VR Presence & Memory Encoding Simulator

Immersion Settings

Immersion level 65%

Background distraction 20%

Learning State

Learner enters the VR environment…

Presence

Cognitive load

Retention rate

Concepts encoded

0 / 24

How it works

Each glowing node in the 3D scene is a "concept" in a knowledge graph. As a simulated learner moves through the VR lesson, concepts get encoded into long-term memory one at a time along a connected path — the model combines three well-established learning-science ideas:

Presence  P  = 0.9·Immersion + (Active ? 10 : 0) − 0.25·Distraction
Load      L  = Intrinsic(35) + 0.65·Distraction − (Active ? 20 : 0)
Capacity  C  = clamp(100 − L, 0, 100)
Retention R  = clamp(P/100 · C/100, 0, 1)

Presence (Witmer & Singer, 1998) — the sense of "being there" in the virtual environment; it rises with immersion and active control, and is degraded by distraction.
Cognitive Load Theory (Sweller, 1988) — a fixed intrinsic load from the material's own complexity, an extraneous load from distraction that wastes working memory, and germane capacity freed up by active, generative processing.
Active vs. passive processing (Mayer's generative-learning principle) — manipulating content, not just watching it, converts more of the session into durable encoding.

The retention rate sets how fast the highlighted concept's node fills in and lights up; once full, encoding moves to the next connected concept, and its edges brighten to show the growing memory network. Push distraction high enough that load exceeds 100% and encoding stalls — a simplified, pedagogically-grounded model of cognitive overload, not a literal biological simulation.

Encoded concept Not yet encoded

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