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All Visualizations

Showing 12 results

Electric Potential Energy & Potential visualization thumbnail
AP PHYSICS C EM

Electric Potential Energy & Potential

Calculate electric potential $V = \frac{kQ}{r}$ and potential energy $U = qV$ for point charges and charge distributions. Visualize equipotential surfaces, explore the relationship $\vec{E} = -\nabla V$, and analyze how charges move from high to low potential.

Faraday's Law & Lenz's Law visualization thumbnail
AP PHYSICS C EM

Faraday's Law & Lenz's Law

Apply Faraday's law $\mathcal{E} = -\frac{d\Phi_B}{dt}$ to calculate induced EMF from changing magnetic flux. Visualize how Lenz's law determines the direction of induced current to oppose flux changes, and explore applications in generators and transformers.

Electric Field & Gauss's Law visualization thumbnail
AP PHYSICS C EM

Electric Field & Gauss's Law

Apply Gauss's law $\oint \vec{E} \cdot d\vec{A} = \frac{Q_{enc}}{\epsilon_0}$ to calculate electric fields for symmetric charge distributions. Visualize electric flux through Gaussian surfaces and solve for fields around spheres, cylinders, and infinite planes using symmetry arguments.

Maxwell's Equations Overview visualization thumbnail
AP PHYSICS C EM

Maxwell's Equations Overview

Explore the four Maxwell's equations that unify electricity and magnetism: Gauss's law, Gauss's law for magnetism, Faraday's law, and Ampère-Maxwell law. Visualize how these equations predict electromagnetic wave propagation and the interconnection between electric and magnetic fields.

RL Circuit Transient Response visualization thumbnail
AP PHYSICS C EM

RL Circuit Transient Response

Analyze RL circuit behavior with exponential current growth $I(t) = I_0(1 - e^{-t/\tau})$ where $\tau = L/R$ is the time constant. Visualize how inductors resist current changes, store magnetic energy, and create transient responses when switches open or close.

Classical Conditioning visualization thumbnail
AP PSYCHOLOGY

Classical Conditioning

Explore Pavlov's classical conditioning process where neutral stimuli become conditioned stimuli through repeated pairing with unconditioned stimuli. Visualize acquisition, extinction, spontaneous recovery, and stimulus generalization in learning paradigms.

Normal Distribution & IQ visualization thumbnail
AP PSYCHOLOGY

Normal Distribution & IQ

Visualize how IQ scores follow a normal distribution with mean 100 and standard deviation 15. Explore z-scores, percentiles, and the 68-95-99.7 rule to understand how intelligence test scores are distributed across populations.

Maslow's Hierarchy of Needs visualization thumbnail
AP PSYCHOLOGY

Maslow's Hierarchy of Needs

Explore Maslow's five-tier pyramid of human needs from physiological basics to self-actualization. Visualize how lower-level needs must be satisfied before higher-level psychological and self-fulfillment needs can be pursued.

Memory & Encoding visualization thumbnail
AP PSYCHOLOGY

Memory & Encoding

Explore the three stages of memory: encoding, storage, and retrieval. Visualize different encoding strategies including semantic, acoustic, and visual processing, and understand how depth of processing affects long-term memory retention.

Neuron & Action Potential visualization thumbnail
AP PSYCHOLOGY

Neuron & Action Potential

Visualize how neurons transmit electrical signals through action potentials. Explore the resting potential, depolarization, repolarization cycle, and the all-or-none principle as sodium and potassium ions flow across the axon membrane.

Signal Detection Theory visualization thumbnail
AP PSYCHOLOGY

Signal Detection Theory

Explore signal detection theory's framework for measuring perceptual sensitivity (d') and response bias. Visualize the four outcomes—hits, misses, false alarms, and correct rejections—and understand how expectation and motivation affect detection thresholds.

Binomial Distribution visualization thumbnail
AP STATISTICS

Binomial Distribution

Model discrete probability distributions for fixed trials with $P(X=k) = inom{n}{k}p^k(1-p)^{n-k}$. Visualize how the number of trials and success probability affect the shape, mean $mu = np$, and standard deviation $sigma = sqrt{np(1-p)}$ of binomial distributions.