In partnership with the Society of Biology, Royal Society of Chemistry, and Institute of Physics

An 'authentic' approach

Scientific inquiry is one of the processes used to develop scientific knowledge. However, it does not necessarily represent an effective pedagogic approach for learning scientific theories in school science.

School science investigations are often reduced to a series of easy to follow steps (Donnelly et al., 1996). This ‘painting by numbers’ approach can lead to students mechanistically applying a set of common, rote-learned questions, in the same sequence, to all investigation contexts. It is also often assumed that theories will emerge from the evidence; that by collecting data and analysing it, students will be able to draw conclusions that explain the data. This is known as ‘induction’. Research has shown that viewing inquiry as an inductive process is a flawed idea. We need theories to make the link between data and explanations, and students need access to these theories if they are to be expected to develop explanations (Driver et al., 2000).

A model for scientific reasoning

The diagram below (Fig. 1) presents a more authentic model for scientific reasoning. Through observation and measurement, scientists collect data on the real world. Scientists also generate models to explain the behaviour of the real world, which they can use to make predictions. They then compare their predictions with the data. If there is agreement between the prediction and the data this increases the scientists’ confidence in the model which provides an explanation for this particular phenomenon. If there is disagreement between the prediction and the data, scientists might question the model, the reasoning that led to the prediction, or the quality of the data. If the model is brought into question it will be revised and the process begins again.

Key ideas from this framework for scientific reasoning

  • Explanations of scientific phenomena are developed from theories or models based on the theories. This is a creative process. There is no direct route from data to explanation.
  • Predictions are tested against evidence derived from observation and experiment.
  • Knowledge of a theory or model is used to predict the outcomes of experiments. Theory comes before, and informs observations and experimental planning.
  • Scientists engage in questioning and discussion about how the data they have collected can be explained in terms of their theory-based models.
  • Scientists rarely work in isolation. Research is more of a social activity where small groups discuss, question, postulate, explain, disagree or propose alternative explanations and interpretations of data, based on what is already known about the problem. This style of collaborative and co-operative learning lies at the heart of model-based inquiry.

Page last updated on 02 May 2013