Learning graphs: Tools to plan for progression

By Carrie Anne Philbin. Posted

The learning graph for the Year 8 topic of Python programming. Solid arrows show prerequisites while dotted arrows show a suggested order of learning.

Carrie Anne Philbin introduces an approach to map the computing curriculum

A progression framework is the backbone of any subject curriculum. It illustrates the sequence in which children learn, noting where core understanding of a topic is established in order to progress. As part of the National Centre for Computing Education (NCCE), my team is developing a bank of teaching resources that deliver the computing curriculum in England. In order to successfully do this, we’ve studied a lot of progression frameworks, examination specifications, and even some research papers. We found that there are two quite different ways of presenting progression that show what should be taught and when it should be taught, as well as information on how or why concepts should be taught.

Firstly, there is the approach of creating a categorisation of skills and concepts into a list or table. Sequencing is shown by having objectives listed by key stage, year group or even by learners’ age. Examples of this approach include the CAS computing progression pathways, and the Massachusetts Digital Literacy and Computer Science Curriculum Framework. They are essentially a list of required knowledge bundled by theme. 

Another approach is to use a map of possible trajectories through learning waypoints—key building blocks of learning—and how they connect to each other. This approach highlights where prerequisite knowledge needs to be mastered before students can move on, as well as the dependent knowledge contained in other ‘nodes’, each containing one part of the computing curriculum that needs to be mastered in order to progress. Cambridge Mathematics are leading the way in ‘developing a flexible and interconnected digital framework to help reimagine mathematics education 3–19’. We’ve been lucky enough to learn from their work, which has helped us to create learning graphs.

A tool for teachers

The learning graphs organise computing content—concepts, knowledge, skills, and objectives—into interconnected networks. We found that nodes often form clusters corresponding to specific themes and we could connect them if they represent two adjacent waypoints in the learning process. Depending on the context, the nodes in a learning graph could contain anything ranging from the contents of a curriculum strand across an entire key stage, to the learning objectives of a 6-lesson unit. When our team starts working on a unit, the learning graphs are in a fluid state: they uncover the structure of the content and the possible journeys through it, without being bound to a specific teaching pathway. The graphs eventually reach a fixed state, where the nodes are further structured and arranged to reflect our suggestions on the order that the content could actually be delivered.

We believe that learning graphs could be useful to teachers on a whole new level. They directly inform lesson planning, but also add value by showing opportunities to assess understanding at landmark points in a lesson or unit. By checking that students are grasping the concepts, teachers are able to think more about how they are teaching. They can revisit knowledge that perhaps didn’t land with learners the first time.

All progression frameworks are subjective, and with little research into computing education, we rely on teachers’ experience of combining the ‘what’ we teach and ‘how’ to teach it to help inform this work. If you’ve not taken a look at our learning graphs, you can access them via teachcomputing.org/resources. Do let us know your thoughts by emailing us at resourcesfeedback@raspberrypi.org.

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