Multi-Vector Learning

Our curiosity began when we did an A/B experiment at Woodcrest State College with 4 classes of Year 4 students.

The grades from the two classes that used DiGs for their unit were significantly higher than those that used traditional explicit instruction.

As we explored the reasons for this higher academic achievement as learning came alive at Woodcrest, we wanted to understand the system dynamics of this transformation. This exploration led us to develop a causal loop diagram to describe the dynamics.

From that diagram, we created a driver tree that focused on the dynamics that led to higher student achievement.

We named and began to be curious about the dynamics that swirled around student achievement, calling this a 'learning vortex'.

We then began to unpack the learning experience using the model of Active Inference. This process led us to define what we called the Learning Schema.

This model helped us to illuminate how the DiG framework helps develop the Third Order Meaning that powerful demonstrates Deep Learning.

Using this model, we began to capture the learning stories of individual students.

Mapping these stories together we could, for the first time, see their interconnections that allows Creative Amplification to exponentially increase learning potential.

Here we realized that learning had moved from a 'single vector' to a networked 'multi-vector' experience, helping us to more deeply understand the experience and to begin to be more intentional about Creating Space for learning and guiding us into a Blue Plane Vision.