# US 2019/0318650 A1 · Method and apparatus for learner diagnosis using reliability of cognitive diagnostic model

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- Number: US 2019/0318650 A1
- Document ID: US20190318650A1
- Status: Published application
- Kind: A1
- Application number: 16/377,624
- Filed: 2019-04-08
- Published: 2019-10-17
- Earliest claimed: 2018-04-11
- Assignees: Electronics and Telecommunications Research Institute
- Applicants: Electronics and Telecommunications Research Institute
- Inventors: Ho Young YOO; Su Woong LEE; Hyung Keun JEE
- Web page: https://patentgrep.com/patent/US20190318650A1

## CPC

- G09B 19/00: Teaching not covered by other main groups of this subclass (teaching or practice apparatus for gun-aiming or gun-laying F41G3/26)
- G06F 17/18: for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)}
- G09B 7/02: of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student
- G09B: EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS (devices for psychotechnics or for testing reaction times A61B5/16; games, sports, amusements A63; projectors, projector screens G03B)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

Provided are an apparatus and method for learner diagnosis using reliability of a cognitive diagnostic model, which estimates the reliability of the cognitive diagnostic model that estimates a concept vector (α) of a learner through a Q-matrix regarding a question and an R-matrix regarding a response to a question, the method including assuming a probability (P(X|α)) of a learner response (X) when a concept vector (α) of a learner is given; obtaining a concept pattern-specific probability (P(α|X)) of the learner from the assumed concept vector and learner response of the learner; obtaining an information entropy (H) value of the learner from the concept pattern-specific probability (P(α|X)) of the learner; and obtaining reliability (γ) of an estimated result of a learner-specific concept understanding using the information entropy value of the learner and a number of concepts.

## Claims

### Claim 1

A method for learner diagnosis using reliability of a cognitive diagnostic model, which estimates the reliability of the cognitive diagnostic model that estimates a concept vector (α) of a learner through a Q-matrix regarding a question and an R-matrix regarding a response to a question, the method comprising:
assuming a probability (P(X|α)) of a learner response (X) when a concept vector (α) of a learner is given;
obtaining a concept pattern-specific probability (P(α|X)) of the learner from the assumed concept vector and learner response of the learner;
obtaining an information entropy (H) value of the learner from the concept pattern-specific probability (P(α|X)) of the learner; and
obtaining reliability (γ) of an estimated result of a learner-specific concept understanding using the information entropy value of the learner and a number of concepts.

### Claim 2 (depends on 1)

The method for learner diagnosis of claim 1, wherein the obtaining of the concept pattern-specific probability (P(α|X)) of the learner is achieved using a Bayesian theorem.

### Claim 3 (depends on 1)

The method for learner diagnosis of claim 1, wherein the obtaining of the information entropy (H) of the learner is achieved using an expectation-maximization (EM) algorithm or a Markov chain Monte Carlo (MCMC) algorithm.

### Claim 4 (depends on 1)

The method for learner diagnosis of claim 1, wherein, in response to presence of reliabilities (γ) of estimated results from a plurality of estimations on a learner-specific concept understanding for the learner, an ith reliability is used as a weighting coefficient for a concept understanding to obtain a concept understanding (α1) of an ith learner.

### Claim 5

An apparatus for learner diagnosis using reliability of a cognitive diagnostic model, comprising:
a learner response probability calculation unit configured to assume a probability (P(X|α)) of a learner response (X) when a concept vector (α) of a learner is given;
a concept pattern-specific probability calculation unit configured to obtain a concept pattern-specific probability (P(α|X)) of the learner as a posterior probability;
a learner information entropy calculation unit configured to obtain an information entropy (H) value of the learner from the concept pattern-specific probability (P(α|X)) of the learner; and
a reliability calculation unit configured to obtain reliability (γ) of an estimated result of a learner-specific concept understanding using the information entropy value of the learner and a number of concepts.

### Claim 6 (depends on 5)

The apparatus of claim 5, wherein the learner response probability calculation unit uses a Bayesian theorem.

### Claim 7 (depends on 5)

The apparatus of claim 5, wherein the learner information entropy calculation unit uses an expectation-maximization (EM) algorithm or a Markov chain Monte Carlo (MCMC) algorithm.

### Claim 8 (depends on 5)

The apparatus of claim 5, further comprising a weight processing unit configured to use an ith reliability as a weighting coefficient for a concept understanding to obtain a concept understanding (αl) of an ith learner, in response to presence of reliabilities γ of estimated results from a plurality of estimations on a learner-specific concept understanding for the learner.