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Learning must be informed by scientific research and by establishing evidence-based feedback-loop. This is why the idea of a “Science Of Learning” attracted deep interest from people across different fields. This includes psychology, education, neuroscience, and technology, as well as from practitioners. The applied nature of Science Of Learning can be seen more and more in designing real-world learning contexts like a classroom, work environment, online learning, sports, etc. 

Evidence-based scientific principles are crucial for us to thrive in the dynamic world driven by exponential technology changes. This is why the “Science Of Learning” as a body of knowledge and understanding its core tenants are of utmost importance.

What is the Science Of Learning?

The Science Of Learning is a systematic and empirical approach to understanding how people(Or Organisms, Animals, Society, Organizations, Machines etc)learn. Richard Mayer defined the science of learning as the “scientific study of how people learn”. According to him Learning depends on the learner’s cognitive processing during learning and includes (a) Selecting: attending to the relevant incoming material; (b) Organizing: organizing the incoming material into a coherent mental representation; and (c)Integrating: relating the incoming material with existing knowledge from long-term memory. 

Even though this outlook is very satisfactory in explaining the process of human conscious learning, the real scope of the Science Of Learning is much bigger and can be represented by a much broader set of domains collectively called “Learning Sciences”. Norbert M. Seel the editor of The Encyclopedia of the Sciences of Learning prefaces the compendium as an indispensable source of information for scientists, educators, engineers, and technical staff active in all fields related to the learning of animals, humans, or machines. This suggests that Learning Sciences are much broader and generally covers three broad areas related to learning Animals, Humans, and  Machine Learning. 

The discourse about the Science Of Learning can be further broadened by zooming back into the big picture evolutionary perspective(Adaptation as Learning to fit into a niche and an ecosystem.). Further the scope of Learning Sciences can be expanded to Animal-AI fusion and other kinds of bio-intelligence development with the advancement in technologies like synthetic biology. 

Let us look into each of these dimensions of learning in focus and try to unpack the general outlook of the scope.

1. Learning from an Evolutionary perspective.

Learning is an integral part of an organism’s (any animal, plant, fungus, protist, bacterium, or archaeon on earth) biological adaptation, and like any other adaptation, learnability is the outcome of evolution by natural selection. Because it is acted upon by natural selection, learning in different species of organisms exhibits modifications and specialized adaptations. Many properties of learning, like the finding and constructing associations between different objects, symbols, sounds, and meanings, etc, are widely shared among animals. The molecular mechanisms of learning are also evidently similar among different organisms. In addition to this, learning exhibits specialized adaptations and modifications of learning which differ between different species. Animals learn to exploit new habitats and new resources within their ecosystem and thus re-balance the selective pressures they are exposed to. Learning also has an evolutionary impact that extends beyond the animal itself and affects other animals and plants it interacts within its ecosystem.

2. Learning in Animals.  

The scope of animal learning includes the understanding of why animals, including human beings behave as they do. Most parts of the history of animal learning have come from laboratory experiments that are carried out in controlled settings like labs, coupled with careful field studies of natural behavior. Indeed, there are many approaches to understanding animal behavior, with equally many terms used to describe the endeavor and its adjacent merged fields: animal learning, animal cognition, comparative cognition, comparative ethology, and cognitive ethology, to name just a few. However, all of these approaches have a foundation built on the principles of learning. 

Animals may learn behaviors in a variety of ways. Some ways in which animals learn are relatively simple. Others are very complex. Types of learning include the following like Habituation, Sensitization, Classical conditioning, Operant conditioning, Observational learning, Play, and Insight learning. The major studies in Animal learning focus on proximate causes of behavior, its development, and its evolution from a variety of different perspectives. Some major perspectives are, using behavioral, genetic, pharmacological and neuroscience approaches to study the mechanisms that underlie learning in animals. 

3. Learning in Humans.

Human learning involves the foundations of the two previously discussed levels of learning and most of its methodologies. The fundamental difference between animal learning and human learning is in its complexity which is mostly the product of evolved neo-cortex and consequent ability to learn and use abstract and symbolic knowledge in multiple levels of complexity(eg building tools). Professor Marc Hauser presents his theory of “Humaniqueness,” which includes four factors that make human cognition special. 1) The ability to combine and recombine different types of information and knowledge in order to gain new understanding; 2)To apply the same “rule” or solution to one problem to a different and new situation; 3) To create and easily understand symbolic representations of computation and sensory input; 4) And to detach modes of thought from raw sensory and perceptual input.

Some examples of major Human Learning Paradigms are: Social Constructivist and situational theories includes Constructivism(Piaget ), Constructionism (Seymour Papert), Communities of practice (Lave and Wenger), Situated learning (lave), Social learning theory by Albert Bandura, Socialization theories in sociology, Connected learning(Mimi Ito), etc., Behaviorist theories includes Classical conditioning (Pavlov), Operant conditioning (skinner), etc, Cognitivist theories includes Cognitive load theory (Sweller), Elaboration theory (Reigeluth), Situated cognition (Brown, Cllins & Duguid), Desirable difficulty(Robert a. Bjork), Motivation theories includes Flow and mastery (Csikszentmihalyi), Intrinsically motivating instruction (Malone), Self-determination theory (Deci and Ryan), Child development theories includes Attachment theory (Bowlby), Montessori method (Montessori), Piaget’s theory of cognitive development(also constructivism).

Further, the neuroscience perspective of studying about learning in the brain involves; Molecular and cellular neuroscience, Neural circuits and systems approach, Cognitive and behavioral neuroscience, and computational neuroscience. The scientists often use powerful neuroimaging tools like Functional Magnetic Resonance Imaging (fMRI) for studying the brain 

Examples of major domains of human learning include Motor Learning, Academic Learning, Learning in Work, Learning in complex effectual environments like entrepreneurship, politics, and other domains of similar nature. 

4. Learning in Machines 

Machine Learning is the kind of learning in which machines learn on their own without being explicitly programmed. It is an application of Artificial Intelligence that provides the system with the ability to automatically learn and improve from experience. It makes use of artificial evolution with genetic algorithms and deep learning techniques like neural networks to mimic human brains. Neural networks are adaptive to dynamic input; so the network generates the best possible result without a need for total revamp or redesign. The design of such an artificial neural network is inspired by the biological neural network of the human brain, leading to a process of learning that’s far more capable than that of standard machine learning models.

While Artificial Intelligence (AI) is concerned with getting computers to perform tasks that currently are only feasible for humans, Machine Learning(part of AI) aims to build computers that can learn how to make decisions or carry out tasks without being explicitly told how to do so.  

5. Future of Learning: Technology, AI, Biology, Synthetic Biology, and Combinatorial technologies.

This domain involves the current and futuristic technologies and its many different variables and how it is affecting the learning and the science of learning. In my opinion, there are three levels of advancement which can be expected in this category of learning. 

The first level includes methods like blended learning and a combination of technology tools like Learning Apps, Games, Augmented and Virtual reality, etc. This demonstrates the success of immersive technology designs in helping the acceleration of learning and mastery. A similar example will be the computational thinking perspective of math learning proposed by people like Conrad Wolfram which challenges the unresponsive status quo and change blindness in our math curriculum. According to Peter Diamandis of Singularity University following are the top five technologies that will reshape the near future of education 1) Virtual Reality, which can make learning truly immersive, 2) 3D Printing, which is allowing students to bring their ideas to life real-time, 3) Innovation and expansion of Sensors & Networks, which is going to connect everyone at gigabit speeds, making access to rich informational resources available at all times, 4) Machine Learning, which is making learning more adaptive and personalized, 5) Finally, Artificial-Intelligence based personalized teaching companion.”  

Second level futuristic technology include technologies like that of Elon Musk’s “Neural Lace” which is conceptualized as an interface that will link the human brain with artificial intelligence. The device will be an AI interface woven into the human brain. The device would enable users to access Google and other tools by just thinking about it or back-up their personal information from the mind in case he or she dies physically.

The third level involves futuristic technology combinatorialism. Predictive synthetic biology is coming under this area of significant possibilities. It lies in the integration of machine learning, synthetic biology, and automation, enabling disruptive changes in both computer science and biology.  This combinatorial nature of multiple technologies coming together may give rise to The Biointelligence Explosion as the British philosopher David Pearce conceptualized. He also wrote an essay “The Bio-intelligence Explosion” which explores how recursively self-improving organic robots will modify their own genetic source code and bootstrap our way to full-spectrum super-intelligence. 

                The Birds-Eye View, Evolution and Future.

                                  Humankind is facing unprecedented revolutions. This includes the emergence of intelligent machines, engineered organisms, Ai that can understand us better than ourselves, climate change and extreme weather conditions, skill displacement, job displacement, fake news (including deep fake tech), the reemergence of global right-wing, etc. 

This is why from a bird’s-eye view, an evolutionary perspective is the most powerful tool for understanding the real purpose of learning and hence the real “Science of learning”. We are organisms of evolution temporarily captivated by an artificial self-perpetuating social institution of “Education”,  which has outgrown its real purpose to become an externality for human learning and growth. The only way to see the reality is to get out of the box and see the “Box As A Whole”. Our past experiences are becoming less reliable guides for the future. Humankind as a whole is increasingly dealing with things nobody has ever encountered before. In other words,  life has become more Complex, Emergent and Exponential. 

What all of this reveals is that no human being can afford stability, we need the ability to constantly learn and to reinvent ourselves. Change is the only constant and learning to learn fast is the most important skill one can master. This is why I believe the  “Science Of Learning” is the most interesting domain of research in the future.