By Helen Abadzi, College of Education, University of Texas at Arlington
In 2014, a television show in the United Kingdom, “The Apprentice”, selected candidates for their business acumen and quick wits. Two young women and a man teamed up to develop a product that would be marketed and sold. They had to calculate the amount of incense fluid to buy for 60 reeds that weighed 100 grams each. To the amazement of the audience, the two women declared that they could not do the calculations. The failure to perform on the spot a set of relatively simple math operations was found unacceptable, and they were shamefully removed from the show.
This was a poignant case of a skills gap demonstrated before thousands of viewers. The workers could not quickly meet the demands of the job. And almost every day, some publications or conferences refer to this gap. For example, in the 2016 World Strategic Forum, university presidents and senior bank managers shared their views on the importance of high-level skills for technological achievement and innovation. Speakers envisioned education systems as engines of innovation and entrepreneurship, driving job generation, reducing inequality, and increasing competitiveness. A frequent argument is that traditional schooling does not work. Education thinking must change in order to prepare the youth for complex 21st century skills.
Recommendations are often long on visions but short on specifics. And learning research on the skills gap is rarely cited. But the tendency to aspire high and skip the boring basics seems to permeate schools worldwide. Teachers are trained to avoid activities that are considered traditional (chalk and talk, drill and practice) and teach creatively: engaging students in innovative ideas, collaborative learning projects, and personalized learning on the topics that suit student interests.
Are such activities likely to bridge the math skills gap of performing math calculations when needed? Research on memory and learning suggests otherwise. And the cards are not stacked in favour of innovation.
First some basics. “Skill” means ability to link and execute sequences of items relatively effortlessly. The items may be concepts and procedures. To create skills, we must assemble these chains piece by piece, like connecting cars for a toy railroad. The right car must be found and put into the place it fits best for optimal performance. We must practice the many times, get feedback and correct errors, so that the sequence is performed effortlessly and rapidly. The process also moves the skill from our conscious, explicit memory into the unconscious, implicit memory.
There is a pattern in this chaos. Practice results conform to certain mathematical trends. Progress is initially rapid and levels off with practice, forming an L shape. For example, Cuban workers initially improved the time needed to roll a cigar rapidly and continued to improve slowly, even after rolling 10 million cigars for seven years. It is possible to estimate timeframes and training costs through these almost universal learning curves.
Consider a beginners’ music class in a school. The teacher writes notes on the blackboard: Sol la si do si la sol fa mi… Then the teacher points to the notes one by one. Each child has a flute, finds the correct hole, then pauses looking for the next note. The execution is slow and riddled with errors, but the teacher corrects, and the children try again. By the end of the lesson, nearly all children play a tune laboriously but in the right sequence. Tomorrow they will play the second part of the song, again finding the notes one by one. Then they will put them together, and link the piece to the tune they learned the previous day. After a year of consistent practice, they will read notes and find holes in milliseconds and unconsciously. And when asked to play what they know, they will instantly, fluently perform, without thinking.
We need the automaticity because we have very little processing capacity at a given moment. Your current thoughts reside in “working memory” (short-term memory is one of its components). That mental space is very limited; it holds 4-7 information items for perhaps 12-25 seconds. Then they disappear! We have no time to think consciously about everything that we do. We must make a few key decisions in milliseconds and execute other simultaneous tasks unconsciously. Automaticity allows us to offload tasks from our working memory and thus have time to think about more complex issues. If we take too long to execute, we may forget what they were doing, run out of patience, or miss the chance to perform them.
This peculiarity of working memory creates a premium for instant acts and thoughts. To read, think, walk, type, or talk at the same time, actions must be performed in milliseconds. It is usually insufficient to know how to do something but recall it an hour or a week later. Skills are marketable mainly if they are executed effortlessly whenever they are needed.
The stepwise process of linking basic units is one reason why it is inefficient to teach complex skills to students who have not automatized the prerequisite components. Creativity, innovation, complex thinking about a topic are only possible after people know a great deal about it, practiced assembling the information, and bring it into their working memory in milliseconds.
However, practice activities may seem needlessly repetitive and pointless. It is tempting to reduce them in favour of more creative and fun activities. High-scoring and well-to-do students may easily automatize what they do not already know. But lower-performing students who skip the boring basics may be short changed in exactly the skills they need for productive work. Finding fun ways to facilitate practice is important, but mere fun activities may not create proficiency in procedures such as mental math. Furthermore, research shows that interest in various topics results from knowledge and practice. If young people get too little basic math practice, they may say that they are not interested in math or that it takes a lot of intelligence to perform.
Skills caught in the web of innovation
Educational institutions must prepare students for complex skills and must do so as efficiently as possible. But educational practices are often at odds with learning research. Colleges of education rarely teach cognitive science, so automaticity are neglected. In fact these concepts may be regarded negatively. By contrast, creative classroom activities may engage students and reduce discipline problems. But they do not do lower-scoring students a favour. “Traditional,” “old-fashioned” reading or math drill exists because over the centuries teachers realized the utility of practice. Learning is biologically determined, and its rules do not change on demand. The skills gap originates in deficits of basic performance chains.
Unfortunately the various eminent persons who lecture on skills often give the wrong advice. Their beliefs reflect the perspective of people who have highly complex skills. Their focus inevitably is what their own very accomplished children can do. Similarly, international agencies give general, idealized policy advice to governments. The specifics to teach basic skills efficiently to the entire population are often considered details, left up to teachers.
To teach any skills to for any subject, the process should be like that of the flute instruction. In reading, students must be taught reading through individual letters, and these must be practiced in combinations for many hours. In math, chains of numbers must be similarly manipulated. Instructional time should therefore be used to maximize practice in order to speed up performance.
There is indeed a need for a paradigm shift in educational thinking and skill preparation. But students need early engagement in drill and practice. Freeing up working memory pays big dividends in innovation and critical thinking later on. To explain these counterintuitive concepts, it is important to practice international education according to memory research. It is also important to finance more of it, particularly research on the mastery of low-level, component skills.
The challenges are large, but they must be confronted if the Sustainable Development Goals are to be met in 2030. Means must be found to train staff of donor agencies and governments on the ways that people process information. Only then can the skills gap close.
Helen Abadzi is a Greek psychologist who speaks many languages. After spending 27 years as an education specialist at the World Bank, she is a researcher at the University of Texas at Arlington. To improve the outcomes of education investments she regularly monitors research in cognitive psychology and neuroscience. Her publications can be found at: uta.academia.edu/HelenAbadzi.
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