Predictive recommendation engine
First Claim
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.
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Accused Products
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.
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Citations
20 Claims
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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:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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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:
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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. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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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:
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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. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification