Journal article in Science and Children, Volume 30, Issue 7.
Access [free]: MacDonald (1993) via jstor.org
Scope of Paper
The author examines the scenarios by which a teacher might solicit predictions from their students. The paper assesses how effectively student predictions augment learning, analysed in the context of the prior learning of the students.
- Asking students to make predictions, when the prediction will be a total guess, does not benefit learning.
- Asking students to make predictions that are essentially logical deductions does not provide enough cognitive dissonance to augment learning.
- The sweet spot of using predictions to augment learning arises when students can apply previously learned concepts to new scenarios, or, even better, apply multiple previously learned concepts to make a prediction about a new scenario.
Teachers often ask students to make predictions. These requests are always context dependent, and when the teacher asks the question, they already know roughly how well a student should be able to answer.
In most cases, teachers will expect a response that lies somewhere on a continuum ranging from ‘guess’ at one end, and ‘logical deduction’ at the other end.
We can consider this continuum through a series of scenarios in which a teacher uses a demonstration to illustrate the properties of materials, in particular, that they expand when heated, and contract when cooled. The teacher places three candles underneath a thin copper strip and asks students to predict what will happen once the candles are lit.
Scenario 1: Guesswork
In scenario one, the students have no prior knowledge of what happens to materials when heated. They might predict that the copper will melt when the candles are lit: this prediction is a guess, a guess that serves no purpose to their learning. It is a) wrong, and b) related to a different area of the curriculum (changes of state).
Scenario 2: Logical deductions
In scenario two, the students have already conducted an investigation where they used warm water to remove a stuck glass beaker from another beaker, and discussed the outcome with the class. These students might predict that the copper strip will expand when heated: a logical deduction, that although correct, does little to augment learning. In this instance, the prediction is a logical deduction, a direct application of a previously learned fact.
The Goldilocks Zone: Predictions based on prior learning
The author argues that the use of student predictions to augment learning is most powerful when the predictions are at their most authentic.
An authentic prediction arises when students have enough knowledge through prior learning to make accurate judgments about a given scenario. The resolution of a problem containing a genuine uncertainty is more powerful than the logical application of prior knowledge.
In this context, when faced with a genuine uncertainty, students need to form new connections both within and between the schema of their long-term memory.
By building these links, student predictions can make new, meaningful connections between the existing pieces of knowledge required to make the prediction. This strengthens both the retention and the retrievability of the learned knowledge, and allows students to remember both the required knowledge and the relevant context.
The author concludes by stating that to be most effective, asking to students to make predictions must fall into the sweet spot between guesswork and pure logic. The inclusion of a genuine uncertainty against the background of prior learning helps students build schema in their memory, and promotes interest in the subject matter at the same time.
At the same time, of course, an authentic notion of prediction in science does involve some degree of certainty, because students predict from an informed rather than uninformed basis. Using predictions to test explanations allows teachers to be both pedagogically effective and to convey an authentic notion of science in their teaching … [Both student and teacher will be] emphatically reminded that the power of science comes from precisely the fact that our scientific explanations for events we encounter in our explorations can be tested in the real world through experiment and prediction.
— Dougal MacDonald (1993)