Category Archives: Linux

Dabao Evaluation Board to Showcase Open-RTL Baochip-1x RISC-V MCU

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Baochip has previewed the Baochip-1x, a mostly open RTL, RISC-V–based microcontroller fabricated on TSMC’s 22 nm process. Designed with openness and verifiability in mind, the MCU integrates a VexRiscv application core running at up to 350 MHz, alongside a quad-core I/O accelerator cluster clocked at 700 MHz. The Baochip-1x uses a VexRiscv RV32IMAC processor with […]

Just the Browser is just the beginning: Why breaking free means building small

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Privacy tools are a start, but real freedom lives in the digital outskirts of the webOpinion The Net is born free, but everywhere is in chains. This is a parody of Jean-Jacques Rousseau’s 1762 book The Social Contract where he said the same about humans, but it’s nonetheless true. The Net is built out of open, free protocols and open, free code. Yet it and we are bound by the rulemakers who build the services and set the laws of the places we go and the things that we do, not to our advantage.…

New Intel Linux Code For DG2 Graphics Can Improve Performance As Much As “A Whopping 260%”

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A set of 18 patches were merged overnight to Mesa 26.1 for working around graphics corruption on Meteor Lake and DG2/Alchemist class graphics hardware. Not only are some graphics corruption issues worked around but for that hardware there is as much as “a whopping 260%” performance improvement observed for some graphics workloads…

ThinkPads On Linux Appear Nearly Ready For Improved Trackpoint Doubletap Handling

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Being worked on for a while by Lenovo engineer Vishnu Sankar is nicely handling support for double-tap functionality with TrackPoints on ThinkPads under Linux. The sixth iteration of this enablement work was posted today and is just documentation updates, so it’s looking like this new TrackPoint doubletap code could soon be crossing the threshold for the mainline Linux kernel…

How to strategically plan your computing curriculum

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Traditionally, curriculum planning has often looked like a linear list: Topic A leads to Topic B, which leads to Topic C. However, as educators we know that learning rarely happens in such a simple, linear way. Concepts are regularly covered in different overlapping topics, and students can often take different routes to reach the same destination.

Image displaying the Learning Graphs Quick Read.

In today’s blog we’re exploring learning graphs, a helpful tool that you can use to plan your computer science curriculum. We’ll share how they can provide educators with a clear, structured way to visualise students’ non-linear progression in a subject.

We also share our new Pedagogy Quick Read about learning graphs, which you can download for free to:

  • Find practical tips on how you can use learning graphs to design your curriculum
  • Read a summary of the research behind them

What is a learning graph?

A learning graph is a visual tool for curriculum planning that moves beyond simple lists. At its core, a learning graph is a network of ‘nodes’ (specific concepts and skills) and ‘links’ (the connections between them).

Image from the Learning Graphs Quick Read that showcases an example of a learning graph.

Learning graphs build on research into ‘learning progressions’ and ‘knowledge maps’. They are a practical tool that educators can use to design and validate different curricula. For example, they can help teachers to:

  • Visualise and map progression
  • Identify curriculum gaps, so educators can shape and restructure learning experiences as necessary
  • Ensure the use of consistent terminology
  • Sequence learning and manage cognitive load

How to create a learning graph

Building a learning graph is an iterative process that helps you think critically about how different parts of your curriculum relate to each other.

Nodes and links

The first step in creating a learning graph is often to identify your start and end nodes. First, you consider the key concepts and skills that your learners must acquire by the end of a series of lessons. This gives you some end nodes to work towards. Then, you think about learners’ existing knowledge, to help determine your start point. You then work backwards and forwards between these points to identify the different nodes that learners need to cover to get from the beginning to the end.

Educators sharing ideas around a table.

Once you have determined your nodes, you add them to your graph and connect them via ‘links’ until your graph is complete. Where knowledge of particular concepts or skills is essential for learning others, you connect the nodes with solid lines. For prior learning that is helpful but not essential, you use dotted lines.

When developing a learning graph, there isn’t a specific level of granularity that you have to work towards. Progression can be as detailed or as high-level as you need. This makes them a helpful tool in creating bespoke learning experiences and curricula for learners.

Collaboration and development

It is most effective to design learning graphs collaboratively within a small group. This allows curriculum designers to discuss their ideas and challenge each other’s thinking, which helps hone the designs.

Educators collaborating using post-it notes, planning currciulum.

When creating learning graphs, it can be extremely useful to use a tool that is dynamic and allows you to move elements and make changes quickly and easily. At the Raspberry Pi Foundation, our team has experimented with a range of tools, including using editable shapes in Google Slides, collaborating in Figma, and arranging sticky notes on paper. We recommend finding a tool that works for you and the educators you are working with. Although it can work, we suggest avoiding using a pen and paper if possible, as designs can quickly become messy and difficult to navigate after lots of iterations.

The process of designing learning graphs has strong links to ABC learning design and the creation of concept maps, which can also be used for curriculum planning.

Learning graphs in your teaching

Once created, learning graphs can support you to design and adapt your curricula and assess your students’ learning.

For example, to help sequence learning, you can track or predict the paths through a topic most commonly taken by learners and use this to inform your curriculum design.

If you are adapting a unit of work for a specific qualification or new context, you can prune nodes that are not relevant and add any further knowledge and skills your learners need, then use the new learning graph to guide you as you develop the unit.

Finally, you can assess which node a learner has completed, and use this to identify the next logical step in their learning, ensuring the difficulty level is always appropriate.

Using learning graphs to support analysis

Another benefit of learning graphs is that they can be combined with lots of other frameworks, for example, Bloom’s taxonomy. This allows you to better assess and validate the learning journeys you have designed, and ensure that they are suitably accessible, challenging, and relevant for your learners.

Photo of an educator presenting at the front of a classroom of other teachers.

There are a number of ways that you could link your learning graphs to other frameworks, such as annotating nodes with extra information, or using colour coding.

As well as working with learning graphs for specific learning experiences, you can connect multiple learning graphs together and analyse how they intersect. This can help identify inconsistencies between connected sequences of lessons. It can also help uncover broader themes of progression and highlight alternative learning pathways you might not have considered.

Find out more about learning graphs

If you’d like to find out more about learning graphs, you can download our Pedagogy Quick Read for free.

To find out more about how we use learning graphs when planning curriculum resources at the Raspberry Pi Foundation, take a look at our teaching and learning design principles.

The post How to strategically plan your computing curriculum appeared first on Raspberry Pi Foundation.

Challenger+ T3217 Packages 8-bit ATtiny3217 in a Compact, Battery-Ready Board

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The Challenger+ T3217 is a compact development board based on Microchip’s ATtiny3217, combining the tinyAVR 1-series platform with a small, battery-ready form factor for low-power embedded applications. The board is based on the ATtiny3217, an 8-bit AVR microcontroller running at up to 20 MHz from its internal oscillator. It integrates 32 KB of Flash, 2 […]

AMD Squeezing Out More More ROCm/HIP Performance With New Device-Side PGO

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Compiler profile guided optimization (PGO) techniques have paid off well for increasing CPU performance via application/workload-specific profiles fed back to the compiler to make more informed decisions. AMD compiler engineers have been working on crafting device-side PGO for their AMDGPU LLVM back-end for allowing ROCm/HIP workloads to achieve greater GPU performance. An initial merge request is now open for upstream LLVM…