Iterative

Contribution

Kolb's four-stage cycle is not a linear pipeline but an explicit iteration: each pass through experience → reflection → conceptualisation → experimentation feeds the next pass with refined hypotheses.

In our sims

Multi-round simulations let students complete two or three full Kolb cycles inside a single workshop. The same module run with different volatility forces re-experimentation, not repetition.

Contribution

Dweck's research shows that learners holding a growth mindset treat mistakes as informational input rather than identity-threatening failure, and consequently iterate more, faster.

In our sims

Sim language is deliberately growth-mindset shaped: 'you can replay this round with different volatility' is the default option after a poor run, not a remedial action.

Contribution

Ericsson's work establishes deliberate practice, focused, feedback-rich, repeated effort at the edge of competence, as the mechanism behind elite performance.

In our sims

Iteration is structured: students replay specific decision points with the rest of the simulation re-randomised, not the whole sim restarted. Practice is deliberate, not just exposure.

Contribution

Hattie identifies feedback as the highest-effect-size factor but with one critical condition: it must close the loop. Feedback received but not applied in a subsequent attempt produces minimal gain.

In our sims

Iteration closes Hattie's loop. The post-run reflection in MyEdMentor is followed (where the tutor permits) by a second run with the student carrying forward what they noted.

Contribution

The OECD's 2030 framework explicitly proposes an Anticipation–Action–Reflection (AAR) cycle as the operating loop for developing student agency in modern education systems.

In our sims

Each sim turn is an AAR micro-cycle: students anticipate (read the advisor brief), act (commit a decision), reflect (debrief journal). The full sim is then a macro-AAR, replay the module with what you learned.