Achieving Automaticity: Mastering Baseline Knowledge to Enable Higher Learning
Every academic subject or job function requires specific knowledge to be mastered. These areas of competency vary greatly in scope and difficulty: the elementary school student who must memorize spelling words; the airplane pilot who must know instinctively how to maneuver in seconds to avoid a mid-air crash. Although there may seem to be little comparison, they have an important commonality: with a high level of competency, certain knowledge becomes “second nature,” allowing actions to become “automatic.” This is automaticity.
A simple example of automaticity is the skilled typist who no longer needs to think about the keys on the keyboard. Fingers move automatically, and words appear on the screen. Similarly, fluency in a foreign language gives a non-native speaker automaticity in vocabulary and grammar; now the focus can be on the message being delivered. An advanced example of automaticity is the well-trained and highly experienced surgeon who can react in an instant to life-or-death patient scenarios. In everyday life, in school and the workplace, people cannot function without at least some degree of automaticity—what Daniel Kahneman, author of Thinking, Fast and Slow, calls “System 1 thinking.”
Across the learning spectrum – K-12, post-secondary education, and corporate learning – automaticity provides foundational knowledge. Although automaticity does not need to be achieved in all subject matter, a certain baseline must be established to the point that it is second nature.
Automaticity is central to the work we do with the Center for Curriculum Redesign. The objective is providing people with a level of proficiency that gives them internal access to knowledge far more efficiently than looking up information. This is vital in any number of areas, from the performance of fundamental job skills to making critical decisions involving safety.
While automaticity is important, it is not an end goal in itself; rather, it is the means to an even greater end. Automaticity in baseline knowledge allows learners to move beyond day-to-day assignments and expand into more sophisticated material. They are able to devote more time and attention to other areas of learning, unlocking a wide realm of possibilities at every level.
Making Time for the Four Dimensions
Charles Fadel, the founder of the Center for Curriculum Redesign, has described education and learning in terms of four dimensions: knowledge, skills, character and meta-learning (i.e., learning how to learn). Primary and secondary schools, Fadel explains, focus on the development of a foundation for future learning, both knowledge and competencies. Foundational knowledge is a baseline of what one must know in order to learn more and apply what was learned in a real-world setting. Essential content is what students must know, rather than look up, in order to learn additional concepts and make informed decisions.
The challenge of traditional education is how to impart so much baseline knowledge to students so that they can keep learning and apply new knowledge as they interact with others. The same dilemma faces corporate learning and development professionals who need to determine how best to educate workers in specific areas. The illusive goal has been a streamlined process for learning in order to achieve automaticity.
The process of achieving automaticity is anything but automatic. It takes time to achieve such knowledge; in fact, knowledge acquisition remains the most intense and time-consuming component of learning. One answer in traditional education has been to reduce the scope of the curriculum; for example, instead of requiring ten concepts to be mastered, students only need to focus on five or six. But which six? And is reducing learning really the best way to achieve competency? With this thinking, for example, elementary school students would not be required to memorize spelling words with the assumption that they could then spend more time on learning grammar and composition. But expecting them to look up each unfamiliar word is counterproductive because of the time involved and the possibility of introducing errors.
Automaticity in baseline knowledge is required in order to move on to higher-order concepts. Maya Bialik and Charles Fadel of the Center for Curriculum Redesign explain that even complex concepts are made up of smaller pieces of information; mastering the baseline to achieve automaticity allows more complex understanding to be achieved. They note: “While anyone can look up anything at any time, having to look up everything would slow down future learning and problem solving.” Furthermore, when knowledge must be processed in real-time—for example, in the midst of a group project—continually looking up information is not practical. Without the speed and accuracy that comes with automaticity, the result is a “bottleneck to learning higher-level concepts,” they state.
The solution for breaking the bottleneck is adaptive learning, a computer-based learning approach. With adaptive learning, students can quickly and efficiently acquire baseline knowledge, becoming both competent and confident in what they know. Over the past two decades, our approach has been to develop and refine a biological approach to adaptive learning, combining the latest in brain science with cutting-edge computer technology. Instead of assuming where learners will struggle or where they will need reinforcement, which is the approach of the more traditional “inference models,” biological platforms mirror how learning actually occurs.
By probing what the individual learner already knows, the system can focus on knowledge gaps, as well as those areas in which the learners assume incorrectly that they possess the required knowledge, but in fact do not. (We call this “unconscious incompetence.”) By focusing on knowledge gaps, instead of needlessly spending time on what learners already know, adaptive learning becomes more engaging for the learner and more efficient. As we have observed, adaptive platforms in corporate learning can result in 50% less time to achieve mastery compared to traditional e-learning. This is important from a productivity standpoint; faster, more effective training means people can return to their jobs sooner.
Adaptive learning also addresses one downside of automaticity, which can potentially lock people into certain pre-programmed responses that may no longer be appropriate if the environment changes. Adaptive learning helps people become more self-aware and “unlearn” outdated concepts as they update their automaticity.
Currently, we are at work on next-generation adaptive models that will help learners achieve automaticity with greater speed and effectiveness. With automaticity in baseline knowledge, there is more time and capacity for the other three dimensions of learning, as defined by Fadel: skills, character, and meta-learning. Skills include 21st century workforce requirements of communication, creativity, collaboration, and critical thinking, which are essential for employees to stay relevant in the AI-enabled economy. Character addresses ethics and leadership skills, which are vitally important to promote a diverse and inclusive work environment. Meta-learning is composed of two components: metacognition, or “thinking about thinking,” and having a growth mindset, which in a corporate context encourages career advancement.
Automaticity and Team Performance
While automaticity improves the individual’s performance, it also enhances teams. When each person is proficient, the entire team performs better, and people can learn from each other. A simple example is an orchestra. Each performer masters his or her instrument and learns the music to be performed. After these individuals achieve automaticity, they come together to seamlessly blend their skills and talent. The result is a whole that is truly greater than the sum of individual parts. This example applies in so many areas, from a hospital where specialists confer on a complex case or a crisis in the operating room to a corporate call center. Individuals who possess in-depth knowledge that has become “second nature” interact confidently and competently with their colleagues. Group decision-making is enhanced, and a learning culture is established to encourages the growth mindset that underpins the four dimensions of learning.
For the individual, automaticity serves as a gateway to bigger and better things, enabling them to take on more complex subjects and concepts. In the 21st century workplace, the key to taking on these challenges is to help learners achieve automaticity in baseline knowledge by using the best of adaptive learning. Their need to succeed will be satisfied with greater engagement in the learning process, and their reward will be more opportunities to learn and grow.