Predictive recommendation engine

• US 10,290,223 B2
• Filed: 05/26/2017
• Issued: 05/14/2019
• Est. Priority Date: 10/31/2014
• Status: Active Grant

• Current Assignee: Pearson Education Incorporated (Pearson plc)
• Original Assignee: Pearson Education Incorporated (Pearson plc)
• Inventors: Rho, Yun Jin, Anderson, Jacob M., Kukartsev, Gennadiy A.
• Primary Examiner(s): Wong, Linda
• Application Number:
  US15/607,205
• Publication Number:
  US 20170330474A1
• Time in Patent Office: 718 Days
• Field of Search: 375219, 375220, 375221, 375222, 375224, 375240, 37524026-24027, 375284, 375285, 375278, 375295, 375316, 375324, 375340, 375346, 375347, 375354, 375356, 375211, 375213, 375147, 375148, 375358, 434322, 434350, 434362, 434323, 434236, 434327, 434346, 434351, 434353, 434365, 705 10, 705 7, 705769, 705E17014, 705 11, 705 52
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 50/20 Education
• CPC Class Codes: G09B 5/065 Combinations of audio and video presentations, e.g. videotapes, videodiscs, television systems
• CPC Class Codes: G09B 7/00 Electrically-operated teaching apparatus or devices working with questions and answers mechanically operated G09B3/00; computing arrangements G06F
• 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
• CPC Class Codes: G09B 7/07 providing for individual presentation of questions to a plurality of student stations
• CPC Class Codes: H04B 3/544 Setting up communications; Call and signalling arrangements
• CPC Class Codes: H04L 41/069 using logs of notifications; Post-processing of notifications
• CPC Class Codes: H04L 63/0272 Virtual private networks
• CPC Class Codes: Y04S 10/50 Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

Computer processes, systems and methods for alerting a student device when an objective is mastered according to a piecewise Gaussian distribution updated according to a Bayesian method are disclosed herein. The system can include a student device having a network interface to exchange data with a server via a communication network, and an I/O subsystem to convert electrical signals to user interpretable outputs user interface. The system can include a server that can: receive a student identification; retrieve the next learning objective; determine the difficulty level of the next objective problem set; and determine the probability of the student correctly answering the problems in the problem set. The system may also include a teacher device.

Claims

1. A system for alerting a student device when a learning objective is mastered according to a piecewise Gaussian distribution updated according to a Bayesian method, the system comprising:

a student device;

a server connected to the student device over a network and configured to;

receive a student identification from the student device identifying a student using the student device;

retrieve student attribute data, wherein the student attribute data comprises a piecewise Gaussian distribution model of a student skill level and a student error level;

identify an uncompleted objective, wherein the objective comprises a plurality of assessment data packets;

retrieve a difficulty level for the plurality of assessment data packets in the objective;

estimate a probability of the student overcoming each of the plurality of assessment data packets with a probabilistic model and using;

the difficulty level of the assessment data packets; and

the student skill level; and

identify that the probability exceeds a pre-determined threshold;

update the student attribute data according to a Bayesian method to produce updated attribute data, wherein the updated attribute data updates the piecewise Gaussian distribution model; and

generate and provide an alert to the student device and/or a third-party device indicating mastery of the objective, wherein the alert comprises a code to direct the student device to provide an indicator of the alert.

2. The system for alerting the student device when the learning objective is mastered according to a piecewise Gaussian distribution updated according to a Bayesian method of claim 1, wherein the probabilistic model is an Item Response Theory model.

3. The system for alerting the student device when the learning objective is mastered according to a piecewise Gaussian distribution updated according to a Bayesian method of claim 1, wherein the difficulty level is based on a Gaussian distribution model of a difficulty of the plurality of assessment data packets.

4. The system for alerting the student device when the Beaming objective is mastered according to a piecewise Gaussian distribution updated according to a Bayesian method of claim 1, wherein the server is further configured to determine the student skill level by determining a mode of the piecewise Gaussian distribution.

5. The system for alerting the student device when the learning objective is mastered according to a piecewise Gaussian distribution updated according to a Bayesian method of claim 1, wherein the server is further configured to determine the difficulty level by determining a mode of the piecewise Gaussian distribution.

6. The system for alerting the student device when the learning objective is mastered according to a piecewise Gaussian distribution updated according to a Bayesian method of claim 1, wherein:

the third-party device is a teacher device, wherein the teacher device is connected to the server.

7. The system for alerting the student device when the learning objective is mastered according to a piecewise Gaussian distribution updated according to a Bayesian method of claim 1, wherein an indicator of the alert comprises one:

an aural indicator;

a tactile indicator; and

a visual indicator.

8. A processor-based method for alerting a student device when a learning objective is mastered according to a piecewise Gaussian distribution updated according to a Bayesian method, the method comprising:

connecting a student device to a server connected over a network;

receiving, by the server, a student identification from the student device identifying a student using the student device;

retrieving, by the server, student attribute data, wherein tire student attribute data comprises a piecewise Gaussian distribution model of a student skill level and a student error level;

identifying, by the server, an uncompleted objective, wherein the objective comprises a plurality of assessment data packet;

retrieving, by the server, a set difficulty level the plurality of assessment data packets in the objective;

estimating, by the server, a probability of the student overcoming each of the plurality of assessment data packets with a probabilistic model and using;

the difficulty level of the assessment data packets; and

the student skill level; and

identifying, by the server, that the probability exceeds a pre-determined threshold;

updating, by the server, the student attribute data according to a Bayesian method to produce updated attribute data, wherein the updated attribute data updates the piecewise Gaussian distribution model; and

generating and providing, by the server, an alert to the student device and/or a third-party device indicating mastery of the objective, wherein the alert comprises a code to direct the student device to provide an indicator of the alert.

9. The method of claim 8, wherein the probabilistic model is an Item Response Theory model.

10. The method of claim 8, wherein the difficulty level is based on a Gaussian distribution model of a difficulty of the plurality of assessment data packets.

11. The method of claim 8, further comprising determining the student skill level by determining a mode of the piecewise Gaussian distribution.

12. The method of claim 8, further comprising determining the difficulty level by determining a mode of the piecewise Gaussian distribution.

13. The method of claim 8, wherein the third-party device is a teacher device, and further comprising:

generating and providing the alert to the teacher device connected to the server over the network.

14. The method of claim 8, wherein an indicator of the alert comprises one:

an aural indicator;

a tactile indicator, and a visual indicator.

15. One or more non-transitory tangible computer-readable storage media storing computer-executable instructions for performing a computer process on a computing system for alerting a student device when a learning objective is mastered according to a piecewise Gaussian distribution updated according to a Bayesian method, the computer process comprising:

connecting a student device to a server connected over a network;

receiving, by the server, a student identification from the student device identifying a student using the student device;

retrieving, by the server, student attribute data, wherein the student attribute data comprises a piecewise Gaussian distribution model of a student skill level and a student error level;

identifying, by the server, an uncompleted objective, wherein the objective comprises a plurality of assessment data packet;

retrieving, by the server, a difficulty level the plurality of assessment data packets in the objective;

estimating, by the server, a probability of the student overcoming each of the plurality of assessment data packets with a probabilistic model and using;

the difficulty level of the assessment data packets; and

the student skill level; and

identifying, by the server, that the probability exceeds a pre-determined threshold;

updating, by the server, the student attribute data according to a Bayesian method to produce updated attribute data, wherein the updated attribute data updates the piecewise Gaussian distribution model; and

generating and providing, by the server, an alert to the student device and/or a third-party device indicating mastery of the objective, wherein the alert comprises a code to direct the student device to provide an indicator of the alert.

16. The computer process of claim 15, wherein the probabilistic model is an Item Response Theory model.

17. The computer process of claim 15, wherein the difficulty level is based on a Gaussian distribution model of a difficulty of the plurality of assessment data packets.

18. The computer process of claim 15, further comprising determining the student skill level by determining a mode of the piecewise Gaussian distribution.

19. The computer process of claim 15, further comprising determining the difficulty level by determining a mode of the piecewise Gaussian distribution.

20. The computer process of claim 15, wherein the third-party device is a teacher device and further comprising:

generating and providing the alert to the teacher device connected to the server over the network.