There has been much research into the science of learning. In fact, the Learning Sciences is a field dedicated to the study of learning. It is ironic that few schools and teachers design education and curricula with the principles from the science of learning. Sadly, even if teachers themselves know how to engineer quality and deep learning, they have to follow curricular constraints beyond their control that undermine the science of learning.
Learning is a memory puzzle
In many ways, learning is essentially a memory problem: how to move from short-term memory to long-term memory. In order to stack the probability of the short- to long-term memory transfer, attention, interest and working memory need to be triggered. After this, encoding and consolidation needs to happen so that the knowledge/skill can be retrieved from long-term memory.
As the figure represents, learning is like a mysterious black box. Why are we able to learn some things and not others? The answer is not black and white, but cognitive research into learning offers methods and strategies to stack probability in favor of learning.
What we know about learning
From a general perspective, there are 3 general rules of learning:
- Learning needs memory
- Learning needs repetition and reinforcement
- Learning is an acquired skill.
Helping learners acquire the skill should be the job of teachers and trainers, which is to make the task of learning more efficient and effective. The three key ingredients for effective learning are input (instruction or demonstration of skills), output (skills practice), and we must not forget motivation, because there is no learning without motivation.
Managing learning intensifiers
The first step to managing and optimizing learning is to engineer attention at input. The skill of learning (and the teaching of learning) depends on knowing intensifiers of learning, which include making the learning relevant, personalized and concrete. If it is relevant and personalized, the learner can draw upon prior knowledge and experiences to form a frame of reference and meaningful (i.e., neural) connections. Using concrete examples or metaphors can make abstract concepts easier to grasp. These strategies can make the learner be more attentive during the input part of the learning cycle.
Encoding → consolidation
As an extension of engineering the input to make it more graspable, learners should actively elaborate on the new knowledge to put it into their own words and link it to old knowledge. This is called encoding. Learning is effortful and requires reflection and repeated proactive practice. This is called consolidation. Encoding goes hand-in-hand with reflection, which means the learner retrieves the new knowledge and connects it to new experiences. Further visualization and mental rehearsals help consolidate the knowledge for the next effective practice. A crucial component of this consolidation process is repeated practice and testing – by oneself or a teacher. Mistakes are sources of learning and awareness of a lack of mastery. Practice and testing should not be too easy, and not a matter of passively reading and rereading. Without challenge and effort learning will not happen, so scheduled and repeated testing and spaced recall increase the odds of the knowledge becoming hardwired.
Role of instructor
The instructor or trainer is pivotal here to optimize the learning experience and heighten the probability of deeper learning and entry into long-term memory. That is, the instructor coaches and challenges the learner with
- the appropriate difficulty of input and practice
- spaced practice and testing, and
- corrective feedback.
The coach therefore guides, and even better, also apprentices the learner via demonstrations of expertise so that the learner can most effectively and efficiently learn from mistakes and spot cues to activate the knowledge or skill being learned. As for feedback, delayed feedback is useful to force the learner to review and recall the knowledge at a later time as a type of spaced practice. Peer interaction can also be usefully leveraged to force learners to articulate in their own words the new learning.
The shallow learning standard
Unfortunately, many schools and curricula are designed to inhibit effective learning. Curriculum guidelines more often than not strive for breadth, not depth. The large amount of knowledge and skills “to cover” results in a best-case scenario of knowledge recognition and familiarity – in other words, shallow learning. Knowledge does not enter the long-term memory because high stakes tests encourage cramming and massed learning that results in quick forgetting. Inappropriate level of difficulty for practice (everybody gets the same input at the same time regardless of ability) will either make the learner feel hopeless if too difficult, and bored if insufficiently challenging, which may dangerously create the illusion of mastery. Memories can also be distorted without repeated practice. Most instructors resign themselves to this overbearing and unproductive routine.
Summary
Effective strategies for deep learning, therefore involve and engineered input that makes it relevant to the learner; the learner then needs to REFLECT on the knowledge and skills and ELABORATE them within an existing framework of knowledge. In practical terms, the learner should convert the learning into his own words and use own examples and concrete metaphors to clarify the concepts. This is the ENCODING step. To CONSOLIDATE the learning requires REPEATED effort and SPACED PRACTICE and TESTING with attention paid to mistakes. This repeated TESTING and RETRIEVAL practice should be accompanied with feedback, delayed if possible to stretch out the learning duration. When these strategies are combined together, the probability of the learning going from short-term memory to long-term memory becomes much greater.
