# US 10,373,279 B2 · Dynamic knowledge level adaptation of e-learning datagraph structures

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- Number: US 10,373,279 B2
- Document ID: US10373279B2
- Status: Granted
- Kind: B2
- Application number: 14/630,525
- Filed: 2015-02-24
- Published: 2015-08-27
- Granted: 2019-08-06
- Earliest claimed: 2014-02-24
- Assignees: Mindojo Ltd.
- Applicants: Mindojo Ltd.
- Inventors: Guy Zaslavsky; Andrei Bobkov
- Web page: https://patentgrep.com/patent/US10373279B2

## CPC

- G06Q 50/20: Education
- G06F 3/04842: Selection of displayed objects or displayed text elements (G06F3/0482 takes precedence)
- G06F 16/2228: {Indexing structures}
- G06F 16/248: Presentation of query results
- G06F 40/131: Fragmentation of text files, e.g. creating reusable text-blocks; Linking to fragments, e.g. using XInclude; Namespaces
- G06F 40/216: using statistical methods
- G06F 40/35: Discourse or dialogue representation
- G06N 5/022: Knowledge engineering; Knowledge acquisition
- G06Q 50/205: {Education administration or guidance}
- G09B 5/00: Electrically-operated educational appliances (working with questions and answers G09B7/00; simulators G09B9/00; advertising or displaying in general G09F)
- G09B 7/00: Electrically-operated teaching apparatus or devices working with questions and answers (mechanically operated G09B3/00; computing arrangements G06F)
- 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
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- 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)

## Abstract

Embodiments measure knowledge levels of students with respect to knowledge entities as they proceed through a course datagraph macrostructure, and dynamically adapt aspects of the macrostructure (and/or its embedded microstructures) to optimize knowledge acquisition of the students in accordance with their knowledge level. For example, a course consumption platform can parse the macrostructure and embedded microstructures to identify next microstructures to present to the student in such a way that dynamically adapts knowledge entities of the course to a student as a function of the student's present knowledge level associated with the student and difficulty levels of the presented microstructures. The platform can dynamically compute an updated knowledge level for the student throughout acquisition of the knowledge entity as a function of the student's responses to the microstructures, the student's present knowledge level at the time of the responses, and the difficulty levels of the presented microstructures.

## Claims

### Claim 1

A system for optimizing knowledge acquisition by students in an e-learning datagraph structure, the system comprising:
a non-transient course data store that stores a course datagraph macrostructure having a knowledge entity as a node of the course datagraph macrostructure, the knowledge entity having embedded therein:
a lesson datagraph microstructure comprising a plurality of lesson step objects, each linked with at least another of the lesson step objects by a respective lesson edge that defines a lesson flow relationship between the lesson step objects; and
a plurality of practice datagraph microstructures, each assigned a respective difficulty level, and each comprising a plurality of practice step objects, each practice step object linked with at least another of the practice step objects by a respective practice edge that defines a practice flow relationship between the practice step objects; and
a set of processors in communication with the course data store that implements a course consumption platform to receive consumption commands from a student, translate the consumption commands to executable datagraph commands, and execute the datagraph commands with the set of processors to:
identify a next practice datagraph microstructure to present to the student so as to adapt the knowledge entity of the course to the student as a function of a present knowledge level associated with the student and as a function of the respective difficulty levels of the plurality of practice datagraph microstructures;
receive response data from the student in response to displaying the next practice datagraph microstructure to the student;
calculate an updated knowledge level for the student as a function of the response data, the present knowledge level associated with the student, and the difficulty level of the next practice datagraph microstructure, wherein the set of processors dynamically reduce an effect by the response data on the updated knowledge level in conjunction with an increase in a difference between the present knowledge level and the respective difficulty level of the next practice datagraph microstructure from which the response data was obtained, wherein the set of processors dynamically increase an effect by the response data on the updated knowledge level in conjunction with a decrease in the difference between the present knowledge level and the respective difficulty level of the next practice datagraph microstructure from which the response data was obtained; and
set the updated knowledge level as the present knowledge level for the student after the set of processors perform the calculating, wherein iteration of the identifying, receiving, calculating and setting by the set of processors increases acquisition of knowledge associated with the knowledge entity.

### Claim 2 (depends on 1)

The system of claim 1, wherein the set of processors executes the datagraph commands with the processor further to:
iterate the identifying, receiving, calculating and setting until either the updated knowledge level for the student reaches a target knowledge level stored in association with the knowledge entity or all the plurality of practice datagraph microstructures of the knowledge entity are consumed.

### Claim 3 (depends on 2)

The system of claim 2, wherein:
the next practice datagraph microstructure is consumed when the response data is received from the student in response to displaying the next practice datagraph microstructure.

### Claim 4 (depends on 1)

The system of claim 1, wherein the set of processors assign the respective difficulty levels to the practice datagraph microstructures by:
generating a response dataset that associates a plurality of students with previous response data received for each of the practice datagraph microstructures from the plurality of students;
setting a prior difficulty level for each practice datagraph microstructure and setting a prior knowledge level for each of the plurality of students;
calculating an updated difficulty level for each practice datagraph microstructure and an updated knowledge level for each of the plurality of students as a function of the previous response data, the prior difficulty levels for the practice datagraph microstructures, and the prior knowledge levels for the plurality of students; and determining whether the updated difficulty level for each practice datagraph microstructure differs from the prior difficulty level for each practice datagraph microstructure by less than a pre-determined threshold amount, wherein:
when an answer to the determining is positive, the set of processors assign the updated difficulty levels to the practice datagraph microstructures as their respective difficulty levels; or
when an answer to the determining is negative, the set of processors set the updated difficulty level for each practice datagraph microstructure as the prior difficulty level, set the updated knowledge level for each of the plurality of students as the prior knowledge level, and return to the calculating step.

### Claim 5 (depends on 1)

The system of claim 1, wherein:
the knowledge entity is a first of a plurality of knowledge entities in the course datagraph macrostructure; and
each knowledge entity is linked with at least another of the knowledge entities by a respective knowledge edge that defines a course flow relationship between the knowledge entities.

### Claim 6 (depends on 5)

The system of claim 5, wherein:
an initial knowledge level of the student for the first knowledge entity is determined as least partially according to the student's prior consumption of at least another of the plurality of knowledge entities in the course datagraph macrostructure that is logically related to the present knowledge entity.

### Claim 7 (depends on 1)

The system of claim 1, wherein the course data store is disposed in a first computational environment remote from the student.

### Claim 8 (depends on 7)

The system of claim 7, wherein at least one of the set of processors is disposed in a second computational environment local to the student, and the first and second computational environments are in communication over a communications network.

### Claim 9 (depends on 1)

The system of claim 1, wherein:
the course consumption platform comprises a graphical user interface that receives the consumption commands from the student and translates the consumption commands to datagraph commands for execution by the set of processors; and
the set of processors receives the response data from the student in response to displaying the next practice datagraph microstructure to the student via a graphical user interface of the course consumption platform.

### Claim 10 (depends on 1)

The system of claim 1, wherein the course datagraph macrostructure, the lesson datagraph microstructure, and the practice datagraph microstructures are each stored as directed graph structures.

### Claim 11 (depends on 4)

The system of claim 4, wherein the prior difficulty level is an initial difficulty level, and wherein the prior knowledge level is an initial knowledge level.