intensive jobs like engineering software and information technology solutions, is self-learning good enough for upskilling? To get answers, let’s look into further.
Learning takes place through theorization:
Let’s take the example of teaching a child how to recognize horses. Of course, there are millions of variations of horses. It’s virtually impossible to find two horses with the same color, height, weight, and length. Do we need to expose the child to each of the millions of samples for recognizing horses? Fortunately, no. Through description and observations of a few samples, the child is going to develop abstraction about horses to recognize all other variations of horses.
Similarly, let’s teach a young boy how to ride a bicycle in a particular terrain. Through a bit of education on bicycle dynamics and experimentation, the boy is going to theorize the riding. As a result, he will be acquiring skills by himself how to ride a bicycle on all different terrains.
Let’s also assume that the boy changes the bicycle with an electric bike. Will he need the training to figure out how to ride this new bicycle? For sure, no. Hence, theorizing the reality in terms of a set of variables and their dynamic relations is at the core of the human ability to acquire skills to perform jobs and keep upskilling to adapt to variations.
Self-learning ability due to inherent capabilities:
Let’s assume that a boy shows up in New York City from rural Bangladesh. He had never visited towns or cities before. Hence, he does not know how traffic flow is controlled through red, green, and amber light signals. Does it mean that he must get formal training on safely crossing intersections? Interestingly, no. He will likely silently observe the situation and theorize the reality of the movement of cars and pedestrians in synchronization with light signals. We call it a mental model. Subsequently, through a few experimentations, he will fine-tune the theory and master the skill of how to cross the intersection safely.
Human beings have a set of inherent abilities. Some are sensing, perception, knowledge gathering, idea generation, experimentation, theorization, and skill acquisition. Instead of memorization, learning has roots in theorization. This theory guides us in skill adjustments for dealing with variations. Hence, we need to focus on sharpening our innate abilities and capability of theorizing.
Sharpening innate abilities and scaling up self-learning ability through education:
Learning and skill development begins with curiosity, observation, experimentation, and theorization. Yes, we have inherent abilities for all of these. Upon being curious, we observe for gathering knowledge about the situation in terms of variables and their relations—the essence of theorization. Subsequently, by performing experimentations through changing selected variables, we fine-tune our mental model or the theory about reality. Consequentially, we develop skills in manipulating target variables to get jobs done. Yes, we have ways to sharpen these innate abilities. But they quickly saturate.
To scale up our innate abilities to theorize about how reality works, we have developed a scientific approach. For example, the theory of projectile is the scaling up of our observation about how an object traverse upon being projected. This theorization has scaled up the ability to predict the object’s path upon being projected with a specific force at a certain angle. Consequently, it has scaled up our skill of designing all variations of projectiles.
To help the next generation to learn from current and previous generations, the human race has been theorizing the learning and delivering through education. Hence, education is a vital means to scale up our innate learning and skill development abilities.
Comprehending theories and the ability to theorization play a vital role in self-learning:
The objective of education is to let the next generation benefit from theories developed by previous generations. So that they can apply and extend them in enhancing skills for developing solutions to meet our requirements better than ever before. For example, Newton’s theories have helped the next generation to upskill from craftsmanship to mechanical engineering—without having direct training from Newton. Hence, having a comprehensive understanding of already developed theories and expanding them further contribute to our ability to improve our skills through self-learning.
Example from the software industry:
For engineering software applications, engineers need skills in programming and using library functions. For example, how to program in a specific language, like C++ or Python, is a practical skill. Or how to use a particular set of library functions like TensorFlow in developing target applications is the skill the industry demands. But, often, universities deliver education in the form of theories of computer programming, giving examples with specific programming languages. But in reality, there are dozens of programming languages.
Furthermore, new application-specific languages are being developed. For example, Python has been designed to ease the difficulty of engineering machine learning and data analytics-based applications. Similarly, like TensorFlow, many application-specific libraries are being developed. Yes, most university curricula are filled with relevant theories, as opposed to developing skills of how to program in a particular language or how to use a library function. Hence, to attain the skills of using varying programming languages and library functions, is the role of education in teaching the theory of programming language or machine learning irrelevant? Perhaps, No. Due to this reality, graduates with an education in Computer Science and Engineering have been addressing those skill requirements through self-learning, instead of training.
Focus on education for enhancing self-learning abilities and develop an interface for accruing the skills to enter the job market:
As explained, education has a vital role in scaling up self-learning abilities. But to leverage it, we should avoid memorization. Instead, the focus should be on developing the curiosity about the reality and developing the capacity of theorizing through the detection of variables and establishing relations between them. Hence, we need to sharpen their innate abilities, and to scale them, we need to expose them to the scientific approach. Furthermore, we should share with them theories that we have already developed.
Unfortunately, that theoretical foundation is not good enough to enter the job market. Employers have been asking for readily mastered skills. Before joining the job market, graduates fail to master them. Hence, we need to expose students to acquire those skills by experimentation with how to acquire them with the help of theories already taught. Therefore, we have to show the linkage of skill demand of the industry and relevant theories and sharpen other soft skills so that they get on the track of self-learning those skills. Once they qualify to enter the job market, their strong foundation of theories and ability to theorize will help them keep updating skills through self-learning. So far, data from the software industry are showing favorable indications.
Acquiring and improving self-learning ability:
- In developing self-learning ability, the learner should have a strong foundation in theories and the capability to theorize.
- To put that theory in the application, some of the self-learning steps are (i) monitoring skill trends, identifying learning goals and making a list of skills to master, (ii) questioning the significance of things and finding learning resources, (ii) developing an interest in learning challenges and cultivating self-motivation, (iv) organizing and monitoring own learning process and progress, (iv) doing experimentation and measuring performance in KPIs, and (v) keep practicing, getting feedback, collaborating, sharing and perfecting the skill.
- For many professions, like information technology and software engineering, Internet has been a resource for mastering skills through self-learning. Numerous blog articles with sample codes, YouTube Videos, and online courses are good self-learning resources.
- The question could be, what is the most challenging part of self-learning. Staying motivated and keep progressing to complete things on your initiative is difficult. As there is no consequence if you don’t get things done, it’s easy to get off track. Hence, strong will, self-discipline, time management, realistic career progression paths through skill acquisition, and maintaining a balance between conflicting requirements are must-have soft skills.
- Five principles in guiding self-learning skills are (i) participation through practice, (ii) repetition for perfection, (iii) relevance to target jobs, (iv) transference of skill to jobs, and (v) receiving and giving feedback.
Due to growing technology upgrades, skill requirements have been continuously changing. Perhaps, it will accelerate. Hence, we need to focus on self-learning to keep leveraging emerging skill trends, instead of getting off track.