Response: A Nobel Laureate Writes About Becoming A “Science Coach”

You can become more effective as a science teacher by taking a lesson from athletic coaches. Just as a good coach would never simply send an athlete out to “practice and master tennis,” no teacher should send students out to “learn to do science”. The task is too overwhelming, and the learner will not recognize or practice many of the necessary skills. Instead, a good athletic coach first figures out the essential skills that make up mastery of their sport by looking at what experts do. Second, they create challenging but doable practice activities for the athlete/learner that explicitly practice these necessary skills. Third, the coach motivates their charges to work very hard at this practice, and fourth, they offer frequent and targeted constructive feedback to guide improvement.

All of the same ideas apply to teaching science. This is not a coincidence; research on the development of expertise has shown that this basic process (“deliberate practice”) of intense explicit practice of the essential skills with constructive feedback applies to the acquisition of expertise in all fields. Research has shown that the level of expertise an individual attains correlates strongly with the amount of “deliberate practice” that they have performed, much more strongly than with any measures of their “talent.”

To design suitable practice tasks/problems that will develop your students’ scientific expertise, you need to look at the thinking processes of scientists. Here are examples of some of the specific components of expertise that apply across the sciences (as well as engineering and mathematics).

The use of:

• discipline- and topic-specific mental models involving relevant cause and effect relationships that are used to make predictions about behavior and solve problems;

• criteria for deciding which of these models do or don’t apply in a given situation and processes for regularly testing the appropriateness of the model being used;

• systems for distinguishing relevant & irrelevant information;

• specialized representations that provide novel insights;

• criteria for selecting the likely optimum solution method to a given problem;

• self-checking and sense making, including use of discipline-specific criteria for checking the suitability of a solution method and a result.

For example, you might give your students a problem where, like in the real world, there is all sorts of different information, and as part of the solution the students must say which information is relevant and why. Another part of the problem solution might require them to give criteria by which to judge if their final numerical result is reasonable. In use, all of these components are embedded in the context and knowledge of the relevant area of science, and that needs to be the case for the practice problems/tasks that you design. Michelle Smith gives an example for teaching genetics. Many of these components involve making decisions in the presence of limited information--a vital but often educationally neglected aspect of expertise.

Feedback is an essential part of good coaching and good teaching. Useful feedback tells the learner (or even better, guides them to discover) what aspect of their thinking was wrong and why, and how they can improve while that thinking is still fresh in their mind. In many situations, properly designed collaborative learning tasks can result in individualized feedback provided by their peers. This reduces the burden on you. Collaborative learning tasks can also help make the practice tasks doable and challenging for learners with a broader range of skills.

The brain development that makes up complex learning requires strenuous mental effort, very analogous to the strenuous use of a muscle that results in its growth. So learning is inherently hard work. A critical aspect of good coaching and good teaching is to motivate the learner to do that work. How to do that will depend on both the subject matter and the characteristics of the students--their prior experiences, levels of mastery, and their values. Several things are known to increase learner motivation: having a sense of self-efficacy and some ownership of the learning process; finding the subject interesting, relevant, and inspiring; and developing a sense of identity as a beginning scientist. Motivating all of your students to work hard at learning can be one of the most challenging tasks for a teacher, but it is also probably the most important.

A critical feature of “deliberate practice” is the full mental engagement by the learner. Passive listening to the teacher or even watching engaging demonstrations that do not demand intellectual effort should be avoided. There is a place for short lectures, but only after your students have been prepared to learn from them.

Becoming a “science coach” can make you a more effective teacher.