Submit a request
Submit a request

Adaptive Simulations & Results Explained

When editing Adaptive Activities, simulations of the activity can be created and run from the 'Adaptive' tab.

To create an adaptive simulation, you are required to provide:

  • A unique reference for the simulation
  • The number simulated users to run through the activity (500, 1000 or 10,000)
  • An initial 'true' user ability distribution for the simulated users (Standard or Bimodal Distributions, or, fine-grain custom ranges of user ability).

Standard distribution


The standard distribution model for 'true' user ability requires a mean value and a standard deviation value to generate a simple user ability bell curve. All simulated users will draw from this bell curve for their true ability.

Bimodal distribution


Much like the standard distribution model, the bimodal distribution uses mean values and standard deviations to generate user abilities. This produces a more complex twin bell curve for the simulated users true abilities.

Custom distribution


Custom distribution allows you to set up explicit user ability ranges for all of the simulated users. The number of users attributed to each range will each be assigned random true abilities from within that range, with no other distribution bias. You can be as granular as you want when assigning the user ability scores this way.

Adaptive simulation results

Adaptive simulation settings

The adaptive simulation settings are a summary of the snapshot of data that went into the simulation.
It will break down the user ability distribution by numbers, and allow you to review the adaptive specific activity definition that was captured at the creation time of the simulation.
This section is strictly the definition for the report.


Item exposure

Item exposure results allow you to explore an interactive graph of the total Item exposure (how often an Item or Items were accessed by a simulated user), at the various Item difficulty ranges.
By default, graph data is shown in 'overview' level granularity to give more averaged data of potentially thousands of results.
There is an option to switch to finer granularity, as well as to scale the graph difficulty range down to inspect the data more closely.
More raw Item exposure data is available in table format in the respective tab, and exists at the granularity set on the Item level.


Tag exposure

Tag exposure results allow you to explore and compare Tag exposure (how often Tags are exposed to a simulated user) at Item difficulty ranges.
You're presented with a list of all Tags that achieved any kind of exposure across the activity during the simulation, and can select any number of them to compare exposures on the same graph.
Tag granularity exists from Tag Types to Tag Names, and exposure data is averaged into two useful granularity levels (overview and fine) for viewing and comparison.
The ability to scale the graph difficulty ranges down for fine grain inspection is also available.
The more raw data is available in table format in the Tag exposure data tab.


Was this article helpful?
0 out of 0 found this helpful

Did you arrive here by accident? If so, learn more about Learnosity by clicking here.