# US 11,113,616 B2 · Systems and methods for automated bayesian-network based mastery determination

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- Number: US 11,113,616 B2
- Document ID: US11113616B2
- Status: Granted
- Kind: B2
- Application number: 15/895,951
- Filed: 2018-02-13
- Published: 2018-08-16
- Granted: 2021-09-07
- Earliest claimed: 2017-02-13
- Assignees: Pearson Education, Inc.
- Applicants: Pearson Education, Inc.
- Inventors: Mark Potter; Kimberly Runyon; Laura Pionek
- Web page: https://patentgrep.com/patent/US11113616B2

## CPC

- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06F 3/04842: Selection of displayed objects or displayed text elements (G06F3/0482 takes precedence)
- G06F 3/04847: Interaction techniques to control parameter settings, e.g. interaction with sliders or dials
- G06F 9/453: {Help systems}
- G06F 11/3006: {where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems (multiprogramming arrangements G06F9/46; allocation of resources G06F9/50)}
- G06F 11/3438: {monitoring of user actions (tracking the activity of the user H04L67/535)}
- G06F 11/3447: {Performance evaluation by modeling}
- G06F 16/43: Querying
- G06F 16/435: Filtering based on additional data, e.g. user or group profiles
- G06F 16/9535: Search customisation based on user profiles and personalisation
- G06F 40/205: Parsing
- G06F 40/30: Semantic analysis
- G06N 5/02: Knowledge representation; Symbolic representation
- G09B 5/02: with visual presentation of the material to be studied, e.g. using film strip
- H04L 51/02: using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages
- H04L 51/216: Handling conversation history, e.g. grouping of messages in sessions or threads
- H04L 67/535: {Tracking the activity of the user (network monitoring arrangements H04L43/00; recording of computer activity G06F11/34)}
- G06N 5/027: {Frames}
- H04L 67/10: in which an application is distributed across nodes in the network (software deployment G06F8/60; multiprogramming arrangements G06F9/46)
- H04L 67/306: User profiles
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G09B: EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS (devices for psychotechnics or for testing reaction times A61B5/16; games, sports, amusements A63; projectors, projector screens G03B)
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)

## Abstract

Systems and methods for determining mastery in a Bayesian network are disclosed herein. The system can include memory including a content library database containing content for delivery to a user. The system can include at least one processor that can receive an assertion from a user device and identify one or several nodes relevant to the received assertion. The at least one processor can further evaluate the assertion and calculate a node mastery probability for the identified one or several relevant nodes. The at least one processor can calculate mastery of related nodes and determine mastery of an objective based on the mastery of the relevant nodes and the related nodes. The at least one processor can generate a mastery bar and update the mastery bar with the determined mastery of the objective.

## Claims

### Claim 1

A system for determining mastery in a Bayesian network, the system comprising:
memory comprising: a content library database comprising content for delivery to a user; and
at least one processor configured to:
receive an assertion, comprising a response to a prompt or a question, from a user device;
identify one or several nodes relevant to the received assertion;
evaluate the assertion;
calculate a node mastery probability for the identified one or several relevant nodes;
identify related nodes, comprising a plurality of connected nodes connecting, either directly or via at least one additional node, to a common learning objective, wherein the related nodes are connected to the relevant nodes via conditional dependencies, which identify the likelihood of the relevant nodes being mastered if the related nodes are likewise mastered or unmastered;
calculate mastery of the related nodes according to the node mastery probability and the conditional dependencies;
determine mastery of an objective based on the mastery of the relevant nodes and the related nodes;
generate a mastery bar in a user interface, wherein the mastery bar provides a visual indicator of progress through content and a visual indicator of mastery of the objective; and
update the mastery bar with the determined mastery of the objective.

### Claim 2 (depends on 1)

The system of claim 1, wherein the at least one processor is further configured to parse the received assertion.

### Claim 3 (depends on 2)

The system of claim 2, wherein the parsing of the received assertion comprises natural language processing of the received assertion.

### Claim 4 (depends on 3)

The system of claim 3, wherein the at least one processor is further configured to select a node and determine mastery of the selected node.

### Claim 5 (depends on 4)

The system of claim 4, wherein the at least one processor is further configured to generate and provide supplemental prompts when mastery of the selected node is not determined.

### Claim 6 (depends on 5)

The system of claim 5, wherein determining mastery of an objective based on the mastery of the relevant nodes and the related nodes comprises: retrieving a mastery threshold; and comparing an attribute of the objective to a mastery threshold.

### Claim 7 (depends on 5)

The system of claim 5, wherein determining mastery of an objective based on the mastery of the relevant nodes and the related nodes comprises:
determining mastery of the identified relevant nodes;
determining a total number of nodes relating to an objective;
determining a percent of the total number of nodes that are mastered; and
comparing the percent of the total number of nodes that are mastered to a mastery threshold.

### Claim 8 (depends on 7)

The system of claim 7, wherein updating the mastery bar comprises:
determining a present state of the mastery bar; determining a change in a mastery level; and
controlling pixels in a display to reflect the change in mastery level.

### Claim 9 (depends on 8)

The system of claim 8, wherein the mastery bar comprises a first portion quantifying unstarted nodes, a second portions quantifying started but unmastered nodes, and a third portion quantifying mastered nodes.

### Claim 10 (depends on 9)

The system of claim 9, wherein the at least one processor is further configured to:
provide content to the user;
receive a user response to the provided content;
determine to intervene based on an evaluation of the user response to the provided content; and
provide a prompt to the user via the user interface, wherein the assertion is received in response to the provided prompt.

### Claim 11

A method of determining mastery in a Bayesian network, the method comprising:
receiving an assertion, comprising a response to a prompt or a question, from a user device;
identifying one or several nodes relevant to the received assertion;
evaluating the assertion;
calculating a node mastery probability for the identified one or several relevant nodes;
identifying related nodes, comprising a plurality of connected nodes connecting, either directly or via at least one additional node, to a common learning objective, wherein the related nodes are connected to the relevant nodes via conditional dependencies, which identify the likelihood of the relevant nodes being mastered if the related nodes are likewise mastered or unmastered;
calculating mastery of the related nodes according to the node mastery probability and the conditional dependencies;
determining mastery of an objective based on the mastery of the relevant nodes and the related nodes;
generating a mastery bar in a user interface, wherein the mastery bar provides a visual indicator of progress through content and a visual indicator of mastery of the objective; and
updating the mastery bar with the determined mastery of the objective.

### Claim 12 (depends on 11)

The method of claim 11, further comprising parsing the received assertion.

### Claim 13 (depends on 12)

The method of claim 12, wherein the parsing of the received assertion comprises natural language processing of the received assertion.

### Claim 14 (depends on 13)

The method of claim 13, further comprising selecting a node and determine mastery of the selected node.

### Claim 15 (depends on 14)

The method of claim 14, further comprising generating and providing supplemental prompts when mastery of the selected node is not determined.

### Claim 16 (depends on 15)

The method of claim 15, wherein determining mastery of an objective based on the mastery of the relevant nodes and the related nodes comprises: retrieving a mastery threshold; and comparing an attribute of the objective to a mastery threshold.

### Claim 17 (depends on 15)

The method of claim 15, wherein determining mastery of an objective based on the mastery of the relevant nodes and the related nodes comprises:
determining mastery of the identified relevant nodes;
determining a total number of nodes relating to an objective;
determining a percent of the total number of nodes that are mastered; and
comparing the percent of the total number of nodes that are mastered to a mastery threshold.

### Claim 18 (depends on 17)

The method of claim 17, wherein updating the mastery bar comprises:
determining a present state of the mastery bar; determining a change in a mastery level; and
controlling pixels in a display to reflect the change in mastery level.

### Claim 19 (depends on 18)

The method of claim 18, wherein the mastery bar comprises a first portion quantifying unstarted nodes, a second portions quantifying started but unmastered nodes, and a third portion quantifying mastered nodes.

### Claim 20 (depends on 19)

The method of claim 19, further comprising:
providing content to the user;
receiving a user response to the provided content;
determining to intervene based on an evaluation of the user response to the provided content; and
providing a prompt to the user via the user interface, wherein the assertion is received in response to the provided prompt.