USER STATE MODEL ADAPTATION THROUGH MACHINE DRIVEN LABELING

• US 20170169715A1
• Filed: 12/09/2015
• Published: 06/15/2017
• Est. Priority Date: 12/09/2015
• Status: Abandoned Application

• Current Assignee: Intel Corporation
• Original Assignee: David Stanhill, Eyal Rond, Sinem Aslan, Vladimir Shlain
• Inventors: ALYUZ CIVITCI, NESE, OKUR, EDA, ARSLAN ESME, ASLI, ASLAN, SINEM, OKTAY, ECE, METE, SINEM E., UNLU, HASAN, STANHILL, DAVID, SHLAIN, VLADIMIR, ABRAMOVITCH, PINI, ROND, EYAL
• Application Number:
  US14/964,458
• Publication Number:
  US 20170169715A1
• Time in Patent Office: Days
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 20/20 Ensemble learning
• CPC Class Codes: G09B 5/06 with both visual and audible presentation of the material to be studied

Abstract

Embodiments herein relate to generating a personalized model using a machine learning process, identifying a learning engagement state of a learner based at least in part on the personalized model, and tailoring computerized provision of an educational program to the learner based on the learning engagement state. An apparatus to provide a computer-aided educational program may include one or more processors operating modules that may receive indications of interactions of a learner and indications of physical responses of the learner, generate a personalized model using a machine learning process based at least in part on the interactions of the learner and the indications of physical responses of the learner during a calibration time period, and identify a current learning state of the learner based at least in part on the personalized model during a usage time period. Other embodiments may be described and/or claimed.

Claims

1. An apparatus to provide a computer-aided educational program, comprising:

one or more processors;

a receive module, to be operated on the one or more processors, to receive indications of interactions of a learner with the educational program and to receive indications of physical responses of the learner collected substantially simultaneously as the learner interacts with the educational program;

a calibration module, to be operated on the one or more processors, to generate a personalized model using a machine learning process based at least in part on the interactions of the learner and the indications of physical responses of the learner during a calibration time period; and

a learning state identification module, to be operated on the one or more processors, to identify a current learning state of the learner based at least in part on the personalized model and the indications of physical responses of the learner during a usage time period,

wherein the current learning state of the learner is used to tailor computerized provision of the education program during the usage time period.

2. The apparatus of claim 1, wherein the machine learning process includes using a random forest technique.

3. The apparatus of claim 1, wherein the machine learning process includes training a support vector machine.

4. The apparatus of claim 1, wherein the calibration module is to generate the personalized model by retraining an appearance classifier of a generic model having the appearance classifier and one or more of a performance classifier or a context classifier.

5. The apparatus of claim 4, wherein the generic model includes a performance classifier and a context classifier.

6. The apparatus of claim 5, wherein the calibration module is to determine a learning state label based at least in part on one or more of the performance classifier or the context classifier, and retrain the appearance classifier based at least in part on the learning state label.

7. The apparatus of claim 6, wherein the learning state identification module is to identify the current learning state based at least in part on the retrained appearance classifier.

8. The apparatus of claim 5, wherein the receive module is also to receive indications of learning context, and the calibration module is to determine a learning state label based at least in part on the indications of learning context and the context classifier, and wherein the calibration module is to retrain the appearance classifier based at least in part on the learning state label.

9. The apparatus of claim 1, wherein the indications of physical responses of the learner during the calibration period include at least one of an image or video of the learner and the indications of physical responses of the learner during the usage time period include at least one of an image or video of the learner.

10. The apparatus of claim 1, wherein the current learning state of the learner is at least one of a behavioral state or an emotional state.

11. An apparatus to implement a personalized machine learning model comprising:

one or more processors;

a receive module, to be operated on the one or more processors, to;

receive indications of interactions of a learner with an educational program;

receive indications of physical responses of the learner collected substantially simultaneously as the learner interacts with the educational program; and

receive a request for a current learning state of the learner during a usage time period;

a machine learning model training module, to be operated on the one or more processors, to generate the personalized machine learning model based upon the received indications of interactions and the received indications of physical responses during a calibration time period;

an output module, to be operated on the one or more processors, to;

in response to the received request, determine a current learning state from the personalized machine learning model and the indications of physical responses during the usage time period; and

output the determined current learning state.

12. The apparatus of claim 11, wherein the machine learning model training module is to generate the personalized machine learning model using a random forest technique.

13. The apparatus of claim 11, wherein the machine learning model training module is to generate the personalized machine learning model using a support vector machine.

14. The apparatus of claim 11, wherein the machine learning model training module is to generate the personalized machine learning model by retraining an appearance classifier of a generic machine learning model having the appearance classifier and a performance classifier.

15. The apparatus of claim 14, wherein the generic machine learning model also includes a context classifier.

16. The apparatus of claim 15, wherein the personalized machine learning model is a merged model including the retrained appearance classifier, the performance classifier, and the context classifier.

17. The apparatus of claim 11, wherein the output module is further to determine a confidence level for the determined learning state using the personalized machine learning model and output the confidence level.

18. A method for computerized assisted learning, comprising:

receiving, by a learning state engine operating on a computing system, indications of interactions of a learner with a computerized educational program presented through an educational device;

receiving, by the learning state engine, indications of physical responses of the learner collected substantially simultaneously as the learner is interacting with the educational program;

generating, by the learning state engine, a personalized model using a machine learning process by retraining an appearance classifier of a generic model based at least in part on the indications of interactions and the indications of physical responses during a calibration time period;

identifying, by the learning state engine, a current learning state of the learner, based at least in part on the personalized model and the indications of physical responses during a usage time period; and

outputting, by the learning state engine, the current learning state of the learner, wherein the current learning state of the learner is used to tailor computerized provision of the education program.

19. The method of claim 18, wherein generating the personalized model includes generating the personalized model using at least one of a random forest technique or a support vector machine.

20. The method of claim 18, wherein generating the personalized model includes:

generating a learning state label using at least one of a context or a performance classifier of the generic model in response to a confidence level of an initial label assigned by an appearance classifier is below a predefined threshold value; and

retraining the appearance classifier based at least in part on the learning state label.

21. One or more computer-readable media comprising instructions that cause a computing device, in response to execution of the instructions by the computing device, to:

receive indications of interactions of a learner with a computerized educational program presented through an educational device;

receive indications of physical responses of the learner collected substantially simultaneously as the learner is interacting with the educational program;

generate a personalized model using a machine learning process by retraining an appearance classifier of a generic model based at least in part on the indications of interactions and the indications of physical responses during a calibration time period;

identify a current learning state of the learner, based at least in part on the indications of physical responses and the personalized model during a usage time period; and

output the current learning state of the learner, wherein the current learning state of the learner is used to tailor computerized provision of the education program.

22. The computer-readable media of claim 21, wherein the computing device is further caused to generate a learning state label using at least one of a context classifier or a performance classifier of the generic model during the calibration time period;

and generate the personalized model based at least in part on the learning state label.

23. The computer-readable media of claim 22, wherein the computing device is caused to generate the learning state label using at least one of the context classifier or the performance classifier of the generic model in response to a confidence level of an initial label assigned by the appearance classifier is below a predefined threshold value.

24. The computer-readable media of claim 21, wherein the computing device is caused to generate the personalized model using at least one of a random forest technique or a support vector machine.

25. The computer-readable media of claim 21, wherein the computing device is further caused to receive indications of learning context and generate the personalized model based at least in part on the indications of learning context received during the calibration period.