002_Model - Based Learning

Model-based learning

Model-based learning is based on the generating, testing and revising of scientific models. It is different to typical school science investigations, in that it is centered round a collaborative and co-operative style of learning and places emphasis on the explanatory model.

Model-based learning is different to typical school science investigations, in that it is centered round a collaborative and co-operative style of learning and places emphasis on the explanatory model. In a model-based inquiry students are expected to:

• Use knowledge of a model to predict the outcomes of experiments, and explain their reasoning.
• Test predictions against evidence collected by observation and experiment.
• Engage in questioning and discussion about how the data they have
  collected can be explained in terms of the model.
• Develop explanations of scientific phenomena from models.
  For school science, model-based inquiry provides a framework for engaging students with the science content and ideas behind a practical activity. This kind of ‘minds on’ activity is critical to enhance students’ learning of scientific knowledge and insight into how scientists work (Abrahams and Millar, 2009).

Models in science

Models are a mentally visualisable way of linking theory with experiment. They enable predictions to be formulated and tested by experiment (Gilbert, 1998).
There are many different types of model. These include

• Consensus model – a model which is widely accepted by the scientific community. For example the Bohr model of an atom, or a mathematical relationship between variables.
• Historical model – a previous consensus model which has been replaced by a new, more useful model. For example the plum pudding model of an atom.
• Mental model – an individual’s internal representation (in the mind) of information in a form which is useful for solving problems. For example a flow diagram of an ecosystem.
• Teaching model – a model which has been specifically produced to teach a difficult concept. For example, ripple tanks used to teach about waves.

Model-based learning In the classroom :

Teacher

Plan to establish effective discussions and critical thinking:

• Encourage students to share their own ideas (mental models) for explaining the phenomenon, where possible.
• Listen to discussion – ask open questions which facilitate pupils’ thinking and idea expression.
• Consider the composition of groups, and how this could affect group discussion.
• Present the consensus model at an appropriate point in the lesson.
• Facilitate the critical evaluation of models by students, by challenging misconceptions as they arise and presenting
  alternative / more sophisticated models for the students to analyze the data against.

Model-based learning approach to learning science

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 analyzing 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).

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 behavior 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.
  
  

Strategies used in model-based inquiry

A model-based inquiry lesson includes:

I. Introducing models

Through these students are introduced to new ways of talking and thinking about science practical work.

II. Key conversations

the key conversations within a model-based inquiry. As students gain experience with guided forms of investigation, they become more competent inquirers by ‘internalizing’ the conversations – eventually asking themselves the relevant questions without prompting.

The scientific model can be introduced and used at different points in an inquiry; before data collection to frame a prediction and the design of a suitable experiment to test the prediction, or after data collection to frame the analysis of the results. The teacher’s role is then to support student learning through discussion and feedback.

III. Small group discussion

Small group discussions should be student-centered; structured, prompted, monitored and followed-up by teachers but not dominated by them. Teacher- led question and answer sessions may include scaffolding through cueing, corroboration or disagreement, further explanation and coaching.

Small group discussion and teacher-led question and answer sessions can be used in isolation or in combination. For example, feedback from discussions can be linked with teacher comments which evaluate and reflect on discussion outcomes or provide further explanation.

IV. Scaffolding learning

Questions which might be used to involve students in explaining and interpreting data, and comparing and critiquing models in light of evidence include:

• Why did that happen? (requires an explanation that may expose misconceptions)
• Is this explanation ... (given by students or the teacher) ... supported by the data?
• Based on what you know about ... (topic x), what do you predict would happen when ... / what data would you expect to collect if ... you carried out an experiment like this?
• How does the evidence collected support/contradict the model?
• How would you use this equation to design an experiment to check it is an
  accurate model/ description?
• What results would you expect if it was a good model?

Exemplar lesson

Using a ‘pot model’ to represent osmosis (incomplete model)

Key features

• A simple or incomplete model is presented first for students to use to predict experimental outcomes.
• Activities are devised to make sure students engage deeply with the model.
• The model is critiqued and refined to fit data from the experiment.

Small group discussion

This lesson involves students collaborating to construct a pot model to represent osmosis between a plant cell and surrounding solution. The pot model is related to a 2-dimensional model, and these are used to predict outcomes of an experiment. Simple models can help students to imagine what might be going on ‘beneath the observable surface’ as they manipulate variables and make observations in their experiments. This gives purpose to the manipulations and provides a perspective for thinking and talking about the observations (Solomon, 1999).

Scaffolding learning

When a simple model is provided that is within students’ current understanding, it can be refined through cognitive conflict. If the simple model does not provide a sufficient explanation for the data, a better model is needed. Cognitive conflict has been used in science education as a method to bring about cognitive shifts since the 1980s (Driver et al., 1985). Teachers can help to support the process where students refine their models, by highlighting the added explanatory or predictive power of the new model.

How do I transfer this approach to new contexts?

This approach is not suitable for all kinds of science practical. To decide whether to use this approach, you should consider whether students have previously been introduced to incomplete or naive scientific models. For example, students have been taught about energy and electricity in primary and early secondary education. Their models must be refined further if students are to use them to explain the phenomena they will engage with in more advanced science lessons. Processes which appear in the curriculum at various stages and at varying levels of complexity provide other possible examples, such as photosynthesis.

References

Bruner, J. S. (1999). The Processes of Education (12th ed.). Cambridge, MA: Harvard University Press.

(1996). Investigations by Order: Policy, curriculum and science teachers’ work under the Education Reform Act. pp.260. Nafferton: Studies in Education

Driver, R., Newton, P. and Osborne, J. (2000). ‘Establishing the norms of scientific argumentation in classrooms’. Science Education, 84(3), 287-312.

Driver, R., Guesne, E., and Tiberghien, A. (1985). Children’s ideas in science. Milton Keynes, England: Open University Press.

Gilbert, J.K. (1998). Explaining with models. In M.Ratcliffe (Ed.), ASE Guide to Secondary Science Education (pp.159–174). Hatfield: The Association for Science Education.

Gilbert, J.K. (2004). ‘Models and Modelling: Routes to More Authentic Science Education’. International Journal of Science and Mathematics Education, 2(2), 115–130.

Giere, R. N. (1991). Understanding Scientific Reasoning (3rd ed.). Fort Worth, TX: Holt, Rinehart and Winston.

Kahneman, D. and Tversky, A. (1982). The simulation heuristic. In Kahneman, D., Slovic, P. and Tversky, A. (Eds.), Judgement under uncertainty: Heuristics and biases (pp.201-208). New York: Cambridge University Press.

Leach, J. and Scott, P. (2002). 'Designing and evaluating science teaching sequences: An approach drawing upon the concept of learning demand and a social constructivist perspective on learning'. Studies in Science Education, 38, 115–142.

Millar, R. and Abrahams, I. (2009). 'Practical work: making it more effective'. School Science Review, 91(334), 59-64.

Osborne, J. F. and Patterson, A. (2011). ‘Scientific argument and explanation:

Quintana, C., Reiser, B., Davis, E. A., Krajcik, J., Fretz, E., Duncan, R. G., Kyza, E., Edelson, D. and Soloway, E. (2004). 'A Scaffolding Design Framework for Software to Support Science Inquiry'. Journal of the Learning Sciences, 13(3), 337-386.

Reiser, B., J. (2004). 'Scaffolding complex learning: The mechanisms of structuring and problematizing student work'. The Journal of the Learning Sciences, 13(3), 273-304.

Schwartz, D. L. and Bransford, J. D. (1998). 'A Time for Telling'. Cognition and Instruction, 16(4), 475- 522.

Solomon, J. (1999). Envisionment in practical work: Helping pupils to imagine concepts while carrying out experiments. In Leach, J. and Paulsen, A. (Eds.), Practical work in science education: Recent research studies (pp.60–74). Roskilde/Dordrecht, The Netherlands: Roskilde University Press/Kluwer.

Windschitl, M., Thompson, J. and Braaten, M. (2008). 'Beyond the scientific method: Model based inquiry as a new paradigm of preference for school science investigations.’ Science Education, 92(5), 941-967.

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