SYSTEMS AND METHODS TO CUSTOMIZE STUDENT INSTRUCTION

• US 20130288222A1
• Filed: 04/29/2013
• Published: 10/31/2013
• Est. Priority Date: 04/27/2012
• Status: Active Application

• Current Assignee: Aptima Inc.
• Original Assignee: Aptima Inc.
• Inventors: Stacy, E. Webb, Dean, Courtney, Carlin, Alan, Dumond, Danielle
• Granted Patent: US 10,290,221 B2
• Time in Patent Office: Days
• US Class Current: 434/362
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G09B 5/00 Electrically-operated educational appliances working with questions and answers G09B7/00; simulators G09B9/00; advertising or displaying in general G09F
• CPC Class Codes: G09B 7/00 Electrically-operated teaching apparatus or devices working with questions and answers mechanically operated G09B3/00; computing arrangements G06F

Abstract

A computer implemented systems and methods for determining an action for a user within a learning domain are disclosed, some embodiments of the methods comprise defining an initial learning model of a learning domain, determining an initial user state of the user, determining an initial user action from at least one learning domain action with the initial learning model, receiving a user observation of the user after the user executes the initial user action, determining an updated user state with the initial learning model given the updated user observation and determining a subsequent user action from the at least one learning domain action. Some embodiments utilize a Partially Observable Markov Model (POMDP) as the learning model.

Claims

1. A computer implemented method for determining an action for a user within a learning domain, the method comprising:

defining an initial learning model of a learning domain comprising;

a plurality of learning domain states,

at least one learning domain action,

at least one domain learning domain state transition,

and at least one learning domain observation;

determining an initial user state of the user;

determining an initial user action from the at least one learning domain action with the initial learning model given the initial user state as the at least one learning domain state;

receiving a user observation of the user after the user executes the initial user action;

determining an updated user state with the initial learning model given the updated user observation; and

determining a subsequent user action from the at least one learning domain action.

2. The computer implemented method of claim 1 wherein:

the initial system model comprises a Partially Observable Markov Decision Process (POMDP); and

the at least one domain learning domain state transition comprises a learning domain state transition probability table of P (s′

|s, a) where s is the initial state, s′

is the updated user state and a is the at least one learning domain action.

3. The computer implemented method of claim 2 further comprising automatically determining the learning domain state transition probability table utilizing a Hidden Markov Model (HMM) function where an input to the HMM function comprises a probability that the initial user state will change to the updated user state, a probability that the learning domain observation is obtained while the state of user is the updated user state and a probability of the initial state of the user.

4. The computer implemented method of claim 3 wherein the HMM function comprises a state transition probability distribution, a conditional observation probability distribution, and a probability of the initial state of the user whereby the state transition probability distribution defines the at least one domain learning domain state transition for the POMDP given the at least one learning domain observation defined by the conditional observation probability distribution for the initial state of the user defined by the probability of the initial state of the user.

5. The computer implemented method of claim 1 further comprising defining the at least one learning domain state, the at least one learning domain action and the at least one learning domain observation from a learning domain ontology.

6. The computer implemented method of claim 5 wherein defining an initial learning model of a learning domain further comprises automatically determining the learning domain ontology from a learning domain data set comprising a plurality of learning domain problems and a plurality of learning domain observations.

7. The computer implemented method of claim 6 wherein:

automatically determining the learning domain ontology from a learning domain data set further comprises utilizing a Principal Component Analysis (PCA) algorithm whereby the input is the plurality of learning domain problems and the plurality of learning domain observations and the output is at least one of the plurality of learning domain states.

8. The computer implemented method of claim 7 wherein:

the initial system model comprises a Partially Observable Markov Decision Process (POMDP); and

the at least one of the plurality of learning domain states defines the plurality of learning domain states for the POMDP.

9. The computer implemented method of claim 1, wherein:

the at least one learning domain state comprises a competency measure; and

defining the initial learning model of a learning domain further comprises automatically determining the competency measure.

10. The computer implemented method of claim 9 wherein defining the initial learning model of a learning domain further comprises automatically determining the competency measure further comprises utilizing an Item Response Theory (IRT) algorithm comprising a conditional probability of getting the at least one item correct given a latent proficiency score of the user and a difficulty level of the at least one item.

11. The computer implemented method of claim 10 wherein:

the initial system model comprises a Partially Observable Markov Decision Process (POMDP); and

the conditional probability of getting the at least one item correct given a latent proficiency score of the user and a difficulty level of the at least one item defines the at least one learning domain observation for the POMDP.

12. The computer implemented method of claim 3 wherein:

automatically determining the learning domain ontology from a learning domain data set further comprises utilizing a Principal Component Analysis (PCA) algorithm whereby the input is a plurality of learning domain problems and a plurality of learning domain observations and the output is at least one of the plurality of learning domain states;

the at least one of the plurality of learning domain states defines the plurality of learning domain states for the POMDP;

defining the initial learning model of a learning domain further comprises automatically determining the competency measure further comprises utilizing an Item Response Theory (IRT) algorithm comprising a conditional probability of getting the at least one item correct given a latent proficiency score of the user and a difficulty level of the at least one item; and

the a conditional probability of getting the at least one item correct given a latent proficiency score of the user and a difficulty level of the at least one item defines the at least one learning domain observation for the POMDP.

13. A learning model system for determining an action for a user within a learning domain, the learning model system comprising a computer system including one or multiple processors configured to perform the functions of:

defining an initial learning model of a learning domain comprising;

a plurality of learning domain states,

at least one learning domain action,

at least one domain learning domain state transition,

and at least one learning domain observation;

determining an initial user state of the user;

determining an initial user action from the at least one learning domain action with the initial learning model given the initial user state as the at least one learning domain state;

receiving a user observation of the user after the user executes the initial user action;

determining an updated user state with the initial learning model given the updated user observation; and

determining a subsequent user action from the at least one learning domain action.

14. The learning model system of claim 13 wherein:

the initial system model comprises a Partially Observable Markov Decision Process (POMDP); and

the at least one domain learning domain state transition comprises a learning domain state transition probability table of P (s′

|s, a) where s is the initial state, s′

is the updated user state and a is the at least one learning domain action.

15. The learning model system of claim 14 wherein the one or more processors are further configured to perform the functions of automatically determining the learning domain state transition probability table utilizing a Hidden Markov Model (HMM) function where an input to the HMM function comprises a probability that the initial user state will change to the updated user state, a probability that the learning domain observation is obtained while the state of user is the updated user state and a probability of the initial state of the user.

16. The learning model system of claim 13 wherein:

the initial system model comprises a Partially Observable Markov Decision Process (POMDP);

the one or more processors are further configured to perform the functions of;

automatically determining the learning domain ontology from a learning domain data set utilizing a Principal Component Analysis (PCA) algorithm whereby the input is a plurality of learning domain problems and a plurality of learning domain observations and the output is at least one of the plurality of learning domain states; and

the at least one of the plurality of learning domain states defines the plurality of learning domain states for the POMDP.

17. The learning model system of claim 13 wherein:

the at least one learning domain state comprises a competency measure;

the one or more processors are further configured to perform the functions of;

defining the initial learning model of a learning domain further comprises automatically determining the competency measure utilizing an Item Response Theory (IRT) algorithm comprising a conditional probability of getting the at least one item correct given a latent proficiency score of the user and a difficulty level of the at least one item;

the initial system model comprises a Partially Observable Markov Decision Process (POMDP); and

the conditional probability of getting the at least one item correct given a latent proficiency score of the user and a difficulty level of the at least one item defines the at least one learning domain observation for the POMDP.

18. A computer program product for a learning model system comprising a non-transitory computer readable storage medium having a computer readable program code embodied therein, said computer readable program code configured to be executed to implement a method determining an action for a user within a learning domain, comprising:

defining an initial learning model of a learning domain comprising;

a plurality of learning domain states,

at least one learning domain action,

at least one domain learning domain state transition,

and at least one learning domain observation;

determining an initial user state of the user;

determining an initial user action from the at least one learning domain action with the initial learning model given the initial user state as the at least one learning domain state;

receiving a user observation of the user after the user executes the initial user action;

determining an updated user state with the initial learning model given the updated user observation; and

determining a subsequent user action from the at least one learning domain action.