Why is modelling effective?

In my last blog, I highlighted the dangers of relying too heavily on the cognitive model to explain what makes for good teaching and learning. I argued that cognitive explanations overlook the normative aspects inherent in education.

In this blog, I aim to show some of the conceptual confusion that exists within Barak Rosenshine’s Principles of Instruction. Rosenshine’s occasional lapses into normative language — contrasted with Doug Lemov’s open embrace of such terminology — reveal the limitations of the cognitive model in explaining some important pedagogical practices.

Modelling in Rosenshine

In his Principles of Instruction, Rosenshine outlines the benefits of providing models and scaffolds. In his fourth principle, Provide models, Rosenshine states,

“Students need cognitive support to help them learn to solve problems. The teacher modelling and thinking aloud while demonstrating how to solve a problem are examples of effective cognitive support.

“Worked examples … are another form of modelling … Worked examples allow students to focus on the specific steps to solve problems and thus reduce the cognitive load on their working memory.”

This argument is scientific in tone and in structure. There is nothing that could be described as normative language, and the logic, as I understand it, runs roughly as follows:

• If the cognitive model is true (information is processed by working memory and subsequently stored in long term memory) …
• and the teacher breaks down the steps of a task during modelling, …
• then cognitive load on working memory is reduced …
• thereby increasing the chance of storing information about the procedure securely in long term memory.

This follows the structure of all scientific thinking: a model is presented, then deductions are made from it to generate testable hypotheses.

Compare this to the argument Rosenshine makes in his eighth principle, Provide scaffolds for difficult tasks:

“The process of helping students solve difficult problems by modelling and providing scaffolds has been called ‘cognitive apprenticeship’. Students learn strategies and content during this apprenticeship that enable them to become competent readers, writers, and problem solvers. They are aided by a master who models, coaches, provides supports, and scaffolds them as they become independent.

“Teachers might think aloud while solving a scientific equation or writing an essay, and at the same time provide labels for their mental processes. Such thinking aloud provides novice learners with a way to observe “expert thinking” that is usually hidden from the student.”

The structure of the argument is no longer scientific: no reference is made to the working memory model, and there is little in the way of deduction. Likewise, the tone has shifted; normative expressions like master, coach, novice and expert have crept in.

Indeed, the idea of ‘cognitive apprenticeship’ appears confused. An apprenticeship is a relationship between persons; it is surely a category error to conceive of it in terms of brains. It is thus unclear from Rosenshine’s work whether modelling is effective because of its effect on working memory or some other factor the cognitive model does not account for.

High success rate and the Joy Factor

A similar problem is evident in Rosenshine’s seventh principle, High Success Rate. Rosenshine himself wisely avoids any attempt to explain the research findings (that students in classrooms with higher success rates tend to get better test scores later on) in terms of the cognitive model. This is because his advice around maintaining a high success rate relates to normative factors around student motivation, factors that cannot be explained in causal, scientific terms.

(Others have made such attempts. Peps McCrea, in his book Motivated Teaching, gives an account of “the mechanics of motivation”, describing “machinery … nested deep in our brains and biology. It acts not just in our synapses, but in our genes and in our hormones.”)

In Teach Like A Champion, on the other hand, Doug Lemov offers an explicitly normative account of why it is important for students to achieve success. When asking students questions via the Cold Call technique, for example, Lemov asserts that, “you want students to get the answer right. In your heart, you have to be rooting for success.”

He also frames success more broadly in terms of the so-called Joy Factor:

“The finest teachers offer up their work with generous servings of energy, passion, enthusiasm, fun and humor — not necessarily as the antidote to hard work but because those are some of the primary ways hard work gets done. It turns out that finding joy in the work of learning — the Joy Factor — is a key driver of not just a happy classroom but a high-achieving classroom. People work harder when they enjoy working on something.”

Problems with cognitive explanations

Lemov’s normative account of success, with exhortations to the teacher’s heart and an open acknowledgement of the social basis of student motivation, seems to offer a far more plausible explanation of Rosenshine’s research findings than an account in terms of synapses and genes.

(This is not to say I agree with Lemov’s mantra of joy through work, which sounds like a slogan from the Stakhanovite movement in 1930s Russia. Rather, I agree with his approach of explaining motivation in terms of social influences rather than biological or cognitive causes.)

Likewise, Rosenshine’s normative phrasing around master and apprentice, expert and novice, seems to offer an equally if not more convincing account of why modelling is effective than his earlier explanation in terms of cognitive load.

These discrepancies and ambiguities offer clues to support the thesis I outlined in my previous blog, that the cognitive model of teaching and learning is insufficient for explaining what makes classroom practice effective.