“Learning from failure is a very intuitive and compelling idea that’s been around for ages, but teachers may not know how to use it.” Manu Kapur
Professor Manu Kapur is currently a Professor of Psychological Studies at The Hong Kong Institute of Education and the former head of the Learning Sciences Lab at the National Institute of Education of Singapore. He has pioneered the idea of productive failure, which is “a learning design that entails the design of conditions for learners to persist in generating and exploring representations and solution methods for solving complex, novel problems.”
Dr. Nido Qubein, the President of High Point University, explains that we learn by doing and when we learn something new, such as walking or later driving a car, we become more proficient as we learn from our mistakes. “In a productive failure, you don’t achieve your objective, but you come away with new knowledge and understanding that will increase your chances of success on the next try.”
Studies conducted by Professor Kapur have shown that, when learners try to figure something out on their own before getting help from the instructor, they generate a lot of ideas about the nature of the problem and what potential solutions would look like. Because of this, when they encounter a new but similar problem, they are better able to transfer the knowledge they have gathered more effectively than those who were the passive recipients of someone’s else’s expertise.
Kapur believes that this struggle activates parts of the brain that trigger deeper learning. Learners have to figure out three critical things: (1) what they know, (2) the limits of what they know, and (3) exactly what they do not know.
It’s important to note at this juncture that Kapur uses productive failure to teach mathematics.
In her article, “How ‘Productive Failure’ in Math Class Helps Make Lessons Stick,” Katrina Schwartz identifies the following five principles of productive failure lesson design:
- Tasks must be challenging enough to engage learners, but not so challenging they give up.
- Tasks must have multiple ideas, solutions or ways to solve so that [learners] generate a multitude of ideas. It cannot be a closed task with only one path to finding a correct answer.
- The task must activate prior knowledge, and not just formal learning from a previous lesson. “If you design a task where a student only displays their prior class learning it’s not good because then you aren’t tapping into their intuitive reasoning,” Kapur said. Intuitive reasoning is a big part of how students transfer knowledge to new situations.
- While the task should activate knowledge, it should be designed so that the knowledge students have is not sufficient to solve the problem. They should hit a roadblock that they can’t get around. “It makes the [learner] aware of what he or she knows, and the limits of what he or she knows, and that creates a motivation to figure out what it is they need to know to solve this problem,” Kapur said.
- It helps if that task as an “affective draw,” in that it’s related to something [learners] care about or concerns something with which they identify.
What I find particularly interesting is how Kapur’s research findings seem to contradict Bloom’s Taxonomy of Learning Objectives. In Bloom’s Taxonomy for the Cognitive Domain, learning begins with the acquisition of knowledge which then progresses through comprehension, application, analysis, evaluation, and finally gets to creation. Creation is the highest level of cognitive functioning.
According to Kapur, “We’ve found that creativity actually suffers if you teach [learners] something too early.” When learners who have been taught with direct instruction are later asked to generate as many ways of solving the problem as they can, many can’t go beyond the method they have already been taught.
“They were locked into that way of thinking. When we start with generating or exploring we find that [learners] still learn the material later on, but the knowledge is more flexible.”
From this, Kapur has determined that creativity is itself a function of how learners acquire information.
This is somewhat similar to discovery learning, except that the aim of self-directed learning is for the learners to discover the correct answers for themselves. In productive failure, the learners are not able to discover the correct answers on their own. However, as a result of their “beneficial struggles,” they are primed to learn the answers.
What do you think? Can productive failure be used to teach non-mathematical subjects or can it only work with topics and problems that have finite solutions?
May your learning be sweet.