Patent ID: 11941999
Assignee: INSTRUCTURE, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 18:
19. An apparatus for diagnostic assessment, comprising:
a processor,
memory in electronic communication with the processor; and
a diagnostic assessment component stored in the memory and executable by the processor, the diagnostic assessment component comprising:
a response manager configured to:
receive responses at the processor generated by respondents to items configured to classify at least one ability of the respondents;
identify outputs at the processor of the items based on the responses of the respondents;

a mapping manager configured to identify a mapping at the processor of the items to a plurality of attributes associated with the respondents, wherein a first attribute of the attributes is polytomous and indicates a degree to which a respective respondent of the respondents possesses the first attribute of the attributes, wherein the mapping comprises a relationship matrix that assigns loadings to the attributes measured by each of the items in a confirmatory factor analysis, and wherein the loadings are polytomous;
an attribute profile manager configured to identify a plurality of attribute profiles at the processor representing what attributes are possessed by the respondents, each attribute profile comprising a pattern of descriptive statistics indicating a permutation of possessed attributes;
an item parameter manager configured to:
identify one or more item parameters at the processor for each item based at least in part on the mapping of the items to the plurality of attributes, wherein a set of item parameters of the one or more item parameters associated with a first item is subject to a set of linear monotonicity constraints such that the set of item parameters and a first attribute profile of the plurality of attribute profiles associated with the first item is greater than zero; and
tune the item parameters at the processor using machine learning based on the outputs, past diagnostic assessments, past item parameters, and past outputs, wherein tuning the item parameters comprises:
retrieving by the processor the outputs, the past diagnostic assessments, the past item parameters, and the past outputs from a database; and
storing by the processor updated item parameters in the database after tuning the item parameters;

a probability manager configure to:
determine a plurality of probabilities at the processor for each item based at least in part on identifying the one or more item parameters, each probability indicating a likelihood of a respective response to a respective item conditional on a respective attribute profile of a respective respondent; and
apply the plurality of probabilities at the processor to a standard normal cumulative distribution function;

a classification manager configured to classify the respondents at the processor with attribute profiles based at least in part on the responses received for the items and determining the plurality of probabilities for each item, wherein the respondents are classified with the attribute profiles based at least in part on the plurality of probabilities being applied to the standard normal cumulative distribution function; and
a vector manager configured to:
generate at the processor vector values by applying a vector-valued function to the attribute profiles and the loadings of the relationship matrix; and
combine at the processor the vector values with the item parameters according to the function:

λiTh(qi,αr)=λi,0+Σa=1Aλi,1(a)(qiaαra)+Σa=1A−1Σb=a+1Aλi,2,(a,b)(qiaαra)(qibαrb),, where λ refers to the item parameters, h(qi, αr) refers to the vector-valued function, q refers to the relationship matrix, i refers to the items, α refers to the attribute profiles, r refers to the respondents, and a refers to the attributes.