By Ryan Badham
At the very end of the previous academic year, we were joined by a team of Ofsted inspectors whom corroborated our own opinions of the strengths and weaknesses of our department and curriculum, namely great retention of substantive knowledge by our students but a weaker retention of disciplinary knowledge by our students. From our own results analysis this area had been pinpointed as our next area of focus.
In the Ofsted Subject Science Review substantive knowledge is defined as: “(knowledge of the products of science, such as concepts, laws, theories and models): this is referred to as scientific knowledge and conceptual understanding in the national curriculum”
For examples this could by the word or symbol equation of photosynthesis, the different energy stores, the charge and mass of a proton etc.
The other broad categorisation of knowledge is disciplinary knowledge: “(knowledge of how scientific knowledge is generated and grows): this is specified in the ‘working scientifically’ sections of the national curriculum and it includes knowing how to carry out practical procedures.”
What follows below is my thinking and my plan to improve this part of our Science curriculum. The following will be part of a series of blogs focusing on this area
Step 1: Clearly define what is and isn’t disciplinary knowledge
Disciplinary knowledge is how knowledge is generated in scientific fields and how this knowledge evolves. It is more than practical’s in science. A potential pitfall is to think of it as solely doing practical’s in science.
To help focus my thinking I have used the four areas provided by the Ofsted Research into subject science review.
- Knowledge of methods that Scientists use to answer questions
- Knowledge of apparatus, techniques, including measurement
- Knowledge of data analysis
- Knowledge of how science uses evidence to develop explanations
For each of these four areas I have then tried to further describe what aspects would be present within each area and where applicable what that area isn’t.
| Broad Area | Knowledge of methods that scientists use to answer questions. | Knowledge of apparatus and techniques, including measurement. | Knowledge of data analysis. | Knowledge of how science uses evidence to develop explanations |
| Definition | The different methods that scientists use to generate knowledge. | This covers how to carry out specific procedures and protocols safely and with proficiency in the laboratory and field. | This covers how to process and present scientific data in a variety of ways to explore relationships and communicate results to others. Pupils learn about different types of tables and graphs and how to identify correlations. | This covers how evidence is used, alongside substantive knowledge, to draw tentative but valid conclusions. It includes the distinction between correlation and causation and knowing that explanation is distinct from data and does not simply emerge from it. |
| What it is | Use of models, classification techniques, pattern analysis, experimentation | The 21 required practicals specified by AQA Combined science specification. Accurate measurement and recording of data | Identifying correlations/ patterns in graphs. Using Tables and graphs to evaluate a point | How scientific models change over time, how evidence builds on evidence and how technological development leads to changing theories. The importance of Peer review. |
| What it isn’t | Students categorising pictures of animals into arbitrary groups | Simply carrying out the practical. | Simply plotting a graph | Data collection devoid of analysis or explanation |
| A curricular Example | The model used by Watson and Crick The Rutherford model of the atom | AQA Required Practicals | Use of longitudinal studies to show risk factors for non-communicable diseases | The history of the atom The history of the periodic table Andrew Wakefield’s work on Vaccines Drug development |
Step 1.5 Deciding the powerful knowledge
The above table shows my initial plan. Following on from a really interesting conversation with Karen Mcfadian (https://twitter.com/KEMcFadian?s=20) in which she spoke about the importance of breaking down these separate areas into the smaller nuggets of knowledge that make up the larger whole. During this conversation she outlined an example of an observed year 7 lesson about graph drawing in which the aim was to draw a graph and how this overall aim may result in cognitive overload if students are expected to master all the separate aspects of effective graph drawing in one single sitting. From that conversation I have also now looked at breaking down the separate disciplinary areas into their component parts. Any further suggestions welcome.
| Knowledge of methods that scientists use to answer questions. | Knowledge of apparatus and techniques, including measurement. | Knowledge of data analysis. | Knowledge of how science uses evidence to develop explanations |
| Why scientists use models? | Identifying basic lab equipment | Labelling axis | Examples of how a model has changed over time |
| How scientists use models? | Using a Bunsen burner | Creating a scale | Importance of peer review |
| Limitations of models | Using a thermometer | Plotting data on a line graph | Link between technological development and changing models |
| What is classification? | Using digital equipment such as a probe | Plotting data on a bar chart | Meaning of correlation |
| How does classification help scientists answer questions? | Using a measuring cylinder | Drawing a straight line of best fit | Meaning of causation |
| Using a scales/balance | Drawing a curved line of best fit | Difference between correlation and causation | |
| Using a gas syringe | Analysing data from a table | ||
| Meanings of hazard symbols | Creating headings for a table | ||
| Identifying risks | Recording data on a table | ||
| Minimising risks | Calculating uncertainty | ||
| Designing a practical method | Calculating a mean | ||
| Suggest what apparatus to use in different scenarios | |||
Ultimately, she was saying we should be teaching the component skills of graph drawing over a significant period of time (KS3) building them up over time rather going straight in expecting them to master the finished product in one lesson resulting in Cog. Overload.
Step 2: Curriculum weaving
Once I had identified the different areas of disciplinary knowledge and began to decide the powerful granular knowledge that makes it up the next step was weaving this into a curriculum. A tick designates where something is already present in our curriculum in my opinion. A ✔X represents an area where it should be present but currently isn’t. The image below shows part of my work on this.
In the next blog in this series, I will look at how I implemented the disciplinary knowledge in our curriculum and any lessons learnt along the way. By no means am I 100% convinced this is the right way to “do” disciplinary knowledge and would welcome any feedback on this. Please don’t hesitate to contact me on ryan.badham@holmesdale.kent.sch.uk. Please feel free to visit my twitter page for any of the resources discussed here – https://twitter.com/mr_badham
CogSciSci 
whilst I agree with the notion of breaking down a skill into constituent parts that can be built up over time to avoid cognitive overload, I think the graph example raises an interesting question about transition. In particular the transition from primary to secondary and what we can expect them to already know / be able to do. Primary colleagues frequently (and often rightly) criticise secondary teachers for underestimating year 7 pupils, but there is a challenge in identifying the level of prior knowledge and skill in a mixed intake from across numerous primary schools and experience of different curricula.
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