This post is aimed to learn lessons from the “Peak: Secrets from the New Science Of Expertise” by Anders Ericsson and Robert Pool that we can apply to improve an education tool that I am building for the last year.
Building mental representations
According to “Peak” the goal of deliberate practice is to build mental representations. The most skillful people in a domain are the ones with the best mental representations. For high-performing experts, these mental representations are not explicit and it often takes a lot of effort from researchers to make them explicit. As mental representations are dependent on other mental representations it takes a lot of effort to get the same mental representations as an expert.
The mental representations concept is quite similar to the knowledge components in the knowledge learning instruction (KLI) framework which most educational scientists use today. They are similar as they are both domain and context dependable, and internal. The educational and deliberate practice strategies will vary a lot between learning different mental representations. There is no one way fits all approach. Educational frameworks and Bloom’s taxonomy do a better job at explaining the nuances of mental representations and what tools and assessments to apply for what type of mental representation.
Knowledge versus skill
The book contrast knowledge and skill. There is a large difference between being able to learn knowledge and learning skills that can be applied. The authors argue that the current educational system focuses too much on knowledge and not on learning skills. More focus could be given to learning activities related to what experts would do with the course knowledge. For example, instead of learning to formulate a dry exponential formula give more exposure to how and when experts would apply that knowledge (e.g. modeling a pandemic).
To attempt deliberate practice there needs to be an objective result in which you can measure your progress. As you know where you stand and get objective feedback if you are improving. As an example, the book gives simple domains like chess and remembering lists of digits. A starting point for anybody wanting to apply deliberate practice is to find objective measurements of success.
For our project, we use learning goals as the binding objective and by attaching learning events and assessments to learning goals. The community that manages these learning goals can reach a consensus on what it means for the student to have learned that goal which the student and teachers can use as an objective measurement. For real educational use, this is quite an important and difficult step that does not get enough attention from our current educational institutions.
Evidence for deliberate practice
The book discusses an experiment with a two-week physics lecture contrasting the traditional lecturing approach with a deliberate practice approach focussed on building expert mental representations. The result was shockingly in favor of the deliberate practice approach with more than twice the rate of learning and higher levels of student motivation.
There seems to be a general scientific consensus that deliberate practice is at least some positive factor in learning even though it is not the only or main factor in learning. The original paper has been cited over 13000 times (that is a lot!). The main research in deliberate practice has gone to experts domains like chess, music, and sports and it has less focus on educational domains like math.
Points of critique
Clearly, not everybody can become an expert in everything. The book attempts to give the idea that any ceiling in skill level can be overcome with deliberate practice. Yet, for even highly motivated individuals applying deliberate practice ceiling will be reached. Some people are just more talented than others and require less practice or go through the practice at a faster rate. Individual differences that are not changeable seem to matter more than Peak would want us to believe.
However, if you do not believe you have to talent to learn something you will never try. Therefore, believing that deliberate practice can make you an expert can be motivating to some students. I agree that it is inefficient to segregate people that have talent and do not have talent in education. Every student should have the benefit of the doubt and get an honest try at learning the expert materials with support from teachers. On average, motivated students who practice will likely outperform talented students that do not practice.
What can we take from the book?
An important focus of deliberate practice is finding expert mentors to guide you forward. In the classroom, we have a teacher-student relationship and we have to design interactions between teachers and students on the platform. I find the deliberate practice approach to be a good initial template to think about this relationship.
Teachers can guide students by assigning different learning goals on the platform to students in course modules. However, we need to create systems where teachers can moderate the process the student makes and course correct. A monitoring solution we are going to make for this is moment-to-moment learning curves dashboards. We really should emphasize teachers as the main users of these dashboards and steer the teachers to mentor the students and make them quick and easy to use. A student that is struggling to learn something needs different attention than a student who is bored.