USER STATE MODEL ADAPTATION THROUGH MACHINE DRIVEN LABELING
First Claim
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.
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Accused Products
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.
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Citations
25 Claims
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1. An apparatus to provide a computer-aided educational program, comprising:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. An apparatus to implement a personalized machine learning model comprising:
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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. - View Dependent Claims (12, 13, 14, 15, 16, 17)
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18. A method for computerized assisted learning, comprising:
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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. - View Dependent Claims (19, 20)
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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:
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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. - View Dependent Claims (22, 23, 24, 25)
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Specification