API services

services Module

class adaptivetesting.services.IEstimator(response_pattern: List[int] | ndarray, items: List[TestItem], optimization_interval: Tuple[float, float] = (-10, 10))

Bases: ABC

This is the interface required for every possible estimator. Any estimator inherits from this class and implements the get_estimation method.

Args: : response_pattern (List[int]): list of responses (0: wrong, 1:right) items (List[TestItem]): list of answered items

abstractmethod get_estimation() → float

Get the currently estimated ability.

Returns: : float: ability

abstractmethod get_standard_error(estimation: float) → float

Calculates the standard error for the given estimated ability level.

Args: : estimation (float): currently estimated ability level

Returns: : float: standard error of the ability estimation

class adaptivetesting.services.ITestResults(simulation_id: str, participant_id: str)

Bases: ABC

Interface for saving and reading test results. This interface may mainly be used for saving simulation results.

Args: : simulation_id (str): The simulation ID. Name of the results file. participant_id (str): The participant ID.

abstractmethod load() → List[TestResult]

abstractmethod save(test_results: List[TestResult]) → None

class adaptivetesting.services.ItemSelectionStrategy(*args, **kwargs)

Bases: Protocol

A protocol for item selection strategies in adaptive testing.

This protocol defines a callable interface for selecting a test item from a list of available items, given the current ability estimate and optional additional parameters.

Args:

items (list[TestItem]): The list of available test items to select from. ability (float): The current ability estimate of the test taker.

**

kwargs: Additional keyword arguments that may be required by specific selection strategies.

Returns: : TestItem: The selected test item based on the implemented strategy.