Note: Descriptions are shown in the official language in which they were submitted.
SYSTEM AND METHOD FOR CENTRALLY MANAGING ADAPTIVE
LEARNING ACROSS MULTIPLE DISTRIBUTED E-LEARNING
EXPERIENCES
FIELD
[0001] The present disclosure relates to methods and systems for e-
learning.
BACKGROUND
[0002] Generating and providing truly portable and accessible online
content is a
challenge in the e-learning industry. There exist standards for development of
accessible
e-learning, such as the World Wide Web Consortium's (W3C) Web Content
Accessibility
Guidelines (WCAG 2.0), Authoring Tool Accessibility Guidelines (ATAG 2.0), and
Accessible Rich Internet Application (ARIA 1.0) specification. However, these
standards
are deficient in addressing the challenges associated with accessible content
development,
especially the economic challenges with developing online content that fully
meets the
needs of the students or learners not just in e-learning, but in all areas of
online content
management.
[0003] Several solutions have been proposed to tackle this problem in
the four sectors
in the educational content space, such as learning management systems (LMS),
online,
authoring tools/content management systems, education providers, and learning
content
management systems (LCMS). Generally, learning management systems are closed
environments where students study, are assessed, and actively interact with
one another.
These systems are analogous to online classrooms. Example products include
Blackboard from Anthology Inc., U.S.A., Instructure0 by Canvas Inc., U.S.A.,
Schoology0 from PowerSchool Group LLC, U.S.A., Brightspace0 from D2L
Corporation, Canada (and open-source solutions like Moodie and Sakai . Online
education providers are also closed ecosystems of independent learning, and
example
products include Pluralsight Skills , LinkedIn Learning, Coursera0, Udemy0,
Udacity0, and Masterclass0. Authoring tools or content management systems
comprise
software and services that authors, such as web developers, designers,
writers, etc., can
use to produce and manage web content e.g., static web pages, dynamic web
applications,
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for websites, blogs, media, and ecommerce-style sites. Example products of
authoring
tools or content management systems include Shopify0, Wix0, Weebly0, Progress
Sitefinity0, Squarespace0, and Open-Source solutions e.g. WordPress and
Drupa10.
Learning content management systems are used to develop course content for
either
distribution to LMSes or direct consumption directly by students or learners.
Example
LMS products include Top Hate, Articulate , Turning , and Lectora0.
[0004] With respect to the authoring tools, accessibility support in
the market today
is focused on functionality that is best reflected by the W3C's Authoring
Tools
Accessibility Guidelines (ATAG) which define how to make the authoring tools
themselves accessible, such that people with disabilities can create web
content, and help
authors create more accessible web content, specifically: enable, support, and
promote
the production of content that conforms to WCAG. These guidelines reflect an
approach
to content contained in these tools that assumes that the consumer will
consume web
content; and that the primary consumer of the content is an enabled person and
that the
point of the guidelines are to make the authoring tool provide a minimal level
of
accessibility support for disabled persons looking to consume this enabled
person
intended content.
[0005] At present, the market expectation for content development is
designed to
gracefully degrade the content experience, such that the most enabled
consumers
experience the content as intended, and the experience degrades for less
enabled
consumers in such a manner as it is designed to minimize the breaking of the
experience
for the less enabled consumers. For example, a content producer will for
example record
and make available a video which is best-suited to or provides the richest
experience to
fully enabled consumers i.e. those without disabilities, then add closed
captioning and/or
described video etc. to express what is in the video for the visually
impaired. The
challenge here is that from a learning perspective the nuance of the actual
learning is
designed around a visual experience that often does not translate fully to the
transcription
as the language prioritizes the description of the video content instead of
the learning
concepts. This methodology applies to most content that is produced by these
existing
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systems whereby the primary intended audience is a fully enabled user with a
desktop
computer or a tablet such that fully enabled user enjoys the best experience,
while the
experience is degraded for individuals with accessibility, equity, or device
challenges.
[0006] As such, none of the products on the market today provide for
the broader
portability and accessibility needs of e-learning content producers (course
publishers,
educational institutions, and governments / educational ministries) to develop
and
manage content effectively from an accessibility-first perspective while also
providing
rich, highly engaged interactive experiences for fully enabled users.
[0007] Furthermore, the ability to effectively build e-learning
content that is adaptive
learning-enabled and adaptive teaching-enabled is historically challenged by
the ability
of content authors to effectively manage the metadata tagging and knowledge
graph
management requirements for organizing that content. The present technology
space
does not address or even attempt to address this problem. There exist some
adaptive
learning products, however, most work in the space outside of proprietary
closed content
ecosystems (e.g., Coursera'TM, Khan Academy', etc. type organizations) is
related to
three areas: tutoring platforms, chat bots, and automated assessment engines.
Furthermore, academia has been focused and has shown great effectiveness on
researching and developing the effectiveness of learning algorithms as opposed
to the
content production and organization. There are no known content development
platforms
designed explicitly to simplify the data classification problem of adaptive
learning by
organizing the content into knowledge components (learning topics) in an
authoring
experience. For example, adaptive learning exists in tutoring platforms today
such as
ALEKSTm and Knewton", as well as in CMSes such as SmartSparrow' and Top Hat'.
However, these products do not provide the unique functionality to solve for
the content
classification and authorship challenges to make adaptive learning content
development
effective. Additionally, no products are explicitly designed to leverage multi-
client
scaling through the universal classification and shared ontology this content
classification
represents as a means of standardizing and streamline the effectiveness of
adaptive
learning algorithms.
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[0008] Generally, adaptive learning and adaptive teaching have not
been
implemented successfully, except in limited closed environments in the
education sector,
largely because the content is being only organized into non-discreet
elements, such as
lessons, that contain numerous learning topics within them. The inability to
easily
modularize content into labelled sections that reflect these discrete learning
topics has
resulted in either unmanageable requirements for the effort to support the
required content
data classification that adaptive learning algorithms depend on, or the over
dependence
of automated classification systems to achieve this.
SUMMARY
[0009] In one of its aspects, a system configured for at least one
content producer to
generate adaptive e-learning experiences for a plurality of learners, the
system
comprising:
at least one memory device configured for storing instructions; and
at least one processor coupled to the at least one memory device and
configured
to execute the instructions to at least:
access a graph database storing a learner knowledge graph comprising an
ontology for a plurality of learning topics;
develop content comprising at least one course material associated with
one of the plurality of learning topics, wherein the content comprises a
learning
section; and wherein the content is associated with at least one learner
experience type;
distribute the at least one course material in accordance with a selected e-
learning experience, wherein the content within each learning section is
tagged
with one of the plurality of learning topics suited for the e-learning
experience;
track learning events generated by the plurality of learner, wherein the
learning events comprise at least one of viewing and interaction activities
related to content consumption and learner validation activities;
generate learner event data from the learning events;
anonymize the learner event data;
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share the learner event data with another at least one content producer;
based on the learner event data, determine an effectiveness quotient of
the content for teaching the course material.
[0010] In another example, a method for generating adaptive e-
learning experiences
for a plurality of learners by at least one content producer, with a processor
coupled to at
least one memory device storing instructions executable by the processor to at
least
perform the operations of:
accessing a graph database storing a learner knowledge graph
comprising an ontology for a plurality of learning topics;
developing content comprising at least one course material associated
with one of the plurality of learning topics, wherein the content comprises a
learning section; and wherein the content is associated with at least one
learner
experience type;
distributing the at least one course material in accordance with a selected
e-learning experience, wherein the content within each learning section is
tagged with one of the plurality of learning topics suited for the e-learning
experience;
tracking learning events generated by the plurality of learner, wherein
the learning events comprise at least one of viewing and interaction
activities
related to content consumption and learner validation activities;
generating learner event data from the learning events;
anonymizing the learner event data;
sharing the learner event data with another at least one content producer;
based on the learner event data, determining an effectiveness quotient of
the content for teaching the course material.
[0011] In another example, at a content producer, a computer readable
medium
storing instructions executable by a processor to generate adaptive e-learning
experiences
for a plurality of learners, wherein the instructions carry out the operations
comprising:
Date Recue/Date Received 2023-11-17
accessing a graph database storing a learner knowledge graph
comprising an ontology for a plurality of learning topics;
developing content comprising at least one course material associated
with one of the plurality of learning topics, wherein the content comprises a
learning section; and wherein the content is associated with at least one
learner
experience type;
distributing the at least one course material in accordance with a selected
e-learning experience, wherein the content within each learning section is
tagged with one of the plurality of learning topics suited for the e-learning
experience;
tracking learning events generated by the plurality of learner, wherein
the learning events comprise at least one of viewing and interaction
activities
related to content consumption and learner validation activities;
generating learner event data from the learning events;
anonymizing the learner event data;
sharing the learner event data with at least another content producer;
based on the learner event data, determining an effectiveness quotient of
the content for teaching the course material.
[0012]
In another example, a method for generating adaptive e-learning experiences
for a plurality of learners by at least one content producer, comprising with
a processor
coupled to at least one memory device storing instructions executable by the
processor
to at least perform the operations of:
associating each of the e-learning experiences with a learning topic
comprising
at least one adaptive learning variable;
executing a first set of instructions to train an adaptive learning model on a
schedule or on-demand;
inputting anonymized learner event data, scoring data and learner knowledge
graph from the at least one content producer as training data;
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training the adaptive learning model with the training data to optimize the at
least one adaptive learning variable; and
updating the learner knowledge graph with the outputted optimized at least one
adaptive learning variable.
[0013] Advantageously, methods and systems are provided for building
e-learning
content that are adaptive learning-enabled and adaptive teaching-enabled by
allowing
content authors to effectively manage the metadata tagging and knowledge graph
management requirements for organizing the content in a structure that enables
adaptive
learning. The methods and systems support the real-time data processes
necessary to
support active adaptive learning experiences and scheduled data processes for
handling
big data and machine learning processes that continuously improve the system's
effectiveness.
[0014] The systems and methods described herein allow for centrally
and
anonymously managing adaptive learning experiences across multiple learning
environments. Generally, e-learning experiences are associated with various
learning
environments such as, but not limited to, learning management systems, online
and
offline web browsers, mobile apps, or offline print. These experiences are
shared, and
users are anonymously managed through a centralized data store and system. The
systems and methods allow for the development and deployment of digital
experiences
across multiple clients and e-learning experiences that share the same
learning topic or
knowledge component. The authoring experience is designed to constrain and
enforce
the data classification of these learning topics to discrete and modular
sections of the
content. This constrained modularization of e-learning content enables shared
artificial
intelligence (AI) and/or machine learning (ML) benefits from the complete pool
of users
studying any materials of the system across multiple clients.
[0015] The systems and methods allow for creating learning content
that is composed
of mixed interactive and non-interactive experiences organized modularly
around
learning topics, which enables not only personalized learning experiences
(adaptive
learning) but also the capacity to repackage alternative learning experiences
rapidly and
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conveniently. For example, a unit of a Math 9 course may be focused on
financial literacy
with a lesson covering the four learning topics of inflation, deflation,
appreciation, and
depreciation, and the content may be developed as a single lesson, tagging the
appropriate
sections of content with their associated learning topics. On completion the
base
experience that the author was building against would be the lesson that
covers all four
learning topics. However, in view of the modularity of the content
classification, this
content may also now be repackaged and distributed as an experience where each
of the
four parts is sent separately to the student or learner, in the form of a
daily email message,
for example. As such, the method and system not only enable adaptability for
the
complexity and depth of the learning, but also the modality of the eLearning
content
consumption.
[0016] To simply and modularize this sectioning effort the systems
allow for a
minimum of two types of content sections that all content must be contained
within:
descriptive sections and labelled learning sections. Descriptive sections are
focused on
content not intended to provide direct learning outcomes, such as introduction
and
summary sections of a lesson and learning sections are for grouping content
into their
respective learning topics by requiring inclusion of an overriding section
learning topic
label that corresponds with learning topics that represent nodes on a
centrally shared
"knowledge tree".
[0017] Generally, the knowledge tree is intended to serve as the data
layer for a
shared service across all clients who will create content that is ready for
machine learning
activities that can benefit from the broader benefits of the large audiences
that are
anonymously shared across these clients. To enable this goal the ontology of
the
knowledge tree must be highly restrictive to ensure content produced by
different clients
on the same learning topic share the benefits of this cross-pollination.
Therefore, this
data is developed and stored in a graph database with an emphasis on upper
ontology
only, universally shared node structure, and highly restricted access to
modify and expand
the knowledge tree.
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[0018]
The systems and methods enable the generation of content that may be
consumed in various formats well beyond "web content" by decoupling the
content from
its presentation and providing a reliable means of exporting the content in
other output
formats. While the systems and methods promote inclusive design, the content
produced
is equally consumable by disabled and enabled persons, more emphasis is placed
on the
needs of disabled persons, who are viewed as the primary consumer from an
authorship
perspective. The difference in approach is best reflected in the latest ATAG
version at
the time of writing (2.0) recommendation B.2.3.1 "Alternative Content is
Editable
(WCAG)", where the guideline states that "if the authoring tool provides
functionality
for adding non-text content, then authors are able to modify programmatically
associated
text alternatives for non-text content".
The equivalent functionality for this
recommendation in the system presented herein would recite "if the authoring
tool
provides functionality for adding text content, then authors will be able to
modify
programmatically associated non-text alternatives for the text content". As an
example,
in the event that a video is to be added as part of the content, the system
expects text
content and provides the ability to include a video alternative to that text.
[0019]
Generally, the systems and methods allow for the development of portable and
deeply accessible online learning experiences that can simultaneously address
numerous
learning experiences from print experiences for users with device limitations,
screen
readers of students with accessible needs, internet limited or no internet
students with
computer or mobile devices, offline experiences for device and internet
enabled users
who prefer to save bandwidth, traditional means of consumption on variant
learning
management systems (Sharable Content Object Reference Model (SCORM)/xAPI), and
live hosted experiences across variant learning management systems (new
methodology).
As such, the content is stored in a "headless" format, where the content and
the
presentation are separated, in which the content details are saved as metadata
instead of
code. The content is then transcompiled and exported for publication in a
plurality of
formats to address the variable learning formats needed to tailor experiences
to
accessibility-first.
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[0020] The systems and methods described herein intentionally reverse
this paradigm
by designing the authoring experience that is primarily intended for the least
enabled
consumers first so that they can fully learn the material regardless of their
disabilities or
device limitations. The learning is then progressively enhanced with
interactivity such
that all users have the best learning experience to their inherent
capabilities. Of note, this
is not simply providing the faculty for alternative content, though aspects of
it would
apply, rather it is a complementary state of content where the content is
shared among
different experience states and edited in such a shared state. In other words,
the system
is not designed as a "print version" that is distinct from a "fully digital
version". Rather
it is designed to manage content in a hybrid state between the variant
experiences
supported such that e-learning content producers (course publishers,
educational
institutions, and governments/ educational ministries) can write and maintain
content that
facilitates numerous experiences and builds alternative content for more
enabled learners.
[0021] The method and system endeavors to address a significant
portion of the
economic barriers with producing content of this nature by drastically
reducing the
overhead and complexity and fundamentally shifting the approach to content
design.
BRIEF DESCRIPTION OF THE DRAWINGS
[0022] Figure la shows a tenant infrastructure for publication,
versioning,
distribution, and remote management of e-learning content for plurality of
learner
experience types;
[0023] Figure lb shows a top-level diagram of an overall system
architecture for
developing content for distribution in a plurality of learner experience
types;
[0024] Figure 2 shows a tenant real-time adaptive learning
infrastructure;
[0025] Figure 3 shows a flow chart outlining example steps for real-
time adaptive
learning;
[0026] Figure 4 shows a tenants scheduled adaptive learning model
training
infrastructure;
[0027] Figure 5 shows a flow chart outlining example steps for
training adaptive
learning models using the tenant real-time adaptive learning infrastructure;
Date Recue/Date Received 2023-11-17
[0028] Figure 6 shows a portion of an authoring canvas with prompts
for adding a
descriptive section and a learning section;
[0029] Figure 7 shows an example user-interface comprising an
authoring canvas;
[0030] Figure 8 shows a portion of an authoring canvas for selecting
a learning
topic within an ontology of a centrally shared knowledge graph;
[0031] Figure 9 shows a portion of an authoring canvas for adding
components to a
learning topic; and
[0032] Figure 10 shows a block diagram of an example of a machine
upon which
any one or more of the techniques (e.g., methodologies) discussed herein can
be
performed.
DETAILED DESCRIPTION
[0033] The following detailed description refers to the accompanying
drawings.
Wherever possible, the same reference numbers are used in the drawings and the
following description to refer to the same or similar elements. While
embodiments of the
disclosure may be described, modifications, adaptations, and other
implementations are
possible. For example, substitutions, additions, or modifications may be made
to the
elements illustrated in the drawings, and the methods described herein may be
modified
by substituting, reordering, or adding stages to the disclosed methods.
Accordingly, the
following detailed description does not limit the disclosure. Instead, the
proper scope of
the disclosure is defined by the appended claims.
[0034] Moreover, it should be appreciated that the particular
implementations shown
and described herein are illustrative of the invention and are not intended to
otherwise
limit the scope of the invention in any way. Indeed, for the sake of brevity,
certain sub-
components of the individual operating components, and other functional
aspects of the
systems may not be described in detail herein. Furthermore, the connecting
lines shown
in the various figures contained herein are intended to represent exemplary
functional
relationships and/or physical couplings between the various elements. It
should be noted
that many alternative or additional functional relationships or physical
connections may
be present in a practical system.
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[0035] Generally, methods and systems described herein pertain to a
software-as-a-
service (SaaS) application designed to address the publication, versioning,
distribution,
and remote management needs of e-learning content across multiple learning
environments. A SaaS provider provisions the application and platform to
customers or
"tenants" of the system. Within a tenant, numerous types of users of the
tenant's
applications (the applications designed to publish, version, distribute, and
manage the
content) are possible, additionally learners using content produced by the
system are also
restricted to that tenant except when explicit permission to share or move
primary data
ownership has been applied (such as when a learner transfers to a new school).
[0036] The systems and methods described herein are configurable to
build e-
learning experiences that share the same learning topic, also known as a
knowledge
component, among multiple clients and e-learning experiences. This collection
of
learning topics form a shared ontology (or dictionary) that enables all
tenants to share
anonymized learning data and benefit from the system learning from this shared
learner
data set. This provides subsequent enhancement opportunities for adaptive
learning
experiences within each learner's e-learning experiences that would not be
attainable in
isolation with one another.
[0037] Generally, methods and systems described herein pertain to a
software-as-a-
service (SaaS) application designed to address the publication, versioning,
distribution,
and remote management needs of e-learning content across multiple learning
environments. A SaaS provider provisions the application and platform to
customers or
"tenants" of the system. Within a tenant, numerous types of users of the
tenant's
applications (the applications designed to publish, version, distribute, and
manage the
content) are possible, additionally learners using content produced by the
system are also
restricted to that tenant except when explicit permission to share or move
primary data
ownership has been applied (such as when a learner transfers to a new school).
[0038] The systems and methods described herein are configurable to
build e-
learning experiences that share the same learning topic, also known as a
knowledge
component, among multiple clients and e-learning experiences. This collection
of
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Date Recue/Date Received 2023-11-17
learning topics form a shared ontology (or dictionary) that enables all
tenants to share
anonymized learning data and benefit from the system by learning from this
shared
learner data set. This sharing allows for enhancing the adaptive learning
experiences for
each learner, which would not be attainable otherwise.
[0039] In Figure la, there is shown a system 10 for authoring,
publishing, versioning,
managing and distributing e-learning content across multiple learning
environments. The
system 10 comprises a tenant infrastructure 11 representing two types of
tenants; siloed
tenants 12a, 12b, where all applications and tenant microservices are made
available
explicitly for that tenant's use, and pooled tenants 12c where tenant
applications 13 and
microservices 14 are shared though the experience is distinct for each tenant
12a-c.
Practically, all tenants 12a-c have the same operating experience, the
difference between
these tenant types reflects an infrastructure differentiation primarily driven
by the size of
the client and the expected load on the SaaS system.
[0040] The tenant microservices 14 serve the functionality required
for the tenant
specific applications for media management (file management, transcoding,
caching,
etc.), content management (data warehousing of headless content), and
packaging and
distribution (transcompiling to the tenant's format, styling, and output
requirements).
Shared microservices 15 serve both the tenant application 13 and learning
environments
16 with functions either not needing to be balanced on load concerns, or where
anonymized "big data" shared across tenants can be valuable for all tenants
using the
system, such as, shared learner data informing numerous tenant experiences.
Example
shared microservices 15 comprise tenant registration, user management and
authentication, tenant management, tenant provisioning, learner knowledge
graph
(ontology), learner activity signaling and adaptive learning.
[0041] In both siloed and pooled tenant cases 12a-c, three types of
tenant learner
experiences are supported i.e. API connected experiences 17a, exported
experiences 16b
(the traditional model the industry currently relies on), and hosted
experiences 17c on
tenant infrastructure. API connected experiences 17a and hosted experiences
17c differ
only in the methods necessary to connect a tenant's clients' learning
management system,
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applications, or hosted environments with the tenant's infrastructure for
delivering and
updating the experiences.
[0042] Figure lb shows an overall system architecture 20 comprising a
client user
device 21, or tenant device, with a processor 22, memory device 23 and
input/output
interface module 24, interconnected by communications bus 25. The client user
may be
a customer who may be represented as a "tenant" of the system 20, such as a
school,
college, university, or other educational institution that serves end users,
such as students
or learners. Tenants have access to tenant applications for publishing,
versioning,
distributing, and managing the content. In one example, learners consuming the
produced
content are also restricted to that tenant except when explicit permission to
share or move
primary data ownership has been approved e.g., when a learner transfers to a
new school
and therefore switches from one tenant to another tenant.
[0043] In one example, memory device 23 is capable of storing machine
executable
instructions 26, data 27, including data models and process models. Further,
the
processor 22 is capable of executing the instructions 26 stored in memory
device 23 to
implement aspects of processes described herein. For example, processor 22 may
be
embodied as an executor of software instructions 26, wherein the software
instructions
26 may specifically configure the processor 22 to perform algorithms and/or
operations
described herein when the software instructions 26 are executed.
Alternatively, the
processor 22 may execute hard-coded functionality. Client device 21 also
comprises a
graphical user interface (GUI) 28 and database 29 are also coupled to tenant
user device
21 via I/0 interface module 24.
[0044] The client device 21 may be communicatively coupled to a
server machine
30, such as a SaaS server computer, via a communication network 31. The server
machine
30 provides a SaaS application designed to address the publication,
versioning,
distribution, and remote management needs of e-learning content across
multiple learning
environments. Coupled to the server machine 30 is one or more shared databases
32 that
are shared by the tenants. The one or more shared databases 32 may be
communicatively
coupled directly to the server machine 30 or via network 31. Similar to client
device 21,
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the server machine 30 comprises one or more processors 33, memory device 34
storing
data 35, including data models and process models, and instructions 36, I/O
interface
module 37, interconnected by communications bus 38.
[0045] At the client device 21, instructions 26 stored in memory
device 23 comprise
several service modules, such as: sign-in module 40; authoring module 42;
content
management module 44; content distribution module 46; lifecycle management
module
48; export management module 50; export management module 50; application
management module 52; media management module 54; packaging and distribution
module 56; and content API module 58.
[0046] Sign-in module 40 comprises an identity and access management
(TAM)
service for administrators, customers, and applications throughout the tenant
system;
authoring module 42 for providing a lesson and page builder environment, a
discreet
editing environment that provides drag and drop and keyboard-based controls
for
developing content that supports print and digital experiences for the same
set of content.
Example features of the system include but are not limited to print and
digital components
that can be used for both print and digital student experiences divided into
non-interactive
components typically equivalent to HTML elements or small groupings of HTML
elements such as, but not limited to headers, paragraphs, and images, and an
assessment
engine which manages all components and question types related to quizzing,
exams, and
other assessments of student experiences. Digital only components that can
only be used
for digital student experiences in view of their interactivity and technology
requirements
are divided into interactive components typically equivalent to HTML elements
or small
groupings of HTML elements and associated interaction such as, but not limited
to,
accordions, tabs, carousels, dynamic tables and iFrames. These components
often wrap,
nest, or elevate other Print and Digital or Digital Only experiences and may
have
configuration options for tailoring the expected experiences; interactive
learning objects
(IL0s) which are interactive components that have been specifically designed
around
learning outcomes, and enable students to better visually understand and
virtually interact
with phenomena they learn in the course material, such as, but not limited to,
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Date Recue/Date Received 2023-11-17
table of elements, environment simulations, and virtual labs; and preset
experience library
components which are standalone experiences with minimal configuration options
such
as, but not limited to standalone games which can be included in the
curriculum largely
"as-is". Other features of the system associated with the authoring module 42
comprise
asset manager 54 which handles uploading, editing, metadata, and inclusion of
media
such as images and video into the authored experience; collaboration tools for
visualizing
and managing shared manipulation of content asynchronously or synchronously as
the
material is built and reviewed. Functionality includes, but is not limited to,
visibility of
simultaneous user activity, commenting, and review process sharing and
management;
adaptive learning content management including tagging functionality,
knowledge graph
use and extension, and content section management.
[0047]
Sign-in module 40 comprises an identity and access management (TAM)
service for administrators, customers, and applications throughout the tenant
system;
authoring module 42 provides a lesson and page builder environment, a discreet
editing
environment that may provide drag and drop and keyboard-based controls for
developing
content that supports print and digital experiences for the same set of
content. Example
features of the system include but are not limited to print and digital
components that can
be used for both print and digital student experiences divided into non-
interactive
components typically equivalent to HTML elements or small groupings of HTML
elements such as, but not limited to headers, paragraphs, and images, and an
assessment
engine which manages all components and question types related to quizzing,
exams, and
other assessments of student experiences. Digital only components may be used
for
digital student experiences in view of their interactivity and technology
requirements, are
divided into interactive components typically equivalent to HTML elements or
small
groupings of HTML elements and associated interaction such as, but not limited
to,
accordions, tabs, carousels, dynamic tables and iFrames. These components
often wrap,
nest, or elevate other Print and Digital or Digital Only experiences and may
have
configuration options for tailoring the expected experiences; interactive
learning objects
(IL0s) which are interactive components that have been specifically designed
around
16
Date Recue/Date Received 2023-11-17
learning outcomes, and enable students to better visually understand and
virtually interact
with phenomena they learn in the course material, such as, but not limited to,
periodic
table of elements, environment simulations, and virtual labs; and preset
experience library
components which are standalone experiences with minimal configuration options
such
as, but not limited to standalone games which can be included in the
curriculum largely
"as-is". Other features of the system associated with the authoring module 42
comprise
asset manager 54 which handles uploading, editing, metadata, and inclusion of
media
such as images, audio, and video into the authored experience; collaboration
tools for
visualizing and managing shared manipulation of content asynchronously or
synchronously as the material is built and reviewed. Functionality includes,
but is not
limited to, visibility of simultaneous user activity, commenting, and review
process
sharing and management; adaptive learning content management including tagging
functionality, knowledge graph use and extension, and content section
management.
[0048]
Content management module 44 is responsible for learning content
development system for creating, editing, and managing the lessons and
supplementary
materials for each course package. The system loads the authoring system for
each
content piece that is being developed and enables the organization and
grouping of the
resulting materials into a course outline that instructs the packaging of
course packages
upon publication ("publishing") of a finalized course. Example features of the
authoring
system include but are not limited to: a course builder system, an overarching
system for
creating, editing, and managing courses within the system; a course outlines
system for
organizing all learning content of a particular course or course instance in
preparation for
publication; a content repository which stores content produced within the
authoring
system as headless content which is content that is described independently
from its
presentation layer save for reflections of components that were used. For
example, but
not limited to, a reference to the inclusion of a video as a video ID and
platform reference,
but no code for producing the appropriate player for that video experience.
Also included
is a course packaging system for publishing a finalized course into the
appropriate formats
in preparation for distribution; a catalogue management system for grouping a
collection
17
Date Recue/Date Received 2023-11-17
of one or more courses together that can be made available through content
distribution
module 46 to either other tenants or the public for various uses, such as but
not limited to
direct registration; version control, a system that tracks and manages all
changes within
the system, both at a authoring level and within other areas of the content
management
system; a article builder system, an overarching system for creating, editing,
and
managing all content produced by the system that is not a course, such as but
not limited
to knowledge base/help articles and blog posts; the overarching system uses
the same
authoring system for content management as the course builder, with learning
components reduced or suppressed in favour of expanded journalist components.
[0049] Content distribution module 46 comprises a system responsible
for creating,
editing, and managing the distribution of these materials to other tenants or
public end
points. Some features of the content distribution system 46 include, but are
not limited
to: reporting and management of how and where the courses are distributed via
automated
tracking of any "live hosted" experiences; reporting and management of how and
where
the courses are distributed via manually tracking for any non-"live hosted"
experiences;
and the systems for managing such distributions, such as but not limited to,
sending new
live updates or exporting new versions for non-"live hosted" experiences and
manually
updating distribution details.
[0050] Lifecycle management module 48 is a system and environment
for the overall
tenant SaaS application. Some features of the lifecycle management module 48
include,
but are not limited to: a reporting engine for all data related reporting
functions of the
application, such as but not limited to, systems usage and performance,
distribution
tracking, learner progression, class progression; notification services for
managing user
and system notifications both within and outside of the application; an update
manager
for managing application updates automated or otherwise; federation and access
control
for managing users, permissions, and groups and for turning on and off
features and
settings from the system; and security and monitoring for managing security
related
concerns.
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Date Recue/Date Received 2023-11-17
[0051] Export management module 50 is system responsible for
transcompiling
courses and articles to the tenant's format, styling, and output requirements.
Some
examples of outputs include, but are not limited to: a PDF format for users
with limited
or no access to devices who are looking for a printable copy of a course; an
accessible-
first HTML version of a course for users using assistive technology such as a
screen
reader; an offline version of the course with full digital interactivity
enabled for users
with limited or no access to the internet but who do have access to a digital
device such
as a computer or smartphone; a traditional LMS package for administrators
looking to
upload a static or traditional SCORM (or similar format) package to a LMS for
students
to study from; a remotely managed LMS package for administrators looking to
distribute
courses to LMSes that can be updated directly and in real-time from within the
platform
without the need to upload a new version; and directly hosted standalone
experiences,
whether public or behind a managed login, which are not dependent on a third
party LMS.
[0052] Application management module 52 is responsible for the
tenant's account
related functions such as but not limited to, billing, knowledge base,
technical support,
account management, third party integrations, theming, licensing, and shared
service
subscriptions, and permissions.
[0053] Media management module 54 comprises the microservices behind
the
system's asset manager for handling uploading, editing, and metadata of media.
Some
features of the media management module 54 include, but are not limited to:
asset variants
from such actions as cropping, colour manipulation etc. that are saved
independently from
but in direct relationship to the source material; compression and
optimization services
for reducing and streamlining file sizes and metadata of assets; and services
for managing
assets between local and remote file systems such as cloud storage providers.
[0054] Packaging and distribution module 56 comprises microservices
responsible
for supporting the course packaging module 44 and content distribution 46
services of the
system, as will be described in more detail with reference to Figures 3 and 5.
19
Date Recue/Date Received 2023-11-17
[0055] Content API module 58 comprises microservices responsible for
transferring
data and meaning of the content between the content store of databases and the
requesting
services.
[0056] For example, authoring module 42 provides a suitable user-
interface with an
authoring environment for building pages for lessons and/or courses. In one
example, the
authoring environment comprises a discreet editing environment that provides
drag and
drop and keyboard-based controls for developing content. In one example, the
same
developed content is suitable for both print and digital experiences. As such,
print and
digital components that can be used for both print and digital end-user
experiences
divided into non-interactive components typically equivalent to HTML elements
or small
groupings of HTML elements such as, but not limited to headers, paragraphs,
and images.
[0057] The systems and methods are configured to build e-learning
experiences that
are accessible-first and reflect inclusive-design with minimal additional
effort. This is
accomplished by biasing the editing experience towards print and accessible-
first
considerations accompanied by component considerations that are designed for
the
progressive enhancement of content experiences. Content that is produced in
this editing
experience is saved in a "headless" format, a format that decouples the
presentation of
the content from its structure, which enables a method of centralized content
management
that supports numerous exported experiences. When an author chooses to publish
and
package their e-learning experience this "headless" content is transcompiled
into various
formats selected by the author. During transcompilation key aspects of
presentation such
as the theming/styling of the content and selecting the appropriate
technologies for
inclusion such as video streaming providers are applied to the content.
[0058] In one example, the system export capabilities can be divided
into two
categories of experiences from a content perspective, such as print-related
experiences
and digital-related experiences.
[0059] Print-related experiences are experiences that are inherently
dependent on text
as a construct for delivery of the educational experience. These experiences
include
printed materials which can support individuals who have limited access to
devices, such
Date Recue/Date Received 2023-11-17
as computers, tablets, or smart phones, and individuals using assistive
technologies, such
as screen readers.
[0060] Digital-related experiences are experiences that are
inherently dependent on
the text print-related experiences but are also progressively enhanced
digitally. These
experiences can support individuals with access to devices, such as computers,
tablets, or
smart phones, who may or may not have consistent access to the internet. For
example,
in remote communities, individuals may not have access, or at least reliable
access to the
internet, however they may have access to devices that can present a digital
experience.
These internet-limited devices can thereby present the digital experience in
offline mode.
For individuals who have access to devices and reliable internet access
several
experiences can be supported, such as but not limited to, direct hosted
experiences where
the individual visits a website or downloads a mobile app and studies within
that
environment, traditional LMS experiences where the content is uploaded to the
LMS and
learners study the material within that environment, or live hosted LMS
experiences
where the learners study the material within their LMS but the content is
centrally
distributed to them through a hosted experience on the server computer 30.
[0061] Example features of the system include but are not limited to
print and digital
components that can be used for both print and digital student experiences
divided into
non-interactive components typically equivalent to HTML elements or small
groupings
of HTML elements such as, but not limited to headers, paragraphs, and images,
and an
assessment engine which manages all components and question types related to
quizzing,
exams, and other assessments of student experiences. Digital only components
that can
only be used for digital student experiences in view of their interactivity
and technology
requirements are divided into interactive components typically equivalent to
HTML
elements or small groupings of HTML elements and associated interaction such
as, but
not limited to, accordions, tabs, carousels, dynamic tables and iFrames. These
components often wrap, nest, or elevate other Print and Digital or Digital
Only
experiences and may have configuration options for tailoring the expected
experiences;
interactive learning objects (IL0s) which are interactive components that have
been
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Date Recue/Date Received 2023-11-17
specifically designed around learning outcomes, and enable students to better
visually
understand and virtually interact with phenomena they learn in the course
material, such
as, but not limited to, periodic table of elements, environment simulations,
and virtual
labs; and preset experience library components which are standalone
experiences with
minimal configuration options such as, but not limited to standalone games
which can be
included in the curriculum largely "as-is". Other features of the system
associated with
the authoring module 42 comprise asset manager 54 which handles uploading,
editing,
metadata, and inclusion of media such as images, audio, and video into the
authored
experience; collaboration tools for visualizing and managing shared
manipulation of
content asynchronously or synchronously as the material is built and reviewed.
Functionality includes, but is not limited to, visibility of simultaneous user
activity,
commenting, and review process sharing and management; adaptive learning
content
management including tagging functionality, knowledge graph use and extension,
and
content section management.
[0062] The courses and articles are transcompiled to the tenant's
format, styling, and
output requirements. Some examples of outputs include, but are not limited to:
a PDF
format for users with limited or no access to devices who are looking for a
printable copy
of a course.; an accessible-first HTML version of a course for users using
assistive
technology such as a screen reader; an offline version of the course with full
digital
interactivity enabled for users with limited or no access to the internet but
who do have
access to a digital device such as a computer or smartphone; a traditional LMS
package
for administrators looking to upload a static or traditional SCORM (or similar
format)
package to a LMS for students to study from; a remotely managed LMS package
for
administrators looking to distribute courses to LMSes that can be updated
directly and in
real-time from within the platform without the need to upload a new version;
and directly
hosted standalone experiences, whether public or behind a managed login, which
are not
dependent on a third party LMS.
[0063] The format, method and technological requirements to fulfill
an experience
type include, but are not limited to: Print for PDF; Print for Braille; Screen
Readers via
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Date Recue/Date Received 2023-11-17
media-less/text only HTML 5; LMSes via traditional learning technology
interoperability
standard packages such as SCORM/AICC/xAPI which contain full course materials
(the
industry standard method); LMSes via "remote lms packages" using a technique
to use
traditional learning technology interoperability standard packages such as
SCORM/AICC/xAPI as a vehicle to distribute iFramed and learning tools
interoperability
(LTI) enabled content which remotely reference full course materials; LMSes
via
"remotely managed" content where the system builds and updates the iFramed and
LTI
enabled content dynamically using the LMS's APIs while maintaining ongoing and
active
management of the content centrally; and Mobile apps or website experiences
without a
LMS via direct hosting of APIs, HTML5 and similar technologies.
[0064] It is to be appreciated that the particular arrangement of
modules 40-58
illustrated in Figure lb embodiment is presented by way of example only, and
alternative
arrangements can be used in other embodiments. For example, the functionality
associated with the modules 40-58 in other embodiments can be combined into a
single
module, or separated across a larger number of modules. As another example,
multiple
distinct processors 22 can be used to implement different ones of the modules
40-58 or
portions thereof. At least portions of the modules 40-58 may be implemented at
least in
part in the form of software comprising program code 26 stored in memory
device 23 and
executed by processor 22.
[0065] In one example, it is assumed that a tenant user associated
with one or more
of the client devices 21 is attempting to access services and applications on
a server
machine 30 over the network 31, and that access to the services requires
successful tenant
user authentication to the server machine 30. As such, the server machine 30
may include
an authentication server running an identity and access management (JAM)
service for
users, such as administrators, customers, and applications throughout the
tenant system.
[0066] The authentication or validation process begins with the
tenant user entering
login credentials on a user-interface 28 presented on a display associated
with the tenant
user device 21 and provided by a web server at the server machine 30. In an
operation,
the tenant user enters identification information that may include a user ID
and a
23
Date Recue/Date Received 2023-11-17
password. First time users may be required to provide personal information and
to select
the user ID and/or the password before being allowed to continue. The user
submits the
identification information to the server machine 30. A test is implemented in
an operation
to determine if the identification information authenticates the tenant user.
For example,
if the submitted user identification information matches the information
stored in a
database for a known valid user of the service, the user is authenticated. If
not, the user is
redirected back to the login user interface or to an error page and may try to
log in again.
If the user is successfully authenticated, the tenant user is provided access
to the service
provided at the server machine 30.
[0067] Looking at Figure 2, there is shown a tenant (or content
producer) real-time
adaptive learning infrastructure 60 associated with the server machine 31,
comprising a
tenants administration environment 62, centrally-hosted tenants shared
databases 32
comprising a learner knowledge graph 64 and anonymized learner event data
store 66,
and instructions 36 stored in memory device 34 and comprising tenants shared
microservices 68 and adaptive learning algorithms 70 executable by processor
33.
[0068] At the tenant device 21, tenant environment 72 comprises
memory device 23
with instructions 26 executable by the processor 33 to provide a tenant SaaS
application
74 with authoring module 42 for developing content and content distribution
module 46
for distributing the content to learners associated with various learner
experiences 76
(e.g.,. LMSes, directly on the web, or via mobile apps) which generates
learner events
78. Also stored in memory device 23 are processor-executable instructions such
as tenant
microservices 80 which anonymize learner event data 82 derived from learner
events 78,
as will be described in more detail below. The tenant environment 72 also
comprises one
or more databases comprising learning record store 84 which outputs the
outcome of the
adaptive learning algorithms 70. Inferred adaptive experiences 26 are fed back
to the
learner experiences 76 to refine the course materials and learning of same.
[0069] Looking at Figure 3, there is shown a flow chart 100 outlining
example steps
for real-time adaptive learning, in which a tenant (or content producer)
develops adaptive
e-learning experiences for a plurality of learners.
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Date Recue/Date Received 2023-11-17
[0070] Starting in step 102, the tenant user accesses one more
tenant shared databases
32 with a learner knowledge graph 64 comprising a centrally hosted graph
database with
the defined ontology (dictionary) for all learning topics that are shared by
all tenants of
the system. Generally, the graph database stores nodes and relationships
instead of tables,
or documents, such that the data is stored without restricting it to a pre-
defined model
which results in flexibility in the use of the data. Example graph data points
include, but
are not limited to: a universally unique identifier (UUID) for each unique
learning topic;
a learning topic label that provides the real world context for authors to
categorize their
content; node relationships that describe cross-dependencies of related
learning topics;
prior learning topics (also known as prerequisites) for formalized
dependencies within
the node relationships; and adaptive learning variables related to the
learning topic, such
as, but not limited to, knowledge component (KC) Init values necessary to more
effectively run Bayesian Knowledge Tracing (BKT) algorithms.
[0071] In step 104, the course materials are developed within an
authoring application
42 of the tenant, and each section of content that is associated with learning
outcomes is
tagged or associated with a learning topic that directly references the topic
within the
learner knowledge graph. The authoring application 42 allows the tenant to
navigate and
select the appropriate learning topic for a section of content and
assessments. Preferably,
any content in the system can only have one learning topic attached to it.
[0072] Next, in step 106, the tenant packages the content into the
course materials,
publishes and distributes the course materials via a course distribution
application 46, and
the content within each learning section is tagged with the learning topic for
all
appropriate user interaction.
[0073] In step 108, each course that is published may be distributed
through
numerous digital means and learner experiences for each Tenant's version of
that course,
such as via learning management systems, directly on the web, or via mobile
apps. The
learner viewing and interaction activities related to content consumption and
learner
validation activities such as completing practice questions, quiz/assessment
progress and
Date Recue/Date Received 2023-11-17
completion, consumption habits, and user/usability feedback are tracked. These
activities
represent learning events 78 which are used to calculate learner progression.
[0074] In step 110, any learner on any learner experience for any
tenant distribution
shares the same set of learning events for the course material being studied.
In one
example, while each tenant will have their own respective learning content,
the sharing
of centralized learning topics guarantees that learners studying a "Math 9"
course that
includes content on the "basics of quadratics" with tenant A, would have
comparable to
content for tenant B who cover similar material in a "Math 10" course that
includes
content on the "basics of quadratics". Effectively, learning insights may be
shared across
learners of both tenants A and B. Accordingly, the more tenants that have
content with
the same learning topics, the higher the collective volume of learners, and
the greater the
size of the learner event data which can be used to train the adaptive
learning models.
These learner events will be passed to the tenant environment's microservices
80 which
manage the learner event data stream 82 for their own learners. These
microservices 80
also anonymize the learner event data prior to sharing the data with other
tenants, thereby
by enhancing privacy of the individual students or learners, including
tenants.
[0075] In step 112, a centrally shared set of adaptive learning
algorithms 70 and their
associated microservices 68 are run on each event that is received from each
tenant during
a learner's experience. For example, when a quiz question is answered the
details of the
interaction (correct or incorrect) are anonymized and sent to the shared
tenant
microservices to determine that learner's progress. As such, as the learners
engage with
the content, events of interests are sent to an event processor which
sanitizes the event by
assigning it an anonymized learner UUID that remains associated with the
tenant and
standardizes the format of the learner event data. Sanitizing this activity at
the point of
origin enables the tenant to track and make requests against their learners
while sharing
the event data anonymously with other tenants.
[0076] In step 114, the results of the adaptive learning algorithms
for the individual
learner are sent back to the calling tenant; and simultaneously, the
anonymized event data
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Date Recue/Date Received 2023-11-17
and results of the adaptive learning algorithms are sent to an anonymized
learner event
store 66, and in step 116, the inferred adaptive experiences are fed back to
step 108.
[0077] Newly anonymized requests are then sent to a learner event
data stream
processor, and the key data points include, but are not limited to: the
learner topic UUID
for relating the event to the appropriate centralized learning topic; the
anonymized learner
UUID for enabling the learner's tenant to make learner tracking requests; the
learning
event representing standardized labels of activity for categorization,
reporting, and
determining the appropriate processing for such events; additional details of
the event,
such as in the case of an assessment event (quiz question answered), was the
response
indicative of a successful answer or a failure to correctly answer; and
tracking
mechanisms or status indicators regarding the post processing of the event.
[0078] In addition to the activities associated with the processing
of an active event
(steps 102-114), the methods and systems support direct requests of the system
to provide
details on a learner. This is typically called at an appropriate time in the
learning
experience as a means of initiating an adaptive experience that was not tied
to an input
event (such as a learner opt-in request for further assistance). This type of
learner status
request directly calls the adaptive learning managing processing scripts,
which would
return the learner scores from the anonymized learner data warehouse.
[0079] The insights derived from the larger data set would then be
processed and any
optimization opportunities sent back to the originating learner knowledge
graph database
for improving the performance of the system. For example, in the case of BKT,
one area
of opportunity is to refine the Knowledge Component init value for each
learning topic.
As such, the central store allows for safe algorithm refinement against all
learners use
across all tenants, as described further with reference to Figures 4 and 5.
[0080] In Figure 4, there is shown a tenants scheduled adaptive
learning model
training infrastructure 200 with tenant administration environment 62
comprising
processor-executable instructions 36 stored in memory device 34 and comprising
tenants
shared microservices 68 and adaptive learning models 120 comprising adaptive
learning
algorithms 70. The tenant administration environment 62 also comprises one or
more
27
Date Recue/Date Received 2023-11-17
tenants shared databases 32 comprising learner knowledge graph 78 and
anonymized
learner event store 66.
[0081] In Figure 5, there is shown a flow chart 300 outlining example
steps for
training adaptive learning models using tenant real-time adaptive learning
infrastructure
200. Generally, the data processes and workflow are comprised of the following
steps: a
processing script responsible for running machine learning to train the
adaptive learning
models is triggered on a regular schedule or on-demand; all applicable learner
event data
and scoring data across all tenants is retrieved from the data warehouse, and
this large
data set is used to train the various adaptive learning algorithms that are
available at the
time.
[0082] In step 302, each of the e-learning experiences associated
with a learning topic
comprising one or more adaptive learning variable.
[0083] In step 304, programmed instructions, such as a microservices
processing
script, are executed to train the adaptive learning models on a schedule or on-
demand.
[0084] In step 306, a full set of data of the anonymized learner
event store 66,
representing all learner activity across all tenants of the system is input as
training data
for the adaptive learning model. The event data store 84 includes results from
the live
run of the algorithms at the time of the event, for example in the case of a
Bayesian
Knowledge Tracing (BKT) algorithm run event the following would be example
data
points captured. By way of example, the anonymized event data store 66
comprises at
least: the anonymized learner UUID for enabling the learner's tenant to make
learner
tracking requests; the learner topic UUID for relating the event to the
appropriate
centralized learning topic; a mastery score that indicates the progress
towards learner
topic mastering; an init value which indicates the initial mastery state
assigned to the
learner prior to commencement of learning; and a slip value to address the
risk of a failure
to correctly answer from forgetting the learning. The actual data stored would
be
reflective of the specific algorithm requirements of any given event.
28
Date Recue/Date Received 2023-11-17
[0085] In step 308, the data from the anonymized event data store 66,
as well as any
relevant data pulled from the learner knowledge graph 64 such as inclusion of
prior results
and optimizations, is used to train the adaptive learner algorithms.
[0086] In step 310, following each iteration, the learner knowledge
graph 66 is
updated with the appropriate algorithm variables output. Data stored in the
learner
knowledge graph 66 comprises a learning topic universally unique identifier
(UUID) to
the system for each unique learning topic; a learning topic label that
provides the real-
world context for authors to categorize their content; a node relationships
that describe
cross dependencies of related learning topics; prior learning topics (also
known as
prerequisites) for formalized dependencies within the node relationships; and
adaptive
learning variables related to the learning topic, such as but not limited to
in the case of a
Bayesian Knowledge Tracing (BKT) algorithm where one or more knowledge
component Inits unique to the learning topic term will be saved. These values
will
indicate the algorithm's expected user initial mastery state that would be
assigned prior
to a commencement of learning in calculating learner progression. As such, the
insights
derived from the larger data set is then processed and any optimization
opportunities sent
back to the originating learner knowledge graph database 64 for improving the
performance of the system. For example, in the case of BKT, the knowledge
component
init value for each learning topic may be refined with each iteration.
[0087] One consideration of the effectiveness of the method or system
is the need to
drastically reduce the overhead and complexity of assigning learning topics to
content.
As shown in Figure 6, the topmost elements within the authoring canvas may be
either a
descriptive section 320 or a learning section 322, though additional section
types may be
supported in the future.
[0088] Descriptive sections 320 are intended to provide context for
the lesson
content, typical examples include instructions on how to navigate a lesson,
introductory
paragraphs that are not intended for learning, and concluding or summary
content without
learning objectives.
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Date Recue/Date Received 2023-11-17
[0089] Learning sections 322 are intended to track student progress
and assessments.
Learning sections 322 comprise the assignment of and limitation of the tenant
user to a
single learning topic before adding components and other content to the
section. All
content within a learning section inherits the learning topic from that
section and therefore
no further metadata entry is needed by the author to assign learning topics,
thereby
drastically reducing the overhead to enable and actively support adaptive
learning
expectations. For example, if an author added an opening paragraph, a video
explainer,
two more paragraphs, and a quiz composed of five questions to a learning
section within
a lesson then, all nine of these elements would automatically be classified
with the same
learning topic as the section.
[0090] Looking at Figure 7, an author is presented with user-
interface 400 comprising
an authoring area or canvas 401 comprising content 402, a top bar 403 and a
components
tool bar 404. The top bar 403 comprises the following tools or elements, or a
sub-set or
superset thereof: logo 405 associated with the tenant user, title and/or
subtitle 406 of the
content 402, print view button 407 which allows the user to view a print
version of the
content 402, digital view button 408 which allows the author to view a digital
version of
the content 402, autosave notification 410, share button 412, preview button
414,
notification component 416 and author identification 418. The components tool
bar 404
comprises the following components or content elements, or a subset or
superset thereof:
section component 420, formatted text paragraph component 422, image component
424,
non-interactive component 426, quiz component 428, video component 430,
interactive
component 432, undo icon 434, redo icon 436 and query icon 438.
[0091] The content 402 within the authoring canvas 401 comprises a
unit title 440, a
title 442, a descriptive section 444 and a learning section 446. Additional
descriptive
sections may be added to content 402 by actuating an "Add Descriptive Section"
dialog
box 320, and learning sections which track student progress and assessments
may be
added to content 402 by actuating "Add Learning Section" dialog box 320.
[0092] As Figure 7 indicates, when adding a descriptive section 320
to the lesson on
the authoring canvas 401, the user is prompted to insert a component 420-430
into box
Date Recue/Date Received 2023-11-17
439. This action is in contrast with learning sections 322 where a topic
selection 447 is
required prior to being permitted to add components 420-432 to descriptive
section 320.
The topic selection offers search and topic recommendations to the author for
their
convenience, but also provides a "browse tree" option 500, as shown in Figure
8, which
allows for the navigation and selection of any learning topic 502 within the
ontology of
the centrally shared knowledge graph.
[0093] As shown in Figure 9, once a learning topic 502 is selected,
the learning topic
label 504 display on the section title bar. This selection may be replaced
with a new
learning topic 502 at any time; however no learning section 444 can exist
without a
learning topic 502 being selected. Once a learning topic 502 is selected the
learning
section 446 behaves exactly like a descriptive section 444 including
immediately
allowing authors to add components 420-430 to the authoring canvas 401.
[0094] Figure 10 illustrates a block diagram of an example machine
600, such as
client device 21 or server computer 30, upon which any one or more of the
techniques
(e.g., methodologies) discussed herein may be performed. Machine 600 (e.g.,
computer
system) may include a hardware processor 601 (e.g., a central processing unit
(CPU), a
graphics processing unit (GPU), a hardware processor core, or any combination
thereof),
a main memory 602 and a static memory 606, connected via an interlink 603
(e.g., link
or bus), as some or all of these components may constitute hardware for
systems or related
implementations discussed above.
[0095] Generally, the hardware processor 601 may, for example,
include at least one
of a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC)
Processor, a Complex Instruction Set Computing (CISC) Processor, a Graphics
Processing Unit (GPU), a Digital Signal Processor (DSP), a Tensor Processing
Unit
(TPU), a Neural Processing Unit (NPU), a Vision Processing Unit (VPU), a
Machine
Learning Accelerator, an Artificial Intelligence Accelerator, an Application
Specific
Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Radio-
Frequency
Integrated Circuit (RFIC), a Neuromorphic Processor, a Quantum Processor, or
any
combination thereof. A processor circuit may further be a multi-core processor
having
31
Date Recue/Date Received 2023-11-17
two or more independent processors (sometimes referred to as "cores") that may
execute
instructions contemporaneously. Multi-core processors contain multiple
computational
cores on a single integrated circuit die, each of which can independently
execute program
instructions in parallel. Parallel processing on multi-core processors may be
implemented
via architectures like superscalar, VLIW, vector processing, or SIMD that
allow each core
to run separate instruction streams concurrently. A processor circuit may be
emulated in
software, running on a physical processor, as a virtual processor or virtual
circuit. The
virtual processor may behave like an independent processor but is implemented
in
software rather than hardware.
[0096] Specific examples of main memory 602 include Random Access
Memory
(RAM), and semiconductor memory devices, which may include storage locations
in
semiconductors such as registers. Specific examples of static memory 606
include non-
volatile memory, such as semiconductor memory devices (e.g., Electrically
Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable
Read-Only Memory (EEPROM)) and flash memory devices; magnetic disks, such as
internal hard disks and removable disks; magneto-optical disks; RAM; or
optical media
such as CD-ROM and DVD-ROM disks.
[0097] The machine 21, 30 may further include a display device 610,
an input device
612 (e.g., a keyboard), and a user interface (UI) navigation device 614 (e.g.,
a mouse). In
an example, the display device 610, input device 612, and UI navigation device
614 may
be a touch-screen display. The machine 21, 30 may include a mass storage
device 616
(e.g., drive unit), a signal generation device 618 (e.g., a speaker), a
network interface
device 620. The machine 21, 30 may include an output controller 723, such as a
serial
(e.g., universal serial bus (USB), parallel, or other wired or wireless (e.g.,
infrared (IR),
near field communication (NFC), etc.) connection to communicate or control one
or more
peripheral devices (e.g., a printer, card reader, etc.).
[0098] The mass storage device 616 may comprise a machine-readable
medium 622
on which is stored one or more sets of data structures or instructions 604
(e.g., software)
embodying or utilized by any one or more of the techniques or functions
described herein.
32
Date Recue/Date Received 2023-11-17
The instructions 604 may also reside, completely or at least partially, within
the main
memory 602, within static memory 606, or within the hardware processor 601
during
execution thereof by the machine 21, 30. In an example, one or any combination
of the
hardware processor 601, the main memory 602, the static memory 606, or the
mass
storage device 616 comprises a machine readable medium.
[0099]
Specific examples of machine-readable media include, one or more of non-
volatile memory, such as semiconductor memory devices (e.g., EPROM or EEPROM)
and flash memory devices; magnetic disks, such as internal hard disks and
removable
disks; magneto-optical disks; RAM; or optical media such as CD-ROM and DVD-ROM
disks. While the machine-readable medium is illustrated as a single medium,
the term
"machine readable medium" may include a single medium or multiple media (e.g.,
a
centralized or distributed database, or associated caches and servers)
configured to store
the one or more instructions 604.
[00100] The term "machine readable medium" includes, for example, any medium
that
is capable of storing, encoding, or carrying instructions for execution by the
machine 21,
30 and that cause the machine 21, 30 to perform any one or more of the
techniques of the
present disclosure or causes another apparatus or system to perform any one or
more of
the techniques, or that is capable of storing, encoding or carrying data
structures used by
or associated with such instructions. Non-limiting machine-readable medium
examples
include solid-state memories, optical media, or magnetic media. Specific
examples of
machine-readable media include: non-volatile memory, such as semiconductor
memory
devices (e.g., Electrically Programmable Read-Only Memory (EPROM),
Electrically
Erasable Programmable Read-Only Memory (EEPROM)) and flash memory devices;
magnetic disks, such as internal hard disks and removable disks; magneto-
optical disks;
Random Access Memory (RAM); or optical media such as CD-ROM and DVD-ROM
disks. In some examples, machine readable media includes non-transitory
machine-
readable media. In some examples, machine readable media includes machine
readable
media that is not a transitory propagating signal.
33
Date Recue/Date Received 2023-11-17
[00101] The instructions 604 may be transmitted or received, for example, over
a
communications network 605 using a transmission medium via the network
interface
device 620 utilizing any one of a number of transfer protocols (e.g., frame
relay, internet
protocol (IP), transmission control protocol (TCP), user datagram protocol
(UDP),
hypertext transfer protocol (HTTP), etc.). Example communication networks
include a
local area network (LAN), a wide area network (WAN), a packet data network
(e.g., the
Internet), mobile telephone networks (e.g., cellular networks), Plain Old
Telephone
(POTS) networks, and wireless data networks (e.g., Institute of Electrical and
Electronics
Engineers (IEEE) 802.11 family of standards known as Wi-Fi0), IEEE 802.15.4
family
of standards, a Long Term Evolution (LTE) 4G or 5G family of standards, a
Universal
Mobile Telecommunications System (UMTS) family of standards, peer-to-peer
(P2P)
networks, satellite communication networks, among others.
[00102] In an example, the network interface device 620 includes one or more
physical
jacks (e.g., Ethernet, coaxial, or other interconnection) or one or more
antennas to access
the communications network 605. In an example, the network interface device
620
includes one or more antennas to wirelessly communicate using at least one of
single-
input multiple-output (SIMO), multiple-input multiple-output (MIMO), or
multiple-input
single-output (MISO) techniques. In some examples, the network interface
device 620
wirelessly communicates using Multiple User MIMO techniques. The term
"transmission
medium" shall be taken to include any intangible medium that is capable of
storing,
encoding or carrying instructions for execution by the machine 600, and
includes digital
or analog communications signals or other intangible medium to facilitate
communication of such software.
[00103] Examples, as described herein, can include, or can operate on, logic
or a
number of components, modules, or mechanisms (all referred to hereinafter as
"modules"). Modules are tangible entities (e.g., hardware) capable of
performing
specified operations and is configured or arranged in a certain manner. In an
example,
circuits are arranged (e.g., internally or with respect to external entities
such as other
circuits) in a specified manner as a module. In an example, the whole or part
of one or
34
Date Recue/Date Received 2023-11-17
more computer systems (e.g., a standalone, client or server computer system)
or one or
more hardware processors are configured by firmware or software (e.g.,
instructions, an
application portion, or an application) as a module that operates to perform
specified
operations. In an example, the software can reside on a non-transitory
computer readable
storage medium or other machine-readable medium. In an example, the software,
when
executed by the underlying hardware of the module, causes the hardware to
perform the
specified operations.
[00104] Accordingly, the term "module" is understood to encompass a tangible
entity,
be that an entity that is physically constructed, specifically configured
(e.g., hardwired),
or temporarily (e.g., transitorily) configured (e.g., programmed) to operate
in a specified
manner or to perform part or all of any operation described herein.
Considering examples
in which modules are temporarily configured, each of the modules need not be
instantiated at any one moment in time. For example, where the modules
comprise a
general-purpose hardware processor configured using software, the general-
purpose
hardware processor is configured as respective different modules at different
times.
Software can accordingly configure a hardware processor, for example, to
constitute a
particular module at one instance of time and to constitute a different module
at a different
instance of time.
[00105] Implementations of the subject matter and the functional operations
described
in this specification can be implemented in digital electronic circuitry, in
tangibly
embodied computer software or firmware, in computer hardware, including the
structures
disclosed in this specification and their structural equivalents, or in
combinations of one
or more of them. Implementations of the subject matter described in this
specification can
be implemented as one or more computer programs, i.e., one or more modules of
computer program instructions encoded on a tangible, non-transitory computer-
storage
medium for execution by, or to control the operation of, data processing
apparatus.
Alternatively or in addition, the program instructions can be encoded on an
artificially
generated propagated signal, e.g., a machine-generated electrical, optical, or
electromagnetic signal that is generated to encode information for
transmission to suitable
Date Recue/Date Received 2023-11-17
receiver apparatus for execution by a data processing apparatus. The computer-
storage
medium can be a machine-readable storage device, a machine-readable storage
substrate,
a random or serial access memory device, or a combination of one or more of
them.
[00106] A computer program, which may also be referred to or described as a
program,
software, a software application, a module, a software module, a script, or
code can be
written in any form of programming language, including compiled or interpreted
languages, or declarative or procedural languages, and it can be deployed in
any form,
including as a stand-alone program or as a module, component, subroutine, or
other unit
suitable for use in a computing environment. A computer program may, but need
not,
correspond to a file in a file system. A program can be stored in a portion of
a file that
holds other programs or data, e.g., one or more scripts stored in a markup
language
document, in a single file dedicated to the program in question, or in
multiple coordinated
files, e.g., files that store one or more modules, sub-programs, or portions
of code. A
computer program can be deployed to be executed on one computer or on multiple
computers that are located at one site or distributed across multiple sites
and
interconnected by a communication network. While portions of the programs
illustrated
in the various figures are shown as individual modules that implement the
various features
and functionality through various objects, methods, or other processes, the
programs may
instead include a number of sub-modules, third-party services, components,
libraries, and
such, as appropriate. Conversely, the features and functionality of various
components
can be combined into single components, as appropriate.
[00107] The processes and logic flows described in this specification can be
performed
by one or more programmable computers executing one or more computer programs
to
perform functions by operating on input data and generating output. The
processes and
logic flows can also be performed by, and apparatus can also be implemented
as, special
purpose logic circuitry, e.g., a CPU, a GPU, an FPGA, or an ASIC.
[00108] The term "graphical user interface," or "GUI," may be used in the
singular or
the plural to describe one or more graphical user interfaces and each of the
displays of a
particular graphical user interface. Therefore, a GUI may represent any
graphical user
36
Date Recue/Date Received 2023-11-17
interface, including but not limited to, a web browser, a touch screen, or a
command line
interface (CLI) that processes information and efficiently presents the
information results
to the user. In general, a GUI may include a plurality of user interface (UT)
elements,
some or all associated with a web browser, such as interactive fields, pull-
down lists, and
buttons operable by the user. These and other UT elements may be related to or
represent
the functions of the web browser.
[00109] Implementations of the subject matter described in this specification
can be
implemented in a computing system that includes a back-end component, e.g., as
a data
server, or that includes a middleware component, e.g., an application server,
or that
includes a front-end component, e.g., a client computer having a graphical
user interface
or a Web browser through which a user can interact with an implementation of
the subject
matter described in this specification, or any combination of one or more such
back-end,
middleware, or front-end components. The components of the system can be
interconnected by any form or medium of wireline and/or wireless digital data
communication, e.g., a communications network 605.
[00110] Various Notes
[00111] Each of the non-limiting aspects in this document can stand on its own
or can
be combined in various permutations or combinations with one or more of the
other
aspects or other subject matter described in this document.
[00112] The above detailed description includes references to the accompanying
drawings, which form a part of the detailed description. The drawings show, by
way of
illustration, specific embodiments in which the invention can be practiced.
These
embodiments are also referred to generally as "examples." Such examples can
include
elements in addition to those shown or described. However, the present
inventors also
contemplate examples in which only those elements shown or described are
provided.
Moreover, the present inventors also contemplate examples using any
combination or
permutation of those elements shown or described (or one or more aspects
thereof), either
with respect to a particular example (or one or more aspects thereof), or with
respect to
other examples (or one or more aspects thereof) shown or described herein.
37
Date Recue/Date Received 2023-11-17
[00113] In this document, the terms "a" or "an" are used, as is common in
patent
documents, to include one or more than one, independent of any other instances
or usages
of "at least one" or "one or more." In this document, the term "or" is used to
refer to a
nonexclusive or, such that "A or B" includes "A but not B," "B but not A," and
"A and
B," unless otherwise indicated. In this document, the terms "including" and
"in which"
are used as the plain-English equivalents of the respective terms "comprising"
and
"wherein." Also, in the following claims, the terms "including" and
"comprising" are
open-ended, that is, a system, device, article, composition, formulation, or
process that
includes elements in addition to those listed after such a term in a claim are
still deemed
to fall within the scope of that claim. Moreover, in the following claims, the
terms "first,"
"second," and "third," etc., are used merely as labels, and are not intended
to impose
numerical requirements on their objects.
[00114] Method examples described herein can be machine or computer-
implemented
at least in part. Some examples can include a computer-readable medium or
machine-
readable medium encoded with instructions operable to configure an electronic
device to
perform methods as described in the above examples. An implementation of such
methods can include code, such as microcode, assembly language code, a higher-
level
language code, or the like. Such code can include computer readable
instructions for
performing various methods. The code may form portions of computer program
products. Such instructions can be read and executed by one or more processors
to enable
performance of operations comprising a method, for example. The instructions
are in any
suitable form, such as but not limited to source code, compiled code,
interpreted code,
executable code, static code, dynamic code, and the like.
[00115] Further, in an example, the code can be tangibly stored on one or more
volatile, non-transitory, or non-volatile tangible computer-readable media,
such as during
execution or at other times. Examples of these tangible computer-readable
media can
include, but are not limited to, hard disks, removable magnetic disks,
removable optical
disks (e.g., compact disks and digital video disks), magnetic cassettes,
memory cards or
sticks, random access memories (RAMs), read only memories (ROMs), and the
like.
38
Date Recue/Date Received 2023-11-17
[00116]
The above description is intended to be illustrative, and not restrictive. For
example, the above-described examples (or one or more aspects thereof) may be
used in
combination with each other. Other embodiments can be used, such as by one of
ordinary
skill in the art upon reviewing the above description. The Abstract is
provided to allow
the reader to quickly ascertain the nature of the technical disclosure. It is
submitted with
the understanding that it will not be used to interpret or limit the scope or
meaning of the
claims. Also, in the above Detailed Description, various features may be
grouped
together to streamline the disclosure. This should not be interpreted as
intending that an
unclaimed disclosed feature is essential to any claim. Rather, inventive
subject matter
may lie in less than all features of a particular disclosed embodiment. Thus,
the following
claims are hereby incorporated into the Detailed Description as examples or
embodiments, with each claim standing on its own as a separate embodiment, and
it is
contemplated that such embodiments can be combined with each other in various
combinations or permutations. The scope of the invention should be determined
with
reference to the appended claims, along with the full scope of equivalents to
which such
claims are entitled.
39
Date Recue/Date Received 2023-11-17
