METHOD AND APPARATUS FOR LEARNER DIAGNOSIS USING RELIABILITY OF COGNITIVE DIAGNOSTIC MODEL

• US 20190318650A1
• Filed: 04/08/2019
• Published: 10/17/2019
• Est. Priority Date: 04/11/2018
• Status: Abandoned Application

• Current Assignee: Electronics and Telecommunications Research Institute
• Original Assignee: Electronics and Telecommunications Research Institute
• Inventors: YOO, Ho Young, LEE, Su Woong, JEE, Hyung Keun
• Application Number:
  US16/377,624
• Publication Number:
  US 20190318650A1
• Time in Patent Office: Days
• CPC Class Codes: 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
• CPC Class Codes: 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
• CPC Class Codes: 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

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

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.

2. 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.

3. 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.

4. 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 i^th reliability is used as a weighting coefficient for a concept understanding to obtain a concept understanding (α

₁) of an i^th learner.

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.

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

7. 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.

8. The apparatus of claim 5, further comprising a weight processing unit configured to use an i^th reliability as a weighting coefficient for a concept understanding to obtain a concept understanding (α

_l) of an i^th 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.