Learning Theories and Electrical Engineering

LTEC612, Introduction to E-learning, introduced us to the learning theories. What are the learning theories and is there a connection between them and Electrical Engineering?

Early on it was thought that learning is either there or it is not, what I call digital thinking. Digital thinking is thinking in ones and zeros, just like digital logic in Electrical Engineering. In Electrical Engineering, presence of electrons is considered negative charge and its absence is considered positive charge. Digital logic defines absence of electrons as positive voltage or a logic 1 while the presence of electrons is defined as zero voltage or logic 0. It is either there or not there. It did not matter what the magnitude of the voltage is or how many electrons accumulated. Christopher Chase calls it linear thinking and made a distinction with systems thinking in a presentation (Chase, 1997).

The earliest learning theory, Behaviorism, resonates this thinking. This school of thought requires that learning manifest as overt changes in behavior that can be observed and measured. The latter is proof that learning has occurred. Its absence, then, by definition, is proof that learning has not occurred. Prediction and control of behavior were its goal (Watson, 1913). The behaviorist school of learning sees the mind as a black box (Ally, 2008).  The transistor was invented in 1947.

The cognitivist recognizes the human who is the learner and models the learner with a set of memory called short term memory and long term memory. Sensations are stored in short term memory first and if processed efficiently they get transferred to long term memory. This was based on the model of the computer (Smith, 2001). The first computer memory was developed in 1942 and Cognitivism gained popularity in the 1950s. It is now known that memory is not located in one specific area in the brain. 

Enter constructivist theory of learning. The constructivist theory of learning does not owe its roots to the development of the computer. It states that learning is contextual and that the learner actively constructs knowledge by interpreting and processing what is received. John Dewey, one of the early authors of the constructivist theory of learning, wrote “If you have doubts about how learning happens, engage in sustained inquiry: study, ponder, consider alternative possibilities and arrive at your belief grounded in evidence”.  If the constructivist theory of learning has roots in sustained inquiry and grounded in evidence, would it not be a sound platform on which to base a hardware architecture for learning? If we can arrive at theories of learning based on the architecture of the computer, would the reverse not be possible? 

The connectivist theory of learning is about the information explosion and the resulting change of that information that requires the learner to continually update his or her learning (Siemens, 2004). It is about the internet and the networked world. 

I would venture to say that the behaviorist theory of learning is an early attempt at defining the what of learning, following digital logic. The congitivist theory defines the who of learning, it is a recognition that learning happens in humans as differentiated from the computer or a machine. The constructivist theory is about the how of learning, how learning occurs and how it manifests itself in the human being. Finally the connectionist theory is about where this learning is located and waiting to be discovered by another learner.

Among these, it is only the constructivist theory that uncategorically states that the occurrence of learning is by the learner, for the learner and in the learner. The constructivist theory of learning is also the only one that does not owe its roots or its presence to the computer or hardware architecture. On the other hand, it would appear that hardware architecture could derive its specifications from it. 

Jean Piaget, another pioneer of the constructivist theory of learning, like some of his predecessors in college at that time, studied Biology, Psychology, Philosophy and combined them into what he called Genetic Epistemology. He used experimentation to derive results. He articulated mechanisms by which individuals interact with new information, called assimilation and accommodation (Piaget, 1989).  Sure enough, there is research work in machine learning that utilizes the concept of assimilation and accommodation (Kuo, 2010).  

The brain processes with 100000 times less energy than the computers, “the net is doing the work in the brain” (Boahen, 2007). In the same video, Boahen goes on to explain how they duplicated the retina on a chip and talks about putting “Africa” in the computer. Perhaps by Africa, he is referring to systems thinking (Chase, 1997). I wonder how the concepts of assimilation and accomodation would work in the functioning of the retina or how the retina impacts assimilation and accomodation. After all, the perception of color is known to be highly subjective. This would mean that different instances of the same hardware produces results that vary considerably.

1. Ally, M, 2008. Foundations of Educational Theory for Online Learning in Theory and Practice of Online Learning (Chapter 1). Retrieved from http://www.aupress.ca/books/120146/ebook/02_Anderson_2008-Theory_and_Practice_of_Online_Learning.pdf 

2. Watson, J. (1913). Psychology as the behaviourist views it. Psychological Review, 20(2), 158-177. 

3. Smith, E. (2001). Cognitive psychology: History. International Encyclopedia of Social and Behavioral Sciences, 2140-2147.

4. Boahen, K. (2007). A computer that works like the brain. TEDGlobal 2007. https://www.ted.com/talks/kwabena_boahen_on_a_computer_that_works_like_the_brain

5. Chase, C. 1997. Two distinct modes of human reasoning in New Paradigms in Education. Fukuoka JALT presentation. 

6. Siemens, G. 2004. Connectivism: A Learning Theory for the Digital Age. Retrieved from http://www.elearnspace.org/Articles/connectivism.htm, 5-10-2018.

7. Piaget’s developmental theory: An overview [Video file]. (1989). Retrieved May 11, 2018, from https://fod.infobase.com/PortalPlaylists.aspx?wID=102793&xtid=44904 

8. Kuo, J., & Cheng, H. (2010). Applying assimilation and accommodation for cooperative learning of RoboCup agent. Machine Learning and Cybernetics (ICMLC), 2010 International Conference on, 6, 3234-3239.