U.S. patent application number 16/673277 was filed with the patent office on 2020-02-27 for network based intervention.
The applicant listed for this patent is Pearson Education, Inc.. Invention is credited to Steven H. Hill, Sean A. YORK.
Application Number | 20200067967 16/673277 |
Document ID | / |
Family ID | 57996156 |
Filed Date | 2020-02-27 |
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United States Patent
Application |
20200067967 |
Kind Code |
A1 |
YORK; Sean A. ; et
al. |
February 27, 2020 |
NETWORK BASED INTERVENTION
Abstract
Methods and systems for network-based intervention are
disclosed. The methods can include receiving a user response and
analyzing the user response and other user data to determine a user
typology. The user typology can be compared with risk data that
indicates the user's risk of failing to achieve a target outcome
based on the identified user typology. If the user's risk of
failing to achieve the target outcome exceeds a desired level, a
mitigation plan can be generated and provided to the user to
thereby facilitate in the attainment of the target outcome.
Inventors: |
YORK; Sean A.;
(Harrisonburg, VA) ; Hill; Steven H.; (Austin,
TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Pearson Education, Inc. |
Bloomington |
MN |
US |
|
|
Family ID: |
57996156 |
Appl. No.: |
16/673277 |
Filed: |
November 4, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15337657 |
Oct 28, 2016 |
10516691 |
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16673277 |
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14928061 |
Oct 30, 2015 |
9928383 |
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15337657 |
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14204398 |
Mar 11, 2014 |
9483954 |
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15337657 |
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62072932 |
Oct 30, 2014 |
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61778296 |
Mar 12, 2013 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 21/577 20130101;
H04L 63/0421 20130101; H04L 63/1433 20130101; H04L 67/306 20130101;
G06F 21/6254 20130101; H04L 63/107 20130101; G06F 2221/2111
20130101; H04L 67/10 20130101 |
International
Class: |
H04L 29/06 20060101
H04L029/06; H04L 29/08 20060101 H04L029/08; G06F 21/62 20060101
G06F021/62 |
Claims
1. A system, comprising: a database coupled to a computer network
and storing: a user data comprising at least one user attribute; a
plurality of question prompts; a threshold; and a plurality of
intervention recommendations; a server comprising a server
computing device coupled to the computer network and including at
least one processor executing instructions within memory which,
when executed, cause the system to: receive, from a user interface
on a user device, a target outcome; select a question prompt, in
the plurality of question prompts, associated in the database with
the target outcome; transmit the question prompt through the
network to a user device; receive, from the user device, a response
to the question prompt; analyze the response to identify at least
one response attribute; input the at least one response attribute
and the at least one user attribute into a predictive model;
receive, as an output from the predictive model: an identified
classification; and a risk score comprising a likelihood of the
target outcome being achieved; responsive to the risk score being
below the threshold, select, from the plurality of intervention
recommendations, at least one intervention recommendation
associated in the database with the identified classification; and
transmit a notification including the intervention recommendation
through the computer network to the user device.
2. The system of claim 1, further comprising a plurality of user
devices, wherein the instructions further cause the server, prior
to receiving the target outcome, to: transmit a previous question
prompt, associated with the target outcome, through the network to
the plurality of user devices; receive, from the plurality of user
devices, a plurality of responses; and define a plurality of
classifications, including the classification, according to the
plurality of responses.
3. The system of claim 2, wherein the predictive model is trained
by: receiving the plurality of responses; identifying, within each
of the plurality of responses, at least one response attribute;
determining a resulting outcome associated with each of the
plurality of responses, the resulting outcome comprising a
determination of whether at least one target outcome was achieved;
and associating a classification with the resulting outcome.
4. The system of claim 3, further comprising a text mining software
running on the server and configured to identify the at least one
response attribute, comprising a common content, style, or tone of
each of the plurality of responses, by identifying a commonality of
terms within each of the plurality of responses.
5. The system of claim 3, further comprising an outcome software
engine running on the server and configured to: identify the
resulting outcome associated with each of the plurality of
responses; store, in association in the database, the resulting
outcome and the at least one response attribute for each of the
plurality of responses.
6. The system of claim 5 further comprising a typology
classification software engine running on the server and configured
to: select a plurality of resulting outcomes and the at least one
response characteristic associated with each of the plurality of
resulting outcomes from the database; determine a degree to which
the plurality of resulting outcomes resulted in at least one target
outcome being achieved; assign a typology categorization to a
subset of the plurality of responses sharing a common degree of
success for achieving the at least one target outcome.
7. A method, comprising: receiving, from a user interface on a user
device, by a server comprising a server computing device coupled to
a computer network and including at least one processor executing
instructions within memory, a target outcome; selecting, by the
server, from a plurality of question prompts stored in a database
coupled to the computer network, a question prompt associated in
the database with the target outcome; transmitting, by the server,
the question prompt through the computer network to a user device;
receiving, by the server from the user device, a response to the
question prompt; analyzing, by the server, the response to identify
at least one response attribute; inputting, by the server, the at
least one response attribute and at least one user attribute stored
in the database, into a predictive model; receiving, by the server,
as an output from the predictive model: an identified
classification; and a risk score comprising a likelihood of the
target outcome being achieved; responsive to the risk score being
below a threshold stored in the database, selecting, by the server,
from a plurality of intervention recommendations stored in the
database, at least one intervention recommendation associated in
the database with the identified classification; and transmitting,
by the server, a notification including the intervention
recommendation through the computer network to the user device.
8. The method of claim 7, further comprising the steps, prior to
receiving the target outcome, of: transmitting, by the server, a
previous question prompt, associated with the target outcome,
through the network to a plurality of user devices; receiving, by
the server, from the plurality of user devices, a plurality of
responses; and defining, by the server, a plurality of
classifications, including the identified classification, according
to the plurality of responses.
9. The method of claim 8, further comprising the step of training
the predictive model by: receiving the plurality of responses;
identifying, within each of the plurality of responses, by a text
mining software running on the server, at least one response
attribute; determining a resulting outcome associated with each of
the plurality of responses, the resulting outcome comprising a
determination of whether at least one target outcome was achieved;
and associating a classification with the resulting outcome.
10. The method of claim 9, wherein the text mining software is
configured to identify the at least one response attribute,
comprising a common content, style, or tone of each of the
plurality of responses, by identifying a commonality of terms
within each of the plurality of responses.
11. The method of claim 9, further comprising the steps of:
selecting, by the server from the database: a plurality of
resulting outcomes; and the at least one response characteristic
associated with each of the plurality of resulting outcomes;
determining, by the server, a degree to which the plurality of
resulting outcomes resulted in at least one achieved target
outcome; and assigning, by the server, a typology categorization to
each of a plurality of subsets of the plurality of responses
sharing a common degree of success for achieving the at least one
target outcome.
12. The method of claim 7 wherein the identified classification
indicates: the characteristics of an exhibited typology associated
with the response; or a degree to which the response exhibits at
least one response characteristic associated with the exhibited
typology.
13. The method of claim 7, wherein the risk score represents a
likelihood that the target outcome will be achieved, or a risk of
the target outcome not being achieved.
14. The method of claim 7, wherein intervention recommendation
comprises at least one action to be taken to mitigate the risk of a
predicted adverse outcome or at least one action to be taken to
increase the likelihood of a predicted positive outcome.
15. A system, comprising: a server comprising a server computing
device coupled to the computer network and including at least one
processor executing instructions within memory which, when
executed, cause the server to: receive, from a user interface on a
user device, a target outcome; select, from a plurality of question
prompts stored in a database coupled to the computer network, a
question prompt associated in the database with the target outcome;
transmit the question prompt through the computer network to a user
device; receive, from the user device, a response to the question
prompt; analyze the response to identify at least one response
attribute; input the at least one response attribute and at least
one user attribute stored in the database, into a predictive model;
receive, as an output from the predictive model: an identified
classification; and a risk score comprising a likelihood of the
target outcome being achieved; responsive to the risk score being
below a threshold stored in the database, select, from a plurality
of intervention recommendations stored in the database, at least
one intervention recommendation associated in the database with the
identified classification; and transmit a notification including
the intervention recommendation through the computer network to the
user device.
16. The system of claim 15, wherein the server is further
configured to, prior to receiving the target outcome: transmit a
previous question prompt, associated with the target outcome,
through the network to a plurality of user devices; receive, from
the plurality of user devices, a plurality of responses; and define
a plurality of classifications, including the identified
classification, according to the plurality of responses.
17. The system of claim 16, wherein the predictive model is trained
by: receiving the plurality of responses; identifying, within each
of the plurality of responses, by a text mining software running on
the server, at least one response attribute; determining a
resulting outcome associated with each of the plurality of
responses, the resulting outcome comprising a determination of
whether at least one target outcome was achieved; and associating a
classification with the resulting outcome.
18. The system of claim 17, wherein the server is further
configured to: select, from the database: a plurality of resulting
outcomes; and the at least one response characteristic associated
with each of the plurality of resulting outcomes; determine a
degree to which the plurality of resulting outcomes resulted in at
least one achieved target outcome; and assign a typology
categorization to each of a plurality of subsets of the plurality
of responses sharing a common degree of success for achieving the
at least one target outcome.
Description
CROSS-REFERENCES TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. application Ser.
No. 15/337,657, filed on Oct. 28, 2016, and entitled "NETWORK BASED
INTERVENTION", which is a continuation-in-part of U.S. application
Ser. No. 14/928,061, filed on Oct. 30, 2015, and entitled "METHODS
AND SYSTEMS FOR NETWORK-BASED ANALYSIS, INTERVENTION, AND
ANONYMIZATION", now U.S. Pat. No. 9,928,383, issued on Mar. 27,
2018, which claims the benefit of U.S. Provisional Application No.
62/072,932, filed Oct. 30, 2014; and this application is a
continuation-in-part of U.S. application Ser. No. 14/204,398, filed
Mar. 11, 2014, and entitled "EDUCATIONAL NETWORK BASED
INTERVENTION", now U.S. Pat. No. 9,483,954, issued on Nov. 1, 2016,
which claims the benefit of U.S. Provisional Application No.
61/778,296, filed on Mar. 12, 2013, the entirety of each of which
is hereby incorporated by reference herein.
BACKGROUND OF THE INVENTION
[0002] This application relates to the field data transmission and
network optimization.
[0003] A computer network or data network is a telecommunications
network which allows computers to exchange data. In computer
networks, networked computing devices exchange data with each other
along network links (data connections). The connections between
nodes are established using either cable media or wireless media.
The best-known computer network is the Internet.
[0004] Network computer devices that originate, route and terminate
the data are called network nodes. Nodes can include hosts such as
personal computers, phones, servers as well as networking hardware.
Two such devices can be said to be networked together when one
device is able to exchange information with the other device,
whether or not they have a direct connection to each other.
[0005] Computer networks differ in the transmission media used to
carry their signals, the communications protocols to organize
network traffic, the network's size, topology and organizational
intent. In most cases, communications protocols are layered on
(i.e. work using) other more specific or more general
communications protocols, except for the physical layer that
directly deals with the transmission media.
[0006] As the volume of data exchanged between nodes in computer
networks has increased, the speed of data transmission has become
increasingly more important. Although current technologies provide
improved speeds as compared to their predecessors, further
developments are needed.
BRIEF SUMMARY OF THE INVENTION
[0007] One aspect of the present disclosure relates to a system for
alerting a user device based on a proposed anonymization of a
contribution to a conversation thread via one or several
location-based anonymization rules. The system includes a user
device including: location determining-features that can determine
a physical location of the user device; a network interface that
can exchange data with a server via a communication network; and an
I/O subsystem that can convert electrical signals to
user-interpretable outputs in a user interface. The system can
include a server that can: receive a contribution from the user
device, which contribution includes content for placement in a
conversation thread; and determine an anonymization level for
applying to the contribution. In some embodiments, determining the
anonymization level includes: receiving physical location
information from the user device, which physical location
information identifies the physical location of the user device;
retrieving an anonymization table from a content access database;
and extracting level data from the anonymization table based on the
physical location information of the user device, which level data
includes the anonymization level. The server can: identify a
potential identifier in the content of the contribution; anonymize
the potential identifier according to the determined anonymization
level; and generate and provide an alert to the user device, which
alert includes code to direct the user device to provide an
indicator of the received alert via the I/O subsystem.
[0008] In some embodiments, the indicator of the received alert
includes: an aural indicator; a tactile indicator; and a visual
indicator. In some embodiments, the contribution includes
contribution data identifying the originator of the contribution.
In some embodiments, the server can determine an active location of
the originator of the contribution, which active location is based
on the physical location and a membership of the user. In some
embodiments, extracting level data from the anonymization table is
based on the physical location information of the user device and
the active location of the user.
[0009] In some embodiments, the server can retrieve anonymization
rules corresponding to the anonymization level. In some
embodiments, the location-determining features include a Global
Positioning System receiver and a Global Positioning System
antenna. In some embodiments, the server can identify the potential
identifier as an actual identifier when the potential identifier
matches user data and when the potential identifier does not match
group materials. In some embodiments, the server can identify the
potential identifier as not an actual identifier when the potential
identifier does not match user data. In some embodiments, the
server can identify the potential identifier as not an actual
identifier when the potential identifier matches user data, when
the potential identifier matches group materials, and when the
potential identifier is associated with the group materials. In
some embodiments, identifying the potential identifier as not an
actual identifier when the potential identifier matches user data,
when the potential identifier matches group materials, and when the
potential identifier is associated with the group materials
includes: identifying a window size for evaluation, which window
size specifies an amount of data surrounding the potential
identifier for analysis; identifying data within the window;
analyzing the data within the window; and outputting an indicator
of association between the potential identifier and the group
materials.
[0010] One aspect of the present disclosure relates to a method for
alerting a user device based on a proposed anonymization of a
contribution to a conversation thread via one or several
location-based anonymization rules. The method includes: receiving
at a server a contribution from a user device via a communication
network, which contribution includes content for placement in a
conversation thread; and determining with the server an
anonymization level for applying to the contribution. In some
embodiments, determining the anonymization level includes:
receiving physical location information from the user device, which
physical location information identifies a physical location of the
user device; retrieving an anonymization table from a content
access database; and extracting level data from the anonymization
table based on the physical location information of the user
device, which level data includes the anonymization level. In some
embodiments, the method includes identifying with the server a
potential identifier in the content of the contribution;
anonymizing with the server the potential identifier according to
the determined anonymization level; and generating and providing an
alert to the user device via the communication network. In some
embodiments, the alert includes code to direct the user device to
provide an indicator of the received alert via an I/O subsystem
configured to convert electrical signals to user-interpretable
outputs in a user interface.
[0011] In some embodiments, the indicator of the received alert
includes: an aural indicator; a tactile indicator; and a visual
indicator. In some embodiments, the contribution includes
contribution data identifying the originator of the contribution.
In some embodiments the method includes determining an active
location of the originator of the contribution, which active
location is based on the physical location and a membership of the
user. In some embodiments, extracting level data from the
anonymization table is based on the physical location information
of the user device and the active location of the user.
[0012] In some embodiments, the method includes retrieving
anonymization rules corresponding to the anonymization level. In
some embodiments, the location information is generated by
location-determining features of the user device. In some
embodiments, the location-determining features include a Global
Positioning System receiver and a Global Positioning System
antenna. In some embodiments, the method includes identifying the
potential identifier as an actual identifier when the potential
identifier matches user data and when the potential identifier does
not match group materials.
[0013] Further areas of applicability of the present disclosure
will become apparent from the detailed description provided
hereinafter. It should be understood that the detailed description
and specific examples, while indicating various embodiments, are
intended for purposes of illustration only and are not intended to
necessarily limit the scope of the disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] The present disclosure is described in conjunction with the
appended figures:
[0015] FIG. 1 is a block diagram showing illustrating an example of
a content distribution network.
[0016] FIG. 2 is a block diagram illustrating a computer server and
computing environment within a content distribution network.
[0017] FIG. 3 is a block diagram illustrating an embodiment of one
or more data store servers within a content distribution
network.
[0018] FIG. 4A is a block diagram illustrating an embodiment of one
or more content management servers within a content distribution
network.
[0019] FIG. 4B is a flowchart illustrating one embodiment of a
process for data management.
[0020] FIG. 4C is a flowchart illustrating one embodiment of a
process for evaluating a response.
[0021] FIG. 5 is a block diagram illustrating the physical and
logical components of a special-purpose computer device within a
content distribution network.
[0022] FIG. 6 is a block diagram illustrating one embodiment of the
communication network.
[0023] FIG. 7 is a block diagram illustrating one embodiment of
user device and supervisor device communication.
[0024] FIG. 8 is a schematic illustration of one embodiment of a
user device for use with a network-based intervention system.
[0025] FIG. 9 is a flowchart illustrating one embodiment of a
process for network based intervention.
[0026] FIG. 10 is a flowchart illustrating one embodiment of a
process for identifying a typology.
[0027] FIG. 11 is a flowchart illustrating one embodiment of a
process for requesting intervention.
[0028] FIG. 12 is a flowchart illustrating one embodiment of a
process for linking a target outcome to a typology.
[0029] FIG. 13 is a flowchart illustrating one embodiment of a
process for identifying a correlation between typology and outcome
achievement.
[0030] In the appended figures, similar components and/or features
may have the same reference label. Where the reference label is
used in the specification, the description is applicable to any one
of the similar components having the same reference label. Further,
various components of the same type may be distinguished by
following the reference label by a dash and a second label that
distinguishes among the similar components. If only the first
reference label is used in the specification, the description is
applicable to any one of the similar components having the same
first reference label irrespective of the second reference
label.
DETAILED DESCRIPTION OF THE INVENTION
[0031] The ensuing description provides preferred exemplary
embodiment(s) only, and is not intended to limit the scope,
applicability or configuration of the disclosure. Rather, the
ensuing description of the preferred exemplary embodiment(s) will
provide those skilled in the art with an enabling description for
implementing a preferred exemplary embodiment. It is understood
that various changes may be made in the function and arrangement of
elements without departing from the spirit and scope as set forth
in the appended claims.
[0032] With reference now to FIG. 1, a block diagram is shown
illustrating various components of a network-based intervention
system 100, also referred to herein as a content delivery network
100, which implements and supports certain embodiments and features
described herein. The content delivery network 100 collects,
receives, and stores data for one or several users of the system.
In some embodiments, for example, the content delivery network 100
can determine whether an intervention is desired based on the
likelihood that a user will achieve a target outcome.
[0033] The content delivery network 100 may include one or more
content management servers 102. As discussed below in more detail,
content management servers 102 may be any desired type of server
including, for example, a rack server, a tower server, a miniature
server, a blade server, a mini rack server, a mobile server, an
ultra-dense server, a super server, or the like, and may include
various hardware components, for example, a motherboard, processing
units, memory systems, hard drives, network interfaces, power
supplies, etc. Content management server 102 may include one or
more server farms, clusters, or any other appropriate arrangement
and/or combination or computer servers. Content management server
102 may act according to stored instructions located in a memory
subsystem of the server 102, and may run an operating system,
including any commercially available server operating system and/or
any other operating systems discussed herein.
[0034] The content delivery network 100 may include one or more
databases servers 104, also referred to herein as databases. The
database servers 104 can access data that can be stored on a
variety of hardware components. These hardware components can
include, for example, components forming tier 0 storage, components
forming tier 1 storage, components forming tier 2 storage, and/or
any other tier of storage or memory. In some embodiments, tier 0
storage refers to storage that is the fastest tier of storage in
the database server 104, and particularly, the tier 0 storage is
the fastest storage that is not RAM or cache memory. In some
embodiments, the tier 0 memory can be embodied in solid state
memory such as, for example, a solid-state drive (SSD) and/or flash
memory.
[0035] In some embodiments, the tier 1 storage refers to storage
that is one or several higher performing systems, and that is
relatively slower than tier 0 memory and relatively faster than
other tiers of memory. The tier 1 memory can be one or several hard
disks that can be, for example, high-performance hard disks. These
hard disks can be one or both of physically or communicatingly
connected such as, for example, by one or several fiber channels.
In some embodiments, the one or several disks can be arranged into
a disk storage system, and specifically can be arranged into an
enterprise class disk storage system. The disk storage system can
include any desired level of redundancy to protect data stored
therein. In one embodiment, the disk storage system can be made
with grid architecture that creates parallelism for uniform
allocation of system resources and balanced data distribution.
[0036] In some embodiments, the tier 2 storage refers to storage
that includes one or several relatively lower performing systems in
the memory management system, as compared to the tier 1 and tier 0
storages. Thus, tier 2 memory is relatively slower than tier 1 and
tier 0 memories. Tier 2 memory can include one or several
SATA-drives or one or several NL-SATA drives.
[0037] In some embodiments, the one or several hardware and/or
software components of the database server 104 can be arranged into
one or several storage area networks (SAN). These one or several
storage area networks can be one or several dedicated networks that
provide access to data storage, and particularly that can provide
access to consolidated, block level data storage. A SAN typically
has its own network of storage devices that are generally not
accessible through the local area network (LAN) by other devices.
The SAN allows access to these devices in a manner such that these
devices appear to be locally attached to the user device.
[0038] Databases 104 may comprise stored data relevant to the
functions of the content delivery network 100. Illustrative
examples of databases 104 that may be maintained in certain
embodiments of the content delivery network 100 are described below
in reference to FIG. 3. In some embodiments, multiple databases may
reside on a single database server 104, either using the same
storage components of server 104 or using different physical
storage components to assure data security and integrity between
databases. In other embodiments, each database may have a separate
dedicated database server 104.
[0039] The content delivery network 100 also may include one or
more user devices 106 and/or supervisor devices 110. In some
embodiments, the user devices 106 and/or supervisor devices 110
allow a user including a student, a teacher, a supervisor/analyst
including, for example, an administrator and/or parent, and/or a
process analyst including, for example, a researcher, observer,
social scientist, or data scientist, to access the CDN 100. User
devices 106 and supervisor devices 110 may display content received
via the content delivery network 100, and may support various types
of user interactions with the content. In some embodiments, the
user devices 106 and the supervisor devices 110 can be configured
to access data in, edit data in, retrieve data from, and/or provide
data to the data extraction and analysis system.
[0040] User devices 106 and supervisor devices 110 may include
mobile devices such as smartphones, tablet computers, personal
digital assistants, and wearable computing devices. Such mobile
devices may run a variety of mobile operating systems, and may be
enabled for Internet, e-mail, short message service (SMS),
Bluetooth.RTM., mobile radio-frequency identification (M-RFID),
and/or other communication protocols. Other user devices 106 and
supervisor devices 110 may be general purpose personal computers or
special-purpose computing devices including, by way of example,
personal computers, laptop computers, workstation computers,
projection devices, and interactive room display systems.
Additionally, user devices 106 and supervisor devices 110 may be
any other electronic devices, such as thin-client computers,
Internet-enabled gaming system, business or home appliances, and/or
personal messaging devices, capable of communicating over
network(s) 120. In some embodiments, the designated role of a
device, including a user device 106 or a supervisor device 110 can
vary based on the identity of the user using that device. Thus, in
some embodiments, both user and supervisor devices 106, 110 can
include the same hardware, but can be configured as one of a user
device 106 or a supervisor device 110 at the time of log-in by a
user to use that device.
[0041] In different contexts of data extraction and analysis
systems 100, user devices 106 and supervisor devices 110 may
correspond to different types of specialized devices, for example,
student devices and teacher devices in an educational network,
employee devices and presentation devices in a company network,
different gaming devices in a gaming network, etc. In some
embodiments, user devices 106 and supervisor devices 110 may
operate in the same physical location 107, such as a classroom, a
clinic, or conference room. In such cases, the devices may contain
components that support direct communications with other nearby
devices, such as a wireless transceivers and wireless
communications interfaces, Ethernet sockets or other Local Area
Network (LAN) interfaces, etc. In other implementations, the user
devices 106 and supervisor devices 110 need not be used at the same
location 107, but may be used in remote geographic locations in
which each user device 106 and supervisor device 110 may use
security features and/or specialized hardware (e.g.,
hardware-accelerated SSL and HTTPS, WS-Security, firewalls, etc.)
to communicate with the content management server 102 and/or other
remotely located user devices 106. Additionally, different user
devices 106 and supervisor devices 110 may be assigned different
designated roles, such as presenter devices, teacher devices,
administrator devices, or the like, and in such cases the different
devices may be provided with additional hardware and/or software
components to provide content and support user capabilities not
available to the other devices.
[0042] The content delivery network 100 also may include a privacy
server 108 that maintains private user information at the privacy
server 108 while using applications or services hosted on other
servers. For example, the privacy server 108 may be used to
maintain private data of a user within one jurisdiction even though
the user is accessing an application hosted on a server (e.g., the
content management server 102) located outside the jurisdiction. In
such cases, the privacy server 108 may intercept communications
between a user device 106 or supervisor device 110 and other
devices that include private user information. The privacy server
108 may create a token or identifier that does not disclose the
private information and may use the token or identifier when
communicating with the other servers and systems, instead of using
the user's private information.
[0043] As illustrated in FIG. 1, the content management server 102
may be in communication with one or more additional servers, such
as a content server 112, a user data server 112, and/or an
administrator server 116. Each of these servers may include some or
all of the same physical and logical components as the content
management server(s) 102, and in some cases, the hardware and
software components of these servers 112-116 may be incorporated
into the content management server(s) 102, rather than being
implemented as separate computer servers.
[0044] Content server 112 may include hardware and software
components to generate, store, and maintain the content resources
for distribution to user devices 106 and other devices in the
network 100. For example, in data extraction and analysis systems
100 used for professional training and educational purposes,
content server 112 may include databases of training materials,
presentations, plans, syllabi, reviews, evaluations, interactive
programs and simulations, course models, course outlines, and
various training interfaces that correspond to different materials
and/or different types of user devices 106. In data extraction and
analysis systems 100 used for media distribution, interactive
gaming, and the like, a content server 112 may include media
content files such as music, movies, television programming, games,
and advertisements. In some embodiments, the one or several content
servers 112 can be in communicating connection with the content
management server 102 via, for example, the internet or other
communication network.
[0045] In some embodiments, the content delivery network 100 can
include a plurality of content servers 112 that can contain the
same or different content. In some embodiments, this plurality of
content servers 112 can be controlled as a part of the content
delivery network 100, and in some embodiments, this plurality of
content servers 112 can be controlled independent of the content
delivery network 100. In such an embodiments, data can be
transferred to and/or from one or several of the plurality of
content servers 112 and some or all of the other components of the
content delivery network 100.
[0046] In one embodiment, for example, the content delivery network
100 can include a first content server, a second content server, a
third content server, and/or a fourth content server. In some
embodiments, for example, some or all of the first, second, third,
and fourth content servers can host websites, which can be unique.
These websites can contain information that can be retrieved and/or
used by some or all of the other components of the content delivery
network 100. In some embodiments, the first content server can be
configured to host and/or can host a first website containing a
first portion of species data, the second content server can be
configured to host and/or can host a second portion of species
data, the third content server can be configured to host and/or can
host a first portion of qualitative data, and/or the fourth content
server can be configured to host and/or can host a second portion
of qualitative data.
[0047] The one or several content servers 112 can be a source of
one or several tasks such as, for example, one or several academic
tasks and/or can be the source of some or all of the user profile
data. Thus, in some embodiments, information such as, for example,
the user's past interactions with the CDN 100 including courses of
study and/or academic tasks that the user has completed, and the
user's performance in those completed courses of study and/or
academic tasks. These tasks can include, for example, tasks that do
not request student input such as an article, a video, or other
instructional information, and/or tasks that request student input
such as a structured learning activity, one or several questions,
an activity assigning roles and responsibility, or any activity
resulting in student generated work product. In some embodiments,
the one or several content servers 112 can comprise a database of
one or more courses of study and/or one or more academic tasks. In
some embodiments, for example, the educational resource can be a
university, a school, an institution of learning, and/or a learning
management system (LMS).
[0048] Data server 114, also referred to herein as a user data
server 114, may include hardware and software components that store
and process data for multiple users relating to each user's
activities and usage of the content delivery network 100 or other
systems. For example, the content management server 102 may record
and track each user's system usage, including their user device
106, content resources accessed, and interactions with other user
devices 106. This data may be stored and processed by the user data
server 114, to support user tracking and analysis features. For
instance, in the professional training and educational contexts,
the user data server 114 may store and analyze each user's training
materials viewed, presentations attended, courses completed,
interactions, evaluation results, and the like. The user data
server 114 may also include a repository for user-generated
material, such as evaluations and tests completed by users, and
documents and assignments prepared by users. In the context of
media distribution and interactive gaming, the user data server 114
may store and process resource access data for multiple users
(e.g., content titles accessed, access times, data usage amounts,
gaming histories, user devices and device types, etc.).
[0049] Administrator server 116 may include hardware and software
components to initiate various administrative functions at the
content management server 102 and other components within the
content delivery network 100. For example, the administrator server
116 may monitor device status and performance for the various
servers, databases, and/or user devices 106 in the content delivery
network 100. When necessary, the administrator server 116 may add
or remove devices from the network 100, and perform device
maintenance such as providing software updates to the devices in
the network 100. Various administrative tools on the administrator
server 116 may allow authorized users to set user access
permissions to various content resources, monitor resource usage by
users and devices 106, and perform analyses and generate reports on
specific network users and/or devices (e.g., resource usage
tracking reports, training evaluations, etc.).
[0050] The content delivery network 100 may include one or more
communication networks 120. Although only a single network 120 is
identified in FIG. 1, the content delivery network 100 may include
any number of different communication networks between any of the
computer servers and devices shown in FIG. 1 and/or other devices
described herein. Communication networks 120 may enable
communication between the various computing devices, servers, and
other components of the content delivery network 100. As discussed
below, various implementations of data extraction and analysis
systems 100 may employ different types of networks 120, for
example, computer networks, telecommunications networks, wireless
networks, and/or any combination of these and/or other
networks.
[0051] In some embodiments, some of the components of the content
delivery network 100 can belong to the content network 122. The
content network 122 can include, for example, the content
management server 102, the database server 104, the privacy server
108, the content server 112, the data server 114, the administrator
server 116, and/or the communication network 120. The content
network 122 can be the source of content distributed by the content
delivery network 100, which content can include, for example, one
or several documents and/or applications or programs. These
documents and/or applications or programs are digital content. In
some embodiments, these one or several documents and/or
applications or programs can include, for example, one or several
webpages, presentations, papers, videos, charts, graphs, books,
written work, figures, images, graphics, recordings, applets,
scripts, or the like.
[0052] The content distribution network 100 may include one or
several navigation systems or features including, for example, the
Global Positioning System ("GPS"), GALILEO, or the like, or
location systems or features including, for example, one or several
transceivers that can determine location of the one or several
components of the content distribution network 100 via, for
example, triangulation. All of these are depicted as navigation
system 123.
[0053] In some embodiments, navigation system 123 can include or
several features that can communicate with one or several
components of the content distribution network 100 including, for
example, with one or several of the user devices 106 and/or with
one or several of the supervisor devices 110. In some embodiments,
this communication can include the transmission of a signal from
the navigation system 123 which signal is received by one or
several components of the content distribution network 100 and can
be used to determine the location of the one or several components
of the content distribution network 100.
[0054] With reference to FIG. 2, an illustrative distributed
computing environment 200 is shown including a computer server 202,
four client computing devices 206, and other components that may
implement certain embodiments and features described herein. In
some embodiments, the server 202 may correspond to the content
management server 102 discussed above in FIG. 1, and the client
computing devices 206 may correspond to the user devices 106.
However, the computing environment 200 illustrated in FIG. 2 may
correspond to any other combination of devices and servers
configured to implement a client-server model or other distributed
computing architecture.
[0055] Client devices 206 may be configured to receive and execute
client applications over one or more networks 220. Such client
applications may be web browser based applications and/or
standalone software applications, such as mobile device
applications. Server 202 may be communicatively coupled with the
client devices 206 via one or more communication networks 220.
Client devices 206 may receive client applications from server 202
or from other application providers (e.g., public or private
application stores). Server 202 may be configured to run one or
more server software applications or services, for example,
web-based or cloud-based services, to support content distribution
and interaction with client devices 206. Users operating client
devices 206 may in turn utilize one or more client applications
(e.g., virtual client applications) to interact with server 202 to
utilize the services provided by these components.
[0056] Various different subsystems and/or components 204 may be
implemented on server 202. Users operating the client devices 206
may initiate one or more client applications to use services
provided by these subsystems and components. The subsystems and
components within the server 202 and client devices 206 may be
implemented in hardware, firmware, software, or combinations
thereof. Various different system configurations are possible in
different distributed computing systems 200 and data extraction and
analysis systems 100. The embodiment shown in FIG. 2 is thus one
example of a distributed computing system and is not intended to be
limiting.
[0057] Although exemplary computing environment 200 is shown with
four client computing devices 206, any number of client computing
devices may be supported. Other devices, such as specialized sensor
devices, etc., may interact with client devices 206 and/or server
202.
[0058] As shown in FIG. 2, various security and integration
components 208 may be used to send and manage communications
between the server 202 and user devices 206 over one or more
communication networks 220. The security and integration components
208 may include separate servers, such as web servers and/or
authentication servers, and/or specialized networking components,
such as firewalls, routers, gateways, load balancers, and the like.
In some cases, the security and integration components 208 may
correspond to a set of dedicated hardware and/or software operating
at the same physical location and under the control of same
entities as server 202. For example, components 208 may include one
or more dedicated web servers and network hardware in a datacenter
or a cloud infrastructure. In other examples, the security and
integration components 208 may correspond to separate hardware and
software components which may be operated at a separate physical
location and/or by a separate entity.
[0059] Security and integration components 208 may implement
various security features for data transmission and storage, such
as authenticating users and restricting access to unknown or
unauthorized users. In various implementations, security and
integration components 208 may provide, for example, a file-based
integration scheme or a service-based integration scheme for
transmitting data between the various devices in the content
delivery network 100. Security and integration components 208 also
may use secure data transmission protocols and/or encryption for
data transfers, for example, File Transfer Protocol (FTP), Secure
File Transfer Protocol (SFTP), and/or Pretty Good Privacy (PGP)
encryption.
[0060] In some embodiments, one or more web services may be
implemented within the security and integration components 208
and/or elsewhere within the content delivery network 100. Such web
services, including cross-domain and/or cross-platform web
services, may be developed for enterprise use in accordance with
various web service standards, such as RESTful web services (i.e.,
services based on the Representation State Transfer (REST)
architectural style and constraints), and/or web services designed
in accordance with the Web Service Interoperability (WS-I)
guidelines. For example, some web services may use the Secure
Sockets Layer (SSL) or Transport Layer Security (TLS) protocol to
provide secure connections between the server 202 and user devices
206. SSL or TLS may use HTTP or HTTPS to provide authentication and
confidentiality. In other examples, web services may be implemented
using REST over HTTPS with the OAuth open standard for
authentication, or using the WS-Security standard, which provides
for secure SOAP messages using XML encryption. In other examples,
the security and integration components 208 may include specialized
hardware for providing secure web services. For example, security
and integration components 208 may include secure network
appliances having built-in features such as hardware-accelerated
SSL and HTTPS, WS-Security, and firewalls. Such specialized
hardware may be installed and configured in front of any web
servers, so that any external devices may communicate directly with
the specialized hardware.
[0061] Communication network(s) 220 may be any type of network
familiar to those skilled in the art that can support data
communications using any of a variety of commercially-available
protocols, including without limitation, TCP/IP (transmission
control protocol/Internet protocol), SNA (systems network
architecture), IPX (Internet packet exchange), Secure Sockets Layer
(SSL) or Transport Layer Security (TLS) protocols, Hyper Text
Transfer Protocol (HTTP) and Secure Hyper Text Transfer Protocol
(HTTPS), Bluetooth.RTM., Near Field Communication (NFC), and the
like. Merely by way of example, network(s) 220 may be local area
networks (LAN), such as one based on Ethernet, Token-Ring and/or
the like. Network(s) 220 also may be wide-area networks, such as
the Internet. Networks 220 may include telecommunication networks
such as a public switched telephone networks (PSTNs), or virtual
networks such as an intranet or an extranet. Infrared and wireless
networks (e.g., using the Institute of Electrical and Electronics
(IEEE) 802.11 protocol suite or other wireless protocols) also may
be included in networks 220.
[0062] Computing environment 200 also may include one or more
databases 210 and/or back-end servers 212. In certain examples, the
databases 210 may correspond to database server(s) 104, the local
data server 109, and/or the customizer data server 128 discussed
above in FIG. 1, and back-end servers 212 may correspond to the
various back-end servers 112-116. Databases 210 and servers 212 may
reside in the same datacenter or may operate at a remote location
from server 202. In some cases, one or more databases 210 may
reside on a non-transitory storage medium within the server 202.
Other databases 210 and back-end servers 212 may be remote from
server 202 and configured to communicate with server 202 via one or
more networks 220. In certain embodiments, databases 210 and
back-end servers 212 may reside in a storage-area network (SAN), or
may use storage-as-a-service (STaaS) architectural model. In some
embodiments, the computing environment can be replicated for each
of the networks 105, 122, 104 discussed with respect to FIG. 1
above.
[0063] With reference to FIG. 3, an illustrative set of databases
and/or database servers is shown, corresponding to the databases
servers 104 of the content delivery network 100 discussed above in
FIG. 1. One or more individual databases 301-310 may reside in
storage on a single computer server 104 (or a single server farm or
cluster) under the control of a single entity, or may reside on
separate servers operated by different entities and/or at remote
locations. In some embodiments, databases 301-310 may be accessed
by the content management server 102 and/or other devices and
servers within the network 100 (e.g., user devices 106, supervisor
devices 110, administrator servers 116, etc.). Access to one or
more of the databases 301-310 may be limited or denied based on the
processes, user credentials, and/or devices attempting to interact
with the database.
[0064] The paragraphs below describe examples of specific databases
that may be implemented within some embodiments of a content
delivery network 100. It should be understood that the below
descriptions of databases 301-310, including their functionality
and types of data stored therein, are illustrative and
non-limiting. Database server architecture, design, and the
execution of specific databases 301-310 may depend on the context,
size, and functional requirements of a content delivery network
100. For example, in content distribution systems 100 used for
professional training and educational purposes, separate databases
may be implemented in database server(s) 104 to store trainee
and/or student data, trainer and/or professor data, training module
data and content descriptions, training results, evaluation data,
and the like. In contrast, in content distribution systems 100 used
for media distribution from content providers to subscribers,
separate databases may be implemented in database server(s) 104 to
store listing of available content titles and descriptions, content
title usage statistics, subscriber profiles, account data, payment
data, network usage statistics, etc.
[0065] A user profile database 301 may include information relating
to the end users within the content delivery network 100. Generally
speaking the user profile database 301 can be a database having
restrictions on access, which restrictions can relate to whether
one or several users or categories of users are enabled to perform
one or several actions on the database or on data stored in the
database. In some embodiments, the user profile database 301 can
include any information for which access is restricted. This
information may include user characteristics such as the user
names, access credentials (e.g., logins and passwords), user
preferences, and information relating to any previous user
interactions within the content delivery network 100 (e.g.,
requested content, posted content, content modules completed,
training scores or evaluations, other associated users, etc.). In
some embodiments, this information can relate to one or several
individual end users such as, for example, one or several students,
teachers, administrators, or the like, and in some embodiments,
this information can relate to one or several institutional end
users such as, for example, one or several schools, groups of
schools such as one or several school districts, one or several
colleges, one or several universities, one or several training
providers, or the like. In some embodiments, this information can
identify one or several user memberships in one or several groups
such as, for example, a student's membership in a university,
school, program, grade, course, class, or the like.
[0066] In some embodiments, the user profile database 301 can
include information relating to a user's status, location, or the
like. This information can identify, for example, a device a user
is using, the location of that device, or the like. In some
embodiments, this information can be generated based on any
location detection technology including, for example, a navigation
system 122, or the like.
[0067] Information relating to the user's status can identify, for
example, logged-in status information that can indicate whether the
user is presently logged-in to the content distribution network 100
and/or whether the log-in-is active. In some embodiments, the
information relating to the user's status can identify whether the
user is currently accessing content and/or participating in an
activity from the content distribution network 100.
[0068] In some embodiments, information relating to the user's
status can identify, for example, one or several attributes of the
user's interaction with the content distribution network 100,
and/or content distributed by the content distribution network 100.
This can include data identifying the user's interactions with the
content distribution network 100, the content consumed by the user
through the content distribution network 100, or the like. In some
embodiments, this can include data identifying the type of
information accessed through the content distribution network 100
and/or the type of activity performed by the user via the content
distribution network 100, the lapsed time since the last time the
user accessed content and/or participated in an activity from the
content distribution network 100, or the like. In some embodiments,
this information can relate to a content program comprising an
aggregate of data, content, and/or activities, and can identify,
for example, progress through the content program, or through the
aggregate of data, content, and/or activities forming the content
program. In some embodiments, this information can track, for
example, the amount of time since participation in and/or
completion of one or several types of activities, the amount of
time since communication with one or several supervisors and/or
supervisor devices 110, or the like.
[0069] In some embodiments in which the one or several end users
are individuals, and specifically are students, the user profile
database 301 can further include information relating to these
students' academic and/or educational history. This information can
identify one or several courses of study that the student has
initiated, completed, and/or partially completed, as well as grades
received in those courses of study. In some embodiments, the
student's academic and/or educational history can further include
information identifying student performance on one or several
tests, quizzes, and/or assignments. In some embodiments, this
information can be stored in a tier of memory that is not the
fastest memory in the content delivery network 100.
[0070] The user profile database 301 can include information
relating to one or several student learning preferences. In some
embodiments, for example, the user, also referred to herein as the
student or the student-user may have one or several preferred
learning styles, one or several most effective learning styles,
and/or the like. In some embodiments, the students learning style
can be any learning style describing how the student best learns or
how the student prefers to learn. In one embodiment, these learning
styles can include, for example, identification of the student as
an auditory learner, as a visual learner, and/or as a tactile
learner. In some embodiments, the data identifying one or several
student learning styles can include data identifying a learning
style based on the student's educational history such as, for
example, identifying a student as an auditory learner when the
student has received significantly higher grades and/or scores on
assignments and/or in courses favorable to auditory learners. In
some embodiments, this information can be stored in a tier of
memory that is not the fastest memory in the content delivery
network 100.
[0071] In some embodiments, the user profile database 301 can
include information relating to one or several student-user
behaviours including, for example: attendance in one or several
courses; attendance and/or participation in one or several study
groups; extramural, student group, and/or club involve and/or
participation, or the like. In some embodiments, this information
relating to one or several student-user behaviours can include
information relating to the student-users schedule.
[0072] In some embodiments, the user profile database 301 can
include response data. In some embodiments, the response data can
include information relating to one or several actions taken by the
user including, for example, responses or comments by the user. In
some embodiments, the response data can store the response, store
information relating to the response such as, for example,
information indicating the substance, style, nature, and/or timing
of the response, or the like. In some embodiments, the response
data can further include one or several values indicating the
results of one or several evaluations of the response data. In some
embodiments, one of these values can include, for example, a
composite response score that will be discussed in greater detail
below.
[0073] The user profile database 301 can further include
information relating to one or several teachers and/or instructors
who are responsible for organizing, presenting, and/or managing the
presentation of information to the student. In some embodiments,
user profile database 301 can include information identifying
courses and/or subjects that have been taught by the teacher, data
identifying courses and/or subjects currently taught by the
teacher, and/or data identifying courses and/or subjects that will
be taught by the teacher. In some embodiments, this can include
information relating to one or several teaching styles of one or
several teachers. In some embodiments, the user profile database
301 can further include information indicating past evaluations
and/or evaluation reports received by the teacher. In some
embodiments, the user profile database 301 can further include
information relating to improvement suggestions received by the
teacher, training received by the teacher, continuing education
received by the teacher, and/or the like. In some embodiments, this
information can be stored in a tier of memory that is not the
fastest memory in the content delivery network 100.
[0074] An accounts database 302 may generate and store account data
for different users in various roles within the content delivery
network 100. For example, accounts may be created in an accounts
database 302 for individual end users, supervisors, administrator
users, and entities such as companies or educational institutions.
Account data may include account types, current account status,
account characteristics, and any parameters, limits, restrictions
associated with the accounts.
[0075] A content library database 303 may include information
describing the individual content items (or content resources)
available via the content delivery network 100. In some
embodiments, the library database 303 may include metadata,
properties, and other characteristics associated with the content
resources stored in the content server 112. In some embodiments,
this data can include the one or several items, also referred to
herein as content items that can include one or several documents
and/or one or several applications or programs. In some
embodiments, the one or several items can include, for example, one
or several webpages, presentations, papers, videos, charts, graphs,
books, written work, figures, images, graphics, recordings, or any
other document, or any desired software or application or component
thereof including, for example, a graphical user interface (GUI),
all or portions of a Learning Management System (LMS), all or
portions of a Content Management System (CMS), all or portions of a
Student Information Systems (SIS), or the like.
[0076] In some embodiments, the data in the content library
database 303 may identify one or more aspects or content attributes
of the associated content resources, for example, subject matter,
access level, or skill level of the content resources, license
attributes of the content resources (e.g., any limitations and/or
restrictions on the licensable use and/or distribution of the
content resource), price attributes of the content resources (e.g.,
a price and/or price structure for determining a payment amount for
use or distribution of the content resource), rating attributes for
the content resources (e.g., data indicating the evaluation or
effectiveness of the content resource), and the like. In some
embodiments, the library database 303 may be configured to allow
updating of content metadata or properties, and to allow the
addition and/or removal of information relating to the content
resources. In some embodiments, the content library database 303
can be organized such that content is associated with one or
several courses and/or programs in which the content is used and/or
provided. In some embodiments, the content library database 303 can
further include one or several teaching materials used in the
course, a syllabus, one or several practice problems, one or
several tests, one or several quizzes, one or several assignments,
or the like. All or portions of the content library database can be
stored in a tier of memory that is not the fastest memory in the
content delivery network 100.
[0077] In some embodiments, the content library database 303 can
comprise information to facilitate in authoring new content. This
information can comprise, for example, one or several
specifications identifying attributes and/or requirements of
desired newly authored content. In some embodiments, for example, a
content specification can identify one or several of a subject
matter; length, difficulty level, or the like for desired newly
authored content.
[0078] In some embodiments, the content library database 303 can
further include information for use in evaluating newly authored
content. In some embodiments, this evaluation can comprise a
determination of whether and/or the degree to which the newly
authored content corresponds to the content specification, or some
or all of the requirements of the content specification. In some
embodiments, this information for use in evaluation newly authored
content can identify or define one or several difficulty levels
and/or can identify or define one or several acceptable difficulty
levels. In some embodiments, for example, this information for use
in evaluation newly authored content can define a plurality of
difficulty levels and can delineate between these difficulty
levels, and in some embodiments, this information for use in
evaluation newly authored content can identify which of the defined
difficulty levels are acceptable. In other embodiments, this
information for use in evaluation newly authored content can merely
include one or several definitions of acceptable difficulty levels,
which acceptable difficulty level can be based on one or several
pre-existing difficult measures such as, for example, an Item
Response Theory (IRT) value such as, for example, an IRT b value, a
p value indicative of the proportion of correct responses in a set
of responses, a grade level, or the like.
[0079] In some embodiments, this information for use in evaluation
newly authored content can further define one or several
differentiation and/or discrimination levels and/or define one or
several acceptable differentiation and/or discrimination levels or
ranges. As used herein, "differentiation" and "discrimination"
refer to the degree to which an item such as a question identifies
low ability versus high ability users. In some embodiments, this
information for use in evaluation newly authored content can
identify one or several acceptable levels and/or ranges of
discrimination which levels and/or ranges can be based on one or
several currently existing discrimination measures such as, for
example, a Point-Biserial Correlation.
[0080] A pricing database 304 may include pricing information
and/or pricing structures for determining payment amounts for
providing access to the content delivery network 100 and/or the
individual content resources within the network 100. In some cases,
pricing may be determined based on a user's access to the content
delivery network 100, for example, a time-based subscription fee,
or pricing based on network usage and. In other cases, pricing may
be tied to specific content resources. Certain content resources
may have associated pricing information, whereas other pricing
determinations may be based on the resources accessed, the profiles
and/or accounts of the users and the desired level of access (e.g.,
duration of access, network speed, etc.). Additionally, the pricing
database 304 may include information relating to compilation
pricing for groups of content resources, such as group prices
and/or price structures for groupings of resources.
[0081] A license database 305 may include information relating to
licenses and/or licensing of the content resources within the
content delivery network 100. For example, the license database 305
may identify licenses and licensing terms for individual content
resources and/or compilations of content resources in the content
server 112, the rights holders for the content resources, and/or
common or large-scale right holder information such as contact
information for rights holders of content not included in the
content server 112.
[0082] A content access database 306 may include access rights and
security information for the content delivery network 100 and
specific content resources. For example, the content access
database 306 may include login information (e.g., user identifiers,
logins, passwords, etc.) that can be verified during user login
attempts to the network 100. The content access database 306 also
may be used to store assigned roles and/or levels of access to
users. For example, a user's access level may correspond to the
sets of content resources and/or the client or server applications
that the user is permitted to access. Certain users may be
permitted or denied access to certain applications and resources
based on their subscription level, training program, course/grade
level, etc. Certain users may have supervisory access over one or
more end users, allowing the supervisor to access all or portions
of the end user's content, activities, evaluations, etc.
Additionally, certain users may have administrative access over
some users and/or some applications in the content management
network 100, allowing such users to add and remove user accounts,
modify user access permissions, perform maintenance updates on
software and servers, etc.
[0083] An aggregate database 307 can include information gathered
from one or several sources, also referred to herein as data
sources relating to one or several students. In some embodiments,
the information within the aggregate database 307 can be organized
by student and/or retrievable by student such that all of the
information relating to a student can be accessed and retrieved.
This information can be received from one or more data sources
which can include one or more data sources controlled by the
content management server 102 or can include one or more data
sources that are not controlled by the content management server
102 but can, in some embodiments, be controlled by a third party.
In some embodiments, these data sources can include, for example,
one or more of the content server 112, the data servers 114, the
administrator server 116, or the like. In some embodiments, one or
more of these data sources can include, for example, a Learning
Management System (LMS) including: information relating to
documents accessed by a student; tasks started and/or completed by
a student; correct or incorrect answers or responses by a student;
student work product; or the like, a Student Information System
(SIS), a gradebook including an electronic gradebook, a library
and/or a networked system of a library, an advisory system, an
instructor feedback system whereby an instructor can, for example,
flag a student, one or several learning applications, student
location and/or proximity information, course attendance
information, or the like.
[0084] In some embodiments, this information that is received from
the one or several data sources can be structured or unstructured.
In some embodiments in which the received data is structured, the
received data can be provided by one or several applications
operating on the one or more data sources and/or by accessing data
from the one or more data sources. Such applications can be
configured, for example, to identify and/or retrieve desired
information, to, if desired, reformat and/or restructure the data,
and/or to provide the data to the content management server 102. In
some embodiments, the data from the data sources can be
restructured and/or reformatted by the content management server
102 before storing in the aggregate database 307.
[0085] A prediction database 308 can include information used in
generating an outcome prediction for a user such as a student. In
some embodiments, for example, the prediction database 308 can
include information creating one or several predictive models used
in predicting a user outcome and/or can include information for use
in creating one or several predictive models for predicting a user
outcome. In some embodiments, this information can be data
collected from users of the data sources and/or from the outcomes
achieved by those users. In some embodiments these users can be
past or present users of the data sources.
[0086] An intervention database 309 can include information used to
generate an intervention recommendation. In some embodiments, the
intervention recommendation can comprise one or several actions to
be taken by a computer, the content management server 102, and/or
by a supervisor of the user-student to mitigate the risk of a
predicted adverse outcome and/or to increase the likelihood of a
predicted positive outcome. These interventions can include, for
example, life coaching or assistance, lifestyle coaching or
assistance, social or cultural coaching or assistance, habit
counseling or assistance, work habit coaching or assistance,
academic coaching or assistance, or any other form of coaching,
counseling, support, or assistance. In some embodiments, an
intervention can include, for example: placement in a study group;
placement with a mentor, friend, or coach; tutoring; remedial tasks
or assignments; work-habit or work-skill training; follow-up
contact such as a text, email, or call; or the like.
[0087] In some embodiments, the intervention database 309 can
include an intervention model that can be used in generating and/or
identifying an intervention for providing to the user. This
intervention model can be, for example, a statistical model that
can be dynamic and adaptive based on the results of past
interventions. In some embodiments, the intervention model can be
adaptive to identify the best and/or worst interventions, to
identify the best and/or worst interventions for a student or
student type, or the like.
[0088] In addition to the illustrative databases described above,
database server(s) 104 may include one or more external data
aggregators 310. External data aggregators 310 may include
third-party data sources accessible to the content management
network 100, but not maintained by the content management network
100. External data aggregators 310 may include any electronic
information source relating to the users, content resources, or
applications of the content delivery network 100. For example,
external data aggregators 310 may be third-party databases
containing demographic data, education related data, consumer sales
data, health related data, and the like. Illustrative external data
aggregators 310 may include, for example, social networking web
servers, public records databases, learning management systems,
educational institution servers, business servers, consumer sales
databases, medical record databases, etc. Data retrieved from
various external data aggregators 310 may be used to verify and
update user account information, suggest user content, and perform
user and content evaluations.
[0089] A course database 311 can include information relating to a
course of study, a group of courses, or a program that can include,
for example, the collection of courses making up a degree, a grade,
or the like. This information can include, for example, educational
material, user performance indicator data such as, for example,
grade data for users participating in the course of study, or data
identifying the users participating in the course of study.
[0090] In some embodiments, the course database 311 can further
include information on groups which can be formal and/or informal,
or communities. In some embodiments, these groups and/or
communities can exist within a course, a group of courses, and/or a
program or course of study, and in some embodiments, these groups
and/or communities can exist across courses, groups of courses,
and/or across programs or courses of study, and can be related
and/or unrelated to a course, a group of courses, and/or a program
or course of study. Advantageously, the groups and/or communities
can facilitate education by encouraging the transfer of ideas
between individuals.
[0091] In some embodiments, the course database 311 can further
include educational activities and information regarding the design
of those activities. Advantageously, the inclusion of education
activities and information regarding the design of those activities
can facilitate the generation of understanding of the
student-teacher relationship and/or interactions and can be used,
for example, to compare and/or predict outcomes.
[0092] An outcome database 312 can include information relating to
one or several outcomes. This information can include, for example,
the identification of one or several outcomes, and/or relationships
between the one or several outcomes. The outcome information can be
input into the CDN 100 via one or several users and/or can be
imported from another system associated with the CDN 100.
[0093] A typology database 313 can include information relating to
one or several typologies. This information can include, for
example, data identifying characteristics of the typology, criteria
for the typology, behaviors associated with the typology, other
related typologies, and/or outcomes associated with the typology.
In some embodiments, for example, the typology database 313 can
include criteria, also referred to herein as classification data,
for use in analyzing user data to determine the user's typology. In
some embodiments, for example, these criteria can allow the binary
classification of the user's typology, or the indication of the
degree of the user's demonstration of the typology. Thus, these
criteria can be used to classify the user as exhibiting the
typology and/or to indicate the degree to which the user exhibits
the typology.
[0094] With reference now to FIG. 4A, a block diagram is shown
illustrating an embodiment of one or more content management
servers 102 within a content delivery network 100. As discussed
above, content management server(s) 102 may include various server
hardware and software components that manage the content resources
within the content delivery network 100 and provide interactive and
adaptive content to users on various user devices 106. For example,
content management server(s) 102 may provide instructions to and
receive information from the other devices within the content
delivery network 100, in order to manage and transmit content
resources, user data, and server or client applications executing
within the network 100.
[0095] A content management server 102 may include a content
customization system 402. The content customization system 402 may
be implemented using dedicated hardware within the content delivery
network 100 (e.g., a content customization server 402), or using
designated hardware and software resources within a shared content
management server 102. In some embodiments, the content
customization system 402 may adjust the selection and adaptive
capabilities of content resources to match the needs and desires of
the users receiving the content. For example, the content
customization system 402 may query various databases and servers
104 to retrieve user information, such as user preferences and
characteristics (e.g., from a user profile database 301), user
access restrictions to content recourses (e.g., from a content
access database 306), and the like. Based on the retrieved
information from databases 104 and other data sources, the content
customization system 402 may modify content resources for
individual users.
[0096] In some embodiments, the content management system 402 can
include a recommendation engine, also referred to herein as an
adaptive recommendation engine. In some embodiments, the
recommendation engine can select one or several pieces of content,
also referred to herein as data packets, for providing to a user.
These data packets can be selected based on, for example, the
information retrieved from the database server 104 including, for
example, the user profile database 301, the content library
database 303, the model database 309, or the like. In one
embodiment, for example, the recommendation engine can retrieve
information from the user profile database 301 identifying, for
example, a skill level of the user. The recommendation engine can
further retrieve information from the content library database 303
identifying, for example, potential data packets for providing to
the user and the difficulty of those data packets and/or the skill
level associated with those data packets.
[0097] The recommendation engine can use the evidence model to
generate a prediction of the likelihood of one or several users
providing a desired response to some or all of the potential data
packets. In some embodiments, the recommendation engine can pair
one or several data packets with selection criteria that may be
used to determine which packet should be delivered to a
student-user based on one or several received responses from that
student-user. In some embodiments, one or several data packets can
be eliminated from the pool of potential data packets if the
prediction indicates either too high a likelihood of a desired
response or too low a likelihood of a desired response. In some
embodiments, the recommendation engine can then apply one or
several selection criteria to the remaining potential data packets
to select a data packet for providing to the user. These one or
several selection criteria can be based on, for example, criteria
relating to a desired estimated time for receipt of response to the
data packet, one or several content parameters, one or several
assignment parameters, or the like.
[0098] A content management server 102 also may include a user
management system 404. The user management system 404 may be
implemented using dedicated hardware within the content delivery
network 100 (e.g., a user management server 404), or using
designated hardware and software resources within a shared content
management server 102. In some embodiments, the user management
system 404 may monitor the progress of users through various types
of content resources and groups, such as media compilations,
courses or curriculums in training contexts, interactive gaming
environments, and the like. For example, the user management system
404 may query one or more databases and servers 104 to retrieve
user data such as associated content compilations or programs,
content completion status, user goals, results, and the like.
[0099] A content management server 102 also may include an
evaluation system 406. The evaluation system 406 may be implemented
using dedicated hardware within the content delivery network 100
(e.g., an evaluation server 406), or using designated hardware and
software resources within a shared content management server 102.
The evaluation system 406 may be configured to receive and analyze
information from user devices 106 via, for example, the end-user
server 107. For example, various ratings of content resources
submitted by users may be compiled and analyzed, and then stored in
a database (e.g., a content library database 303) associated with
the content. In some embodiments, the evaluation server 406 may
analyze the information to determine the effectiveness or
appropriateness of content resources with, for example, a subject
matter, an age group, a skill level, or the like. In some
embodiments, the evaluation system 406 may provide updates to the
content customization system 402 or the user management system 404,
with the attributes of one or more content resources or groups of
resources within the network 100. The evaluation system 406 also
may receive and analyze user evaluation data from user devices 106,
supervisor devices 110, and administrator servers 116, etc. For
instance, evaluation system 406 may receive, aggregate, and analyze
user evaluation data for different types of users (e.g., end users,
supervisors, administrators, etc.) in different contexts (e.g.,
media consumer ratings, trainee or student comprehension levels,
teacher effectiveness levels, gamer skill levels, etc.).
[0100] In some embodiments, the evaluation system 406 can be
further configured to receive one or several responses from the
user and to determine whether the one or several response are
correct responses, also referred to herein as desired responses, or
are incorrect responses, also referred to herein as undesired
responses. In some embodiments, one or several values can be
generated by the evaluation system 406 to reflect user performance
in responding to the one or several data packets. In some
embodiments, these one or several values can comprise one or
several scores for one or several responses and/or data
packets.
[0101] A content management server 102 also may include a content
delivery system 408. The content delivery system 408 may be
implemented using dedicated hardware within the content delivery
network 100 (e.g., a content delivery server 408), or using
designated hardware and software resources within a shared content
management server 102. The content delivery system 408 can include
a presentation engine that can be, for example, a software module
running on the content delivery system.
[0102] The content delivery system 408, also referred to herein as
the presentation module or the presentation engine, may receive
content resources from the content customization system 402 and/or
from the user management system 404, and provide the resources to
user devices 106. The content delivery system 408 may determine the
appropriate presentation format for the content resources based on
the user characteristics and preferences, and/or the device
capabilities of user devices 106. If needed, the content delivery
system 408 may convert the content resources to the appropriate
presentation format and/or compress the content before
transmission. In some embodiments, the content delivery system 408
may also determine the appropriate transmission media and
communication protocols for transmission of the content
resources.
[0103] In some embodiments, the content delivery system 408 may
include specialized security and integration hardware 410, along
with corresponding software components to implement the appropriate
security features content transmission and storage, to provide the
supported network and client access models, and to support the
performance and scalability requirements of the network 100. The
security and integration layer 410 may include some or all of the
security and integration components 208 discussed above in FIG. 2,
and may control the transmission of content resources and other
data, as well as the receipt of requests and content interactions,
to and from the user devices 106, supervisor devices 110,
administrative servers 116, and other devices in the network
100.
[0104] With reference now to FIG. 4B, a flowchart illustrating one
embodiment of a process 440 for data management is shown. In some
embodiments, the process 440 can be performed by the content
management server 102, and more specifically by the content
delivery system 408 and/or by the presentation module or
presentation engine. The process 440 begins at block 442, wherein a
data packet is identified. In some embodiments, the data packet can
be a data packet for providing to a student-user, and the data
packet can be identified by determining which data packet to next
provide to the user such as the student-user. In some embodiments,
this determination can be performed by the content customization
system 402 and/or the recommendation engine.
[0105] After the data packet has been identified, the process 440
proceeds to block 444, wherein the data packet is requested. In
some embodiments, this can include the requesting of information
relating to the data packet such as the data forming the data
packet. In some embodiments, this information can be requested
from, for example, the content library database 303. After the data
packet has been requested, the process 440 proceeds to block 446,
wherein the data packet is received. In some embodiments, the data
packet can be received by the content delivery system 408 from, for
example, the content library database 303.
[0106] After the data packet has been received, the process 440
proceeds to block 448, wherein one or several data components are
identified. In some embodiments, for example, the data packet can
include one or several data components which can, for example,
contain different data. In some embodiments, one of these data
components, referred to herein as a presentation component, can
include content for providing to the student user, which content
can include one or several requests and/or questions and/or the
like. In some embodiments, one of these data components, referred
to herein as a response component, can include data used in
evaluating one or several responses received from the user device
106 in response to the data packet, and specifically in response to
the presentation component and/or the one or several requests
and/or questions of the presentation component. Thus, in some
embodiments, the response component of the data packet can be used
to ascertain whether the user has provided a desired response or an
undesired response.
[0107] After the data components have been identified, the process
440 proceeds to block 450, wherein a delivery data packet is
identified. In some embodiments, the delivery data packet can
include the one or several data components of the data packets for
delivery to a user such as the student-user via the user device
106. In some embodiments, the delivery packet can include the
presentation component, and in some embodiments, the delivery
packet can exclude the response packet. After the delivery data
packet has been generated, the process 440 proceeds to block 452,
wherein the delivery data packet is presented to the user device
106. In some embodiments, this can include providing the delivery
data packet to the user device 106 via, for example, the
communication network 120.
[0108] After the delivery data packet has been provided to the user
device, the process 440 proceeds to block 454, wherein the data
packet and/or one or several components thereof is sent to and/or
provided to the response processor. In some embodiments, this
sending of the data packet and/or one or several components thereof
to the response processor can include receiving a response from the
student-user, and sending the response to the student-user to the
response processor simultaneous with the sending of the data packet
and/or one or several components thereof to the response processor.
In some embodiments, for example, this can include providing the
response component to the response processor. In some embodiments,
the response component can be provided to the response processor
from the content delivery system 408.
[0109] With reference now to FIG. 4C, a flowchart illustrating one
embodiment of a process 460 for evaluating a response is shown. In
some embodiments, the process can be performed by the evaluation
system 406. In some embodiments, the process 460 can be performed
by the evaluation system 406 in response to the receipt of a
response from the user device 106.
[0110] The process 460 begins at block 462, wherein a response is
received from, for example, the user device 106 via, for example,
the communication network 120. After the response has been
received, the process 460 proceeds to block 464, wherein the data
packet associated with the response is received. In some
embodiments, this can include receiving all or one or several
components of the data packet such as, for example, the response
component of the data packet. In some embodiments, the data packet
can be received by the response processor from the presentation
engine.
[0111] After the data packet has been received, the process 460
proceeds to block 466, wherein the response type is identified. In
some embodiments, this identification can be performed based on
data, such as metadata associated with the response. In other
embodiments, this identification can be performed based on data
packet information such as the response component.
[0112] In some embodiments, the response type can identify one or
several attributes of the one or several requests and/or questions
of the data packet such as, for example, the request and/or
question type. In some embodiments, this can include identifying
some or all of the one or several requests and/or questions as
true/false, multiple choice, short answer, essay, or the like.
[0113] After the response type has been identified, the process 460
proceeds to block 468, wherein the data packet and the response are
compared to determine whether the response comprises a desired
response and/or an undesired response. In some embodiments, this
can include comparing the received response and the data packet to
determine if the received response matches all or portions of the
response component of the data packet, to determine the degree to
which the received response matches all or portions of the response
component, to determine the degree to which the receive response
embodies one or several qualities identified in the response
component of the data packet, or the like. In some embodiments,
this can include classifying the response according to one or
several rules. In some embodiments, these rules can be used to
classify the response as either desired or undesired. In some
embodiments, these rules can be used to identify one or several
errors and/or misconceptions evidenced in the response. In some
embodiments, this can include, for example: use of natural language
processing software and/or algorithms; use of one or several
digital thesauruses; use of lemmatization software, dictionaries,
and/or algorithms; or the like.
[0114] After the data packet and the response have been compared,
the process 460 proceeds to block 470 wherein response desirability
is determined. In some embodiments this can include, based on the
result of the comparison of the data packet and the response,
whether the response is a desired response or is an undesired
response. In some embodiments, this can further include quantifying
the degree to which the response is a desired response. This
determination can include, for example, determining if the response
is a correct response, an incorrect response, a partially correct
response, or the like. In some embodiments, the determination of
response desirability can include the generation of a value
characterizing the response desirability and the storing of this
value in one of the databases 104 such as, for example, the user
profile database 301. After the response desirability has been
determined, the process 460 proceeds to block 472, wherein an
assessment value is generated. In some embodiments, the assessment
value can be an aggregate value characterizing response
desirability for one or more a plurality of responses. This
assessment value can be stored in one of the databases 104 such as
the user profile database 301.
[0115] With reference now to FIG. 5, a block diagram of an
illustrative computer system is shown. The system 500 may
correspond to any of the computing devices or servers of the
content delivery network 100 described above, or any other
computing devices described herein, and specifically can include,
for example, one or several of the user devices 106, the supervisor
device 110, and/or any of the servers 102, 104, 108, 112, 114, 116.
In this example, computer system 500 includes processing units 504
that communicate with a number of peripheral subsystems via a bus
subsystem 502. These peripheral subsystems include, for example, a
storage subsystem 510, an I/O subsystem 526, and a communications
subsystem 532.
[0116] Bus subsystem 502 provides a mechanism for letting the
various components and subsystems of computer system 500
communicate with each other as intended. Although bus subsystem 502
is shown schematically as a single bus, alternative embodiments of
the bus subsystem may utilize multiple buses. Bus subsystem 502 may
be any of several types of bus structures including a memory bus or
memory controller, a peripheral bus, and a local bus using any of a
variety of bus architectures. Such architectures may include, for
example, an Industry Standard Architecture (ISA) bus, Micro Channel
Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics
Standards Association (VESA) local bus, and Peripheral Component
Interconnect (PCI) bus, which can be implemented as a Mezzanine bus
manufactured to the IEEE P1386.1 standard.
[0117] Processing unit 504, which may be implemented as one or more
integrated circuits (e.g., a conventional microprocessor or
microcontroller), controls the operation of computer system 500.
One or more processors, including single core and/or multicore
processors, may be included in processing unit 504. As shown in the
figure, processing unit 504 may be implemented as one or more
independent processing units 506 and/or 508 with single or
multicore processors and processor caches included in each
processing unit. In other embodiments, processing unit 504 may also
be implemented as a quad-core processing unit or larger multicore
designs (e.g., hexa-core processors, octo-core processors, ten-core
processors, or greater.
[0118] Processing unit 504 may execute a variety of software
processes embodied in program code, and may maintain multiple
concurrently executing programs or processes. At any given time,
some or all of the program code to be executed can be resident in
processor(s) 504 and/or in storage subsystem 510. In some
embodiments, computer system 500 may include one or more
specialized processors, such as digital signal processors (DSPs),
outboard processors, graphics processors, application-specific
processors, and/or the like.
[0119] I/O subsystem 526 may include device controllers 528 for one
or more user interface input devices and/or user interface output
devices 530. User interface input and output devices 530 may be
integral with the computer system 500 (e.g., integrated audio/video
systems, and/or touchscreen displays), or may be separate
peripheral devices which are attachable/detachable from the
computer system 500. The I/O subsystem 526 may provide one or
several outputs to a user by converting one or several electrical
signals to user perceptible and/or interpretable form, and may
receive one or several inputs from the user by generating one or
several electrical signals based on one or several user-caused
interactions with the I/O subsystem such as the depressing of a key
or button, the moving of a mouse, the interaction with a
touchscreen or trackpad, the interaction of a sound wave with a
microphone, or the like.
[0120] Input devices 530 may include a keyboard, pointing devices
such as a mouse or trackball, a touchpad or touch screen
incorporated into a display, a scroll wheel, a click wheel, a dial,
a button, a switch, a keypad, audio input devices with voice
command recognition systems, microphones, and other types of input
devices. Input devices 530 may also include three dimensional (3D)
mice, joysticks or pointing sticks, gamepads and graphic tablets,
and audio/visual devices such as speakers, digital cameras, digital
camcorders, portable media players, webcams, image scanners,
fingerprint scanners, barcode reader 3D scanners, 3D printers,
laser rangefinders, and eye gaze tracking devices. Additional input
devices 530 may include, for example, motion sensing and/or gesture
recognition devices that enable users to control and interact with
an input device through a natural user interface using gestures and
spoken commands, eye gesture recognition devices that detect eye
activity from users and transform the eye gestures as input into an
input device, voice recognition sensing devices that enable users
to interact with voice recognition systems through voice commands,
medical imaging input devices, MIDI keyboards, digital musical
instruments, and the like.
[0121] Output devices 530 may include one or more display
subsystems, indicator lights, or non-visual displays such as audio
output devices, etc. Display subsystems may include, for example,
cathode ray tube (CRT) displays, flat-panel devices, such as those
using a liquid crystal display (LCD) or plasma display,
light-emitting diode (LED) displays, projection devices, touch
screens, and the like. In general, use of the term "output device"
is intended to include all possible types of devices and mechanisms
for outputting information from computer system 500 to a user or
other computer. For example, output devices 530 may include,
without limitation, a variety of display devices that visually
convey text, graphics and audio/video information such as monitors,
printers, speakers, headphones, automotive navigation systems,
plotters, voice output devices, and modems.
[0122] Computer system 500 may comprise one or more storage
subsystems 510, comprising hardware and software components used
for storing data and program instructions, such as system memory
518 and computer-readable storage media 516. The system memory 518
and/or computer-readable storage media 516 may store program
instructions that are loadable and executable on processing units
504, as well as data generated during the execution of these
programs.
[0123] Depending on the configuration and type of computer system
500, system memory 318 may be stored in volatile memory (such as
random access memory (RAM) 512) and/or in non-volatile storage
drives 514 (such as read-only memory (ROM), flash memory, etc.) The
RAM 512 may contain data and/or program modules that are
immediately accessible to and/or presently being operated and
executed by processing units 504. In some implementations, system
memory 518 may include multiple different types of memory, such as
static random access memory (SRAM) or dynamic random access memory
(DRAM). In some implementations, a basic input/output system
(BIOS), containing the basic routines that help to transfer
information between elements within computer system 500, such as
during start-up, may typically be stored in the non-volatile
storage drives 514. By way of example, and not limitation, system
memory 518 may include application programs 520, such as client
applications, Web browsers, mid-tier applications, server
applications, etc., program data 522, and an operating system
524.
[0124] Storage subsystem 510 also may provide one or more tangible
computer-readable storage media 516 for storing the basic
programming and data constructs that provide the functionality of
some embodiments. Software (programs, code modules, instructions)
that when executed by a processor provide the functionality
described herein may be stored in storage subsystem 510. These
software modules or instructions may be executed by processing
units 504. Storage subsystem 510 may also provide a repository for
storing data used in accordance with the present invention.
[0125] Storage subsystem 300 may also include a computer-readable
storage media reader that can further be connected to
computer-readable storage media 516. Together and, optionally, in
combination with system memory 518, computer-readable storage media
516 may comprehensively represent remote, local, fixed, and/or
removable storage devices plus storage media for temporarily and/or
more permanently containing, storing, transmitting, and retrieving
computer-readable information.
[0126] Computer-readable storage media 516 containing program code,
or portions of program code, may include any appropriate media
known or used in the art, including storage media and communication
media, such as but not limited to, volatile and non-volatile,
removable and non-removable media implemented in any method or
technology for storage and/or transmission of information. This can
include tangible computer-readable storage media such as RAM, ROM,
electronically erasable programmable ROM (EEPROM), flash memory or
other memory technology, CD-ROM, digital versatile disk (DVD), or
other optical storage, magnetic cassettes, magnetic tape, magnetic
disk storage or other magnetic storage devices, or other tangible
computer readable media. This can also include nontangible
computer-readable media, such as data signals, data transmissions,
or any other medium which can be used to transmit the desired
information and which can be accessed by computer system 500.
[0127] By way of example, computer-readable storage media 516 may
include a hard disk drive that reads from or writes to
non-removable, nonvolatile magnetic media, a magnetic disk drive
that reads from or writes to a removable, nonvolatile magnetic
disk, and an optical disk drive that reads from or writes to a
removable, nonvolatile optical disk such as a CD ROM, DVD, and
Blu-Ray.RTM. disk, or other optical media. Computer-readable
storage media 516 may include, but is not limited to, Zip.RTM.
drives, flash memory cards, universal serial bus (USB) flash
drives, secure digital (SD) cards, DVD disks, digital video tape,
and the like. Computer-readable storage media 516 may also include,
solid-state drives (SSD) based on non-volatile memory such as
flash-memory based SSDs, enterprise flash drives, solid state ROM,
and the like, SSDs based on volatile memory such as solid state
RAM, dynamic RAM, static RAM, DRAM-based SSDs, magnetoresistive RAM
(MRAM) SSDs, and hybrid SSDs that use a combination of DRAM and
flash memory based SSDs. The disk drives and their associated
computer-readable media may provide non-volatile storage of
computer-readable instructions, data structures, program modules,
and other data for computer system 500.
[0128] Communications subsystem 532 may provide a communication
interface from computer system 500 and external computing devices
via one or more communication networks, including local area
networks (LANs), wide area networks (WANs) (e.g., the Internet),
and various wireless telecommunications networks. As illustrated in
FIG. 5, the communications subsystem 532 may include, for example,
one or more network interface controllers (NICs) 534, such as
Ethernet cards, Asynchronous Transfer Mode NICs, Token Ring NICs,
and the like, as well as one or more wireless communications
interfaces 536, such as wireless network interface controllers
(WNICs), wireless network adapters, and the like. As illustrated in
FIG. 5, the communications subsystem 532 may include, for example,
one or more location determining features 538 such as one or
several navigation system features and/or receivers, and the like.
Additionally and/or alternatively, the communications subsystem 532
may include one or more modems (telephone, satellite, cable, ISDN),
synchronous or asynchronous digital subscriber line (DSL) units,
FireWire.RTM. interfaces, USB.RTM. interfaces, and the like.
Communications subsystem 536 also may include radio frequency (RF)
transceiver components for accessing wireless voice and/or data
networks (e.g., using cellular telephone technology, advanced data
network technology, such as 3G, 4G or EDGE (enhanced data rates for
global evolution), WiFi (IEEE 802.11 family standards, or other
mobile communication technologies, or any combination thereof),
global positioning system (GPS) receiver components, and/or other
components.
[0129] The various physical components of the communications
subsystem 532 may be detachable components coupled to the computer
system 500 via a computer network, a FireWire.RTM. bus, or the
like, and/or may be physically integrated onto a motherboard of the
computer system 500. Communications subsystem 532 also may be
implemented in whole or in part by software.
[0130] In some embodiments, communications subsystem 532 may also
receive input communication in the form of structured and/or
unstructured data feeds, event streams, event updates, and the
like, on behalf of one or more users who may use or access computer
system 500. For example, communications subsystem 532 may be
configured to receive data feeds in real-time from users of social
networks and/or other communication services, web feeds such as
Rich Site Summary (RSS) feeds, and/or real-time updates from one or
more third party information sources (e.g., data aggregators 310).
Additionally, communications subsystem 532 may be configured to
receive data in the form of continuous data streams, which may
include event streams of real-time events and/or event updates
(e.g., sensor data applications, financial tickers, network
performance measuring tools, clickstream analysis tools, automobile
traffic monitoring, etc.). Communications subsystem 532 may output
such structured and/or unstructured data feeds, event streams,
event updates, and the like to one or more databases 104 that may
be in communication with one or more streaming data source
computers coupled to computer system 500.
[0131] Due to the ever-changing nature of computers and networks,
the description of computer system 500 depicted in the figure is
intended only as a specific example. Many other configurations
having more or fewer components than the system depicted in the
figure are possible. For example, customized hardware might also be
used and/or particular elements might be implemented in hardware,
firmware, software, or a combination. Further, connection to other
computing devices, such as network input/output devices, may be
employed. Based on the disclosure and teachings provided herein, a
person of ordinary skill in the art will appreciate other ways
and/or methods to implement the various embodiments.
[0132] With reference now to FIG. 6, a block diagram illustrating
one embodiment of the communication network is shown. Specifically,
FIG. 6 depicts one hardware configuration in which messages are
exchanged between a source hub 652 via the communication network
120 that can include one or several intermediate hubs 654. In some
embodiments, the source hub 652 can be any one or several
components of the content distribution network generating and
initiating the sending of a message, and the terminal hub 656 can
be any one or several components of the content distribution
network 100 receiving and not re-sending the message. In some
embodiments, for example, the source hub 652 can be one or several
of the user device 106, the supervisor device 110, and/or the
server 102, and the terminal hub 656 can likewise be one or several
of the user device 106, the supervisor device 110, and/or the
server 102. In some embodiments, the intermediate hubs 654 can
include any computing device that receives the message and resends
the message to a next node.
[0133] As seen in FIG. 6, in some embodiments, each of the hubs
652, 654, 656 can be communicatingly connected with the data store
104. In such an embodiments, some or all of the hubs 652, 654, 656
can send information to the data store 104 identifying a received
message and/or any sent or resent message. This information can, in
some embodiments, be used to determine the completeness of any sent
and/or received messages and/or to verify the accuracy and
completeness of any message received by the terminal hub 656.
[0134] In some embodiments, the communication network 120 can be
formed by the intermediate hubs 654. In some embodiments, the
communication network 120 can comprise a single intermediate hub
654, and in some embodiments, the communication network 120 can
comprise a plurality of intermediate hubs. In one embodiment, for
example, and as depicted in FIG. 6, the communication network 120
includes a first intermediate hub 654-A and a second intermediate
hub 654-B.
[0135] With reference now to FIG. 7, a block diagram illustrating
one embodiment of user device 106 and supervisor device 110
communication is shown. In some embodiments, for example, a user
may have multiple devices that can connect with the content
distribution network 100 to send or receive information. In some
embodiments, for example, a user may have a personal device such as
a mobile device, a Smartphone, a tablet, a Smartwatch, a laptop, a
PC, or the like. In some embodiments, the other device can be any
computing device in addition to the personal device. This other
device can include, for example, a laptop, a PC, a Smartphone, a
tablet, a Smartwatch, or the like. In some embodiments, the other
device differs from the personal device in that the personal device
is registered as such within the content distribution network 100
and the other device is not registered as a personal device within
the content distribution network 100.
[0136] Specifically with respect to FIG. 7, the user device 106 can
include a personal user device 106-A and one or several other user
devices 106-B. In some embodiments, one or both of the personal
user device 106-A and the one or several other user devices 106-B
can be communicatingly connected to the content management server
102 and/or to the navigation system 122. Similarly, the supervisor
device 110 can include a personal supervisor device 110-A and one
or several other supervisor devices 110-B. In some embodiments, one
or both of the personal supervisor device 110-A and the one or
several other supervisor devices 110-B can be communicatingly
connected to the content management server 102 and/or to the
navigation system 122.
[0137] In some embodiments, the content distribution network can
send one or more alerts to one or more user devices 106 and/or one
or more supervisor devices 110 via, for example, the communication
network 120. In some embodiments, the receipt of the alert can
result in the launching of an application within the receiving
device, and in some embodiments, the alert can include a link that,
when selected, launches the application or navigates a web-browser
of the device of the selector of the link to page or portal
associated with the alert.
[0138] In some embodiments, for example, the providing of this
alert can include the identification of one or several user devices
106 and/or student-user accounts associated with the student-user
and/or one or several supervisor devices 110 and/or supervisor-user
accounts associated with the supervisor-user. After these one or
several devices 106, 110 and/or accounts have been identified, the
providing of this alert can include determining an active device of
the devices 106, 110 based on determining which of the devices 106,
110 and/or accounts are actively being used, and then providing the
alert to that active device.
[0139] Specifically, if the user is actively using one of the
devices 106, 110 such as the other user device 106-B and the other
supervisor device 110-B, and/or accounts, the alert can be provided
to the user via that other device 106-B, 110-B and/or account that
is actively being used. If the user is not actively using an other
device 106-B, 110-B and/or account, a personal device 106-A, 110-A
device, such as a smart phone or tablet, can be identified and the
alert can be provided to this personal device 106-A, 110-A. In some
embodiments, the alert can include code to direct the default
device to provide an indicator of the received alert such as, for
example, an aural, tactile, or visual indicator of receipt of the
alert.
[0140] In some embodiments, the recipient device 106, 110 of the
alert can provide an indication of receipt of the alert. In some
embodiments, the presentation of the alert can include the control
of the I/O subsystem 526 to, for example, provide an aural,
tactile, and/or visual indicator of the alert and/or of the receipt
of the alert. In some embodiments, this can include controlling a
screen of the supervisor device 110 to display the alert, data
contained in alert and/or an indicator of the alert.
[0141] With reference now to FIG. 8, a block diagram of one
embodiment of a user device 106 is shown. As discussed above, the
user device 106 can be configured to provide information to and/or
receive information from other components of the network-based
intervention system 100. The user device can access the
network-based intervention system 100 through any desired means or
technology, including, for example, a webpage such as, for example,
a social network service page, or a web portal. As depicted in FIG.
8, the user device 106 can include a network interface 800. The
network interface 800 allows the user device 106 to access the
other components of the network-based intervention system 100, and
specifically allows the user device 106 to access the network 110
of the network-based intervention system 100. The network interface
800 can include features configured to send and receive
information, including, for example, an antenna, a modem, a
transmitter, receiver, or any other feature that can send and
receive information. The network interface 120 can communicate via
telephone, cable, fiber-optic, or any other wired communication
network. In some embodiments, the network interface 800 can
communicate via cellular networks, WLAN networks, or any other
wireless network.
[0142] The user device 106 can include, for example, a typology
engine 802. The typology engine 802 can identify and/or facilitate
in identifying a user's typology. In some embodiments, for example,
the typology engine 802 can cooperate with other components of the
network-based intervention system 100 to identify and/or facilitate
in identifying the typology of the user. In one embodiment, for
example, the typology engine 802 can receive user data and typology
data and evaluate the user data to determine the typology of the
user.
[0143] The user device 106 can include an update engine 804. The
update engine 804 can facilitate in updating any information within
the network-based intervention system 100. In some embodiments, for
example, the update engine 804 can facilitate a user such as, for
example, a student in providing user data and/or outcome data, or a
user such as, for example, a teacher providing course data. In some
embodiments, the update engine 804 can be configured to update the
user database 301 with information relating to the interactions
between users of the network-based intervention system 100,
including, for example, user relationships.
[0144] The user device 106 can include a user interface 806 that
communicates information to, and receives inputs from, a user. The
user interface 806 can include a screen, a speaker, a monitor, a
keyboard, a microphone, a mouse, a touchpad, a keypad, a
touchscreen, or any other feature or features that can receive
inputs from a user and provide information to a user.
[0145] The user device 106 can include an outcome engine 808. The
outcome engine 808 can be configured to receive outcome data from
the outcome database 312 and determine the correlation between the
outcome and the typology. In some embodiments, for example, this
determination can include retrieving user data from the user
database 301, determining a subset of users corresponding to the
outcome, determining the degree to which the users in the subset of
users achieve the outcome, determining the typology of the users
within the subset of users, and correlating the typology to the
outcome based on the degree to which the users achieve the
outcome.
[0146] With reference now to FIG. 9, a flowchart illustrating a
process 900 for network-based intervention is provided, which
network-based intervention can be network-based intervention. The
process 900 can be performed by one or several of the components of
the network-based intervention system 100. The process 900 begins
at block 902 wherein a subject prompt, which can be a data packet,
is received. In some embodiments, for example, the data packet can
include data relating to a course of study. This data can include,
for example, a question, a comment, or a quote. In some
embodiments, for example, this prompt can be provided by a user
such as a student and/or a teacher. This prompt can be received by
the user device 106 including, for example, a student device, a
teacher device and/or a supervisor device 110.
[0147] After the data packet has been received, the process 900
proceeds to block 904 wherein target group information is received.
In some embodiments, for example, target group information can
include information identifying the one or several designated
recipient users of the data packet. In some embodiments, for
example, the target group can comprise all, or a subset of, the
users associated with a course of study such as, for example, the
one or several students and/or teachers involved in the course of
study. In some embodiments, for example, the target group
information can be received from one or several of the databases
104 including, for example, the user database 301 and/or the course
database 311. In some embodiments, the target group information can
be received by a component of the network-based intervention system
100 such as, for example one or several of the user devices 106
and/or the processor 102.
[0148] After the target group information has been received, the
process 900 proceeds to block 906 wherein the data packet is
provided. In some embodiments, for example, the data packet
received in block 902 can be stored in one or several of the
databases 104 such as, for example, the user database 301 and/or
the course database 311, and can be provided to one or several of
the users via one or several of the user devices 106. In some
embodiments, the data packet can be provided to the users in the
target group.
[0149] After the data packet has been provided, the process 900
proceeds to block 908 wherein a response is received. The response
can be any user-generated content, and in one embodiment, the
response can be, for example, a reaction to the data packet. The
response can be provided by a user that can be in the target group
such as, for example, a student. The response can include, for
example, a comment, question, a quote, a link, or any other data.
In some embodiments, the response can be input into one or several
of the user devices 106.
[0150] After the response has been received, the process 900
proceeds to block 910 wherein a typology is identified. In some
embodiments, for example, the typology of the responding user can
be identified. In some embodiments, for example, the identification
of the responder's typology can include, for example, determining a
characteristic of the responder's response such as, for example,
the content of the response, the nature of the response, the style
of the response, the timing of the response, and other user
activities generated as a result of the response. In some
embodiments, for example, the identification of the responder's
typology can include, for example, determining one or several
characteristics of the user, based on the user data. This can
include, for example, analyzing past responder responses to
determine the content of the past responses, nature of the past
responses, the timing of the past responses, and/or other user
activities generated as a result of the past responses. In some
embodiments, for example, the identification of the responder's
typology can include determining one or several characteristics of
the responder based on other aspects of the responder's user data
including, for example, past outcomes achieved by the responder,
personality information of the responder, and/or any other
information stored in the user data. The determined characteristics
of the responder can be compared to typology criteria, and, based
on the results of the comparison of the determined characteristics
to the typology criteria, the user typology can be identified
and/or the degree to which the user exhibits a typology can be
identified.
[0151] After the responder's typology has been identified, the
process 900 proceeds to block 911 wherein a target outcome is
identified. In some embodiments, for example, an outcome can
comprise a goal. This goal can include, for example, an academic
task such as, for example, a test and/or a quiz, a goal for course
of study, a goal for a grouping of courses of study, a career goal,
a personal goal, and/or a lifetime goal. These goals can include,
for example, a grade; a degree; a career; an acceptance to, for
example, a university and/or educational program; receipt of an
award; development of a personality characteristic and/or
attribute; and educational goal, or a career goal. In some
embodiments, for example, a target outcome can be a designated
goal. In some embodiments, for example, the goal can be designated
by the user such as, for example, by the student and/or teacher,
and in some embodiments, for example, the goal can be a default
goal. Thus, a user such as a student and/or teacher may select a
target outcome based on his ambition, or a target outcome may be
selected by default such as, for example, enrollment in a course of
study can be associated with a target outcome of a passing
grade.
[0152] After the target outcome has been identified, the process
900 proceeds to decision state 912 wherein it is determined if the
user will likely achieve the target outcome. In some embodiments,
for example, this determination can include the evaluation of the
correlation between the typology and the target outcome. In some
embodiments, for example, this determination can include the
comparison of the correlation between the typology exhibited by the
responder to an intervention criterion. In some embodiments, for
example, the intervention criteria can comprise one or several
values delineating between acceptable risk and unacceptable risk as
to the likelihood of a responder achieving the target outcome. In
some embodiments, the risk of failing to achieve the target outcome
can be represented by a risk score that can indicate the strength
of correlation between the determined typology and the target
outcome. In some embodiments, for example, the intervention
criteria can allow sorting of responders, based on risk, into
groups designated for receiving an intervention and groups
designated for not receiving an intervention. In some embodiments,
for example, this determination can be made by a component of the
network-based intervention system 100 such as, for example, the
processor 102 and/or one or more of the user devices 106 or a
component thereof such as, for example, typology engine 802 and/or
the outcome engine 808.
[0153] If it is determined that the responder risk of achieving the
target outcome is unacceptable, then the process 900 proceeds to
block 914 wherein an intervention is requested. In some
embodiments, for example, the intervention can include providing
the responder with a notification of the risk relating to the
target outcome, providing another user with a notification of the
risk relating to the target outcome and identifying the responder,
providing the responder and/or another user with the remedial plan
for mitigating the risk associated with the target outcome,
recommending additional and/or supplemental content, recommending a
peer tutor and/or instructor, automatic assessment to determine
state of knowledge, and/or any other desired action. In some
embodiments, for example, the intervention can be requested by a
component of the network-based intervention system 100 such as, for
example, the processor 102 and/or one or more of the user devices
106 or components thereof.
[0154] After the intervention has been requested, and returning
again to decision state 912, if it is determined that the risk of
achieving the target outcome is acceptable, the process 900
proceeds to block 916 wherein the responder information is updated.
In some embodiments, for example, the responder information can be
updated in the user database 301. In some embodiments, this update
can reflect the determination made in decision state 912, the
responder typology identified in decision state 910, and the risk
level associated with the typology and achieving the target
outcome.
[0155] With reference now to FIG. 10, a flowchart illustrating one
embodiment of a process 1000 for identifying a typology is shown.
The process 1000 can be performed as part of step 910 as shown in
FIG. 9. In some embodiments, the process 1000 can be performed by
the network-based intervention system 100 and/or one or several
components thereof.
[0156] The process 1000 begins at block 1002 wherein response data
is received. In some embodiments, the response data can include a
response received in block 908 of FIG. 9, and in some embodiments,
the response data can include the received response as well as data
relating to past responses. In some embodiments, the response data
can be retrieved from one of the databases 104 such as, for
example, the user database 301.
[0157] After the response data has been received, the process 1000
proceeds to block 1004 wherein user data is updated. In some
embodiments, the user data can be updated with a value indicating
that the response data has been received and/or retrieved. In some
embodiments, this update can facilitate efficient evaluation of
response data for one or several users. In some embodiments, the
user data can be updated in one of the databases 104 such as, for
example, the user database 301.
[0158] After the user data has been updated, the process 1000
proceeds to block 1006 wherein user data is received. In some
embodiments, the user data can be received from one of the
databases 104 such as, for example, the user database 301. After
the user data has been the received, the process 1000 proceeds to
block 1008 wherein response characteristics of the user are
determined. In some embodiments, the determination of the response
characteristics can comprise evaluating the response data. In some
embodiments, this can include determining the time the response was
made; the content of the response; the style, nature, and/or tone
of the response; or the like. In some embodiments, the
determination of the response characteristics can be performed with
text mining software operating on either the processor 102 or one
or several of the user devices 106.
[0159] After the response characteristics have been determined, the
process 1000 proceeds to block 1010 wherein a composite response
score is generated. In some embodiments, the composite response
score can comprise one or several scores that identify
characteristics of the response and/or of the user data. In some
embodiments, a unique score can be assigned to each characteristic
of the response and/or of the user data, and in some embodiments,
the score can represent a conglomeration of multiple
characteristics. In some embodiments, the score can indicate the
presence of a characteristic such as, for example, a score
generated according to a Boolean function wherein a first value is
assigned if the response demonstrates the presence of the
characteristic and wherein a second value is assigned if the
response does not demonstrate the presence of the characteristic.
In some embodiments, the score can indicate the degree to which a
characteristic is present. In some embodiments, the composite
response score can be generated by the processor 102 and/or one or
several of the user devices.
[0160] After the composite response score has been generated, the
process 1000 proceeds to block 1012 wherein classification data is
retrieved. The classification data can comprise one or several
parameters, equations, and/or values that can be used to identify a
typology based on one or several response scores. In some
embodiments, classification data can be stored in one of the
databases 104 such as, for example, the typology database 313.
[0161] After the classification data has been retrieved, the
process 1000 proceeds to block 1014 wherein the composite response
score is compared to classification data. In some embodiments, the
comparison of the composite response score to the classification
data can include the use of the classification data to determine
one or several user typologies based on the response data, the
response score, and/or user data. The comparison of the response
score to the classification data can be performed by the processor
102 and/or one or several of the user devices 106.
[0162] After the composite response score has been compared to the
classification data, the process 1000 proceeds to block 1016
wherein a value indicative of the typology of the user is
associated with the user data. In some embodiments in which one or
several typologies have been identified, one or several values
indicating the one or several typologies can be associated with the
user data. In some embodiments, these values can indicate the
presence of a typology such as, for example, a value generated
according to a Boolean function wherein a first value is assigned
if application of the classification data to the composite response
score indicates the presence of the characteristic and wherein a
second value is assigned if application of the classification data
to the composite response score does not demonstrate the presence
of the characteristic. In some embodiments, the score can indicate
the degree to which a characteristic is present. In some
embodiments, the value can be stored in one of the databases 104
such as, for example, the user database 301 and/or the typology
database 313.
[0163] After a value indicative of the typology of the user has
been associated with the user data, the process 1000 proceeds to
decision state 1018 wherein it is determined if the response and
user data should be evaluated for additional typologies. In some
embodiments, this determination can include identifying all of the
typologies for which user and/or response data is evaluated and
determining whether the user and/or response data has been
evaluated for indication of all of the identified typologies. If it
is determined that additional evaluation of the user and/or
response data is desired, then the process 1000 returns to block
1014 and proceeds as outlined above. If it is determined that
additional evaluation is not desired, then the process 1000
proceeds to block 1020 and continues at block 912 of FIG. 9.
[0164] With reference now to FIG. 11, a flowchart illustrating one
embodiment of a process 1100 for requesting intervention is shown.
The process 1100 can be performed as part of step 914 shown in FIG.
9. In some embodiments, the process 1100 can be performed by the
network-based intervention system 100 and/or one or several
components thereof.
[0165] The process begins at block 1102 wherein the risk score is
received. In some embodiments, the risk score can represent the
risk of failing to achieve the target outcome and can indicate the
strength of correlation between the determined typology and the
target outcome. In some embodiments, the risk score can be
determined by the processor 102 and/or another component of the
network-based intervention system 100. In some embodiments, the
risk score can be determined as part of decision state 912 shown in
FIG. 9.
[0166] In some embodiments, the receipt of the risk score can
further include receiving a risk threshold. In some embodiments,
the risk threshold can define an upper limit for acceptable risk
levels, and specifically, an upper limit for likelihood of failure
to achieve the target outcome. The risk threshold can be any
desired value and can, in some embodiments, vary, based on the
target outcome. In some embodiments, the risk threshold can be
specified by the user and the risk threshold can be stored in one
of the databases 104 such as, for example, the user database 301
and/or the outcome database 312.
[0167] After the risk score has been received, the process 1100
proceeds to block 1104 wherein the risk score is compared to the
risk threshold. In some embodiments, this can include determining
whether the risk score meets, exceeds, or fails to meet the risk
threshold. In some embodiments, a value is associated with the risk
score based on whether it meets, exceeds, or fails to meet the risk
threshold. In one such embodiment, a first value indicative of an
acceptable risk level is associated with the risk score when the
risk score fails to meet or fails to exceed the risk threshold, and
a second value indicative of an unacceptable risk level is
associated with the risk score when the risk score meets or exceeds
the risk threshold. In some embodiments, the comparison of the risk
score to the risk threshold can be performed by the processor 102
or other component of the network-based intervention system
100.
[0168] In embodiments in which the risk score exceeds the risk
threshold, the comparison of the risk score to the risk threshold
can include determining the degree to which the risk score exceeds
the risk threshold. In some embodiments, this can be performed by
comparing the risk score to a plurality of higher risk thresholds
and determining which of the higher risk thresholds have been met
and/or exceeded. In some embodiments, a value indicative of the
degree to which the risk score exceeds the risk threshold can be
associated with the user and/or the user data. In some embodiments,
this determination of the degree to which the risk score exceeds
the risk threshold can be performed by the processor 102 and/or
another component of the network-based intervention system 100.
[0169] After the risk score has been compared to the risk
threshold, the process 1100 proceeds to block 1106 wherein an
intervention level is determined. In some embodiments this
determination of the intervention level can include retrieving
information indicating the degree to which the risk score exceeds
the risk threshold. In one embodiment, for example, each of the
higher risk thresholds discussed above can be associated with a
different intervention level. In one such embodiment, the
intervention level associated with the highest risk threshold that
is met and/or exceeded can be identified as appropriate for the
student/user.
[0170] Advantageously, in some embodiments, as the risk of failing
to achieve the target outcome increases, the level of intervention
can increase to thereby mitigate the increasing risk. In one
exemplary embodiment, for example, when the risk score exceeds the
risk threshold to a lesser degree, a first intervention level can
be attained whereas in another exemplary embodiment in which the
risk score exceeds the risk threshold to a greater degree a second
intervention level can be attained, and in one exemplary embodiment
in which the risk score exceeds the risk threshold to an even
greater degree, a third intervention level can be attained. In some
embodiments, there can be any desired number of intervention levels
and the intervention levels can be triggered in any desired
fashion. In some embodiments, information relating to the different
intervention levels can be stored in one of the databases 104 such
as, for example, the course database 311 and/or the outcome
database 312.
[0171] After the intervention level has been determined, the
process 1100 proceeds to block 1108 wherein a risk notice is
generated. In some embodiments, the risk notice can comprise a
message corresponding to the determined intervention level. In some
embodiments, the risk notice can serve to notify the recipient of
the risk of failing to achieve the target outcome, and in some
embodiments, the risk notice can include one or several remedial
and/or risk mitigating steps or actions. The risk notice can be
generated by the processor 102 with information retrieved from, for
example, the outcome database 312 and/or the course database
311.
[0172] After the risk notice has been generated, the process 1100
proceeds to block 1110 wherein potential risk notice recipients are
identified. In some embodiments, the identification of potential
risk notice recipients can be performed by the processor 102 with
data stored in one of the databases 104 such as, for example, the
user database 301, the course database 311, and/or the outcome
database 312.
[0173] After the potential risk notice recipients have been
identified, the process 1100 proceeds to block 1112 wherein a
mitigation plan is generated. In some embodiments, the mitigation
plan can correspond to the identified intervention level. Thus, in
some embodiments in which the first intervention level is attained,
the prescribed mitigation plan may involve notifying the
user/student of the risk of failure; in another exemplary
embodiment in which the second intervention level is attained, the
prescribed mitigation plan may involve notifying the user/student
and a teacher/trainer/parent/mentor or other individual of the risk
of failure; and in a third exemplary embodiment in which the third
intervention level is attained, the prescribed mitigation plan may
involve one or several recommended courses, materials, or actions
to affect the typology of the user to thereby decrease the user's
risk of failing to achieve the target outcome in addition to
notifying the user/student and/or a teacher/trainer/parent/mentor
or other individual of the risk of failure to achieve the target
outcome. In some embodiments, information relating to the different
mitigation plans is stored in one of the databases 104 such as, for
example, the course database 311 and/or the outcome database 312.
After the mitigation plan has been generated, the process 1100
proceeds to block 1114 and continues at block 916 of FIG. 9.
[0174] With reference now to FIG. 12, a flowchart illustrating one
embodiment of a process 1200 for linking a target outcome to a
typology is provided. The process 1200 can be performed by the
network-based intervention system 100 and/or components thereof.
The process 1200 begins at block 1202 wherein target outcome
information is received. In some embodiments, for example, the
target outcome information can include the identification of a
goal, and can be received, for example, from one or several users
via one or several user devices 106 and/or from one of the
databases 104 such as, for example, the user database 301 and/or
the outcome database 312.
[0175] After the target outcome information has been received, the
process 1200 proceeds to block 1204 wherein the user data is
received. In some embodiments, and as discussed above in greater
detail, the user data can include information relating to one or
several users. This information can be received, for example, from
one or several users via one or several user devices 106 and/or
from the user database 301.
[0176] After the user data has been received, the process 1200
proceeds to block 1206 wherein the user data is filtered. In some
embodiments, for example, the user data can be filtered to divide
the user data into a first group, or user subset, related to the
target outcome and the second group that is unrelated to target
outcome. In some embodiments, for example, the first group that is
related to the target outcome can be related to the target outcome
in that members of the first group have, to some degree, achieved
the target outcome and/or have taken steps towards achieving the
target outcome. This filtering can be performed, for example, by a
component of the network-based intervention system 100 such as, for
example, the processor 102 and/or one or several of the user
devices 106 or components thereof including, for example, the
outcome engine 808.
[0177] After the user data has been filtered, the process 1200
proceeds to block 1208 wherein a typology group within the user
subset is identified. In some embodiments, for example, the
typology group within the user subset can comprise a group of users
within the subset of users related to the target outcome that
exhibits a typology and/or exhibits at least a certain degree of
the typology. In some embodiments, for example, this identification
can include first analyzing user data associated with individual
users to determine the typology and/or typologies of the individual
users, and then identifying typology groupings of users. This
identification can be performed, for example, by the processor 102
and/by or one or several of the user devices 106 or components
thereof.
[0178] After the typology group within the user subset has been
identified, the process 1200 proceeds to block 1210 wherein the
outcome achievement of the users within the typology group is
identified. In some embodiments, for example, this can include the
binary determination of whether the users within the typology have
achieved the outcome, or non-binary determination of the degree to
which the users within the typology group have achieved the
outcome. The identification of the outcome achievement of the users
can be performed by, for example, processor 102 and/or one or
several of the user devices 106 or components thereof.
[0179] After the outcome achievement of users within the typology
group has been identified, the process 1200 proceeds to block 1212
wherein an outcome achievement value is generated. In some
embodiments, for example, the outcome achievement value can
indicate whether the user achieved the target outcome and/or
indicate the degree to which the user has achieved the target
outcome. In some embodiments, the achievement value can be
generated according to a Boolean function, wherein a first value is
generated if the target outcome is achieved, and a second value is
generated if the target outcome is not achieved. In some
embodiments, the achievement value can be generated by, for
example, the processor 102 and/or one or several of the user
devices 106 or components thereof.
[0180] After the outcome achievement value has been generated, the
process 1200 proceeds to block 1214 wherein the achievement value
is applied. In some embodiments, for example, the outcome
achievement value can be applied to the user from whose user data
the outcome achievement value was generated. In some embodiments,
the application of the achievement value can include the storing of
the achievement value in one of the databases 106 such as user
database 301 and or the outcome database 312.
[0181] After the achievement value has been applied, the process
1200 proceeds to block 1216 wherein the correlation between the
typology and outcome achievement is identified. This correlation
between the typology and outcome achievement can comprise
correlative evidence and/or other evidence of an inferred
relationship. In some embodiments, the generation of the
correlation can include use of a statistical method of evaluation
and/or a stochastic process, and in some embodiments, this may
include other measures of causality including, for example, one or
several discrete probabilities that can be derived through other
techniques such as, for example, information-theoretic mechanisms.
In some embodiments, for example, this can include the generating
of correlation value for the users within the typology group. In
some embodiments, for example, this can include generating a
correlation value for the entire typology group and/or the
correlation value for one or several portions of the typology
group. In some embodiments, for example, it may be advantageous to
generate a correlation value for a subset of the typology group
exhibiting a certain degree and/or a range of degrees of the
typology. In some embodiments, the correlation value can be
generated by a component of the network-based intervention system
100 such as the processor 102 and/or one or several of the user
devices 106 or components thereof.
[0182] After the correlation between the typology and outcome
achievement is identified, the process 1200 proceeds to block 1218
wherein an indication of the correlation is added. In some
embodiments, for example, an indication of the correlation can be
added to one or more of the databases 104 such as, for example, the
outcome database 312 and/or the typology database 313.
[0183] After an indicator of the correlation is added, the process
1200 proceeds to decision state 1220 wherein it is determined if
there is an additional typology represented in the filtered user
data. In some embodiments, for example, the filtered user data can
include users having multiple typologies and/or exhibiting degrees
of multiple typologies. In some embodiments, for example, it may be
advantageous to determine the correlation between outcome
achievement and one, some, or all of the typologies exhibited
within the filtered user data. Thus, it can be advantageous to
perform the steps outlined in blocks 1208 to 1218 for multiple
typologies contained within the filtered user data.
[0184] In some embodiments, for example, the determination of
whether there is an additional typology represented in the filtered
user data can include evaluating the filtered user data for
indications of the additional typologies. This can be performed by,
for example, the processor 102 and/or one or several of the user
devices 106 or components thereof. If it is determined that there
are additional typologies exhibited within the filtered user data,
the process 1200 returns to block 1208. If it is determined that
there are no additional typologies within the filtered user data,
then the process can, for example, terminate.
[0185] With reference now to FIG. 13, a flowchart illustrating one
embodiment of a process 1300 for identifying a correlation between
typology and outcome achievement is shown. The process 1300 can be
performed as part of step 1216 as shown in FIG. 12. In some
embodiments, the process 1300 can be performed by the network-based
intervention system 100 and/or one or several components
thereof.
[0186] The process 1300 begins at block 1302 wherein a typology
group subset is selected. In some embodiments, the typology group
subset can be all or a portion of the users identified within the
typology group. In some embodiments, the typology group subset can
be users exhibiting one or several desired typologies and/or one or
several desired degrees of one or several desired typologies. In
some embodiments, the typology group subset can be the same as the
typology group identified in block 1208 of FIG. 12, and in some
embodiments, the typology group subset can be different from the
typology group identified in block 1208 of FIG. 12. In embodiments
in which the typology group subset is different in the typology
group identified in block 1208 of FIG. 12, the typology group
subset can comprise users having a desired composition of multiple
typologies. In some embodiments, the typology group subset can be
identified based on user data received from, for example, the user
database 301 and this identification can be performed by the
processor 102 and/or another component of the network-based
intervention system 100.
[0187] After the typology group subset has been identified, the
process 1300 proceeds to block 1304 wherein an outcome achievement
value is identified. In some embodiments, the outcome achievement
value can indicate whether some or all of the users within the
typology group subset have attained and/or are attaining the target
outcome and/or the degree to which some or all of the users within
the typology group subset have attained and/or are attaining the
target outcome. In some embodiments, the outcome achievement value
can be generated by the processor 102 and/or another component of
the network-based intervention system 100.
[0188] After the outcome achievement value has been generated, the
process 1300 proceeds to block 1306 wherein the typology group
subset size is determined. In some embodiments, this can include
determining the number of users within the typology group subset.
This determination can be made by the processor 102 or another
component of the network-based intervention system 100 such as, for
example, one or several of the user devices 106. After the typology
group subset size has been determined, the process 1300 proceeds to
block 1308 wherein a correlation value is generated. In some
embodiments, the correlation value can indicate the correlation
between the one or several typologies expressed in the typology
group subset and achievement of the target outcome. In some
embodiments, the correlation value can be calculated using any
desired statistical or stochastic method and can be calculated by
the processor 102 or other component of the network-based
intervention system 100.
[0189] After the correlation value has been generated, the process
1300 proceeds to decision state 1318 wherein it is determined if
there is an additional typology group subset to be evaluated. In
some embodiments, this can include determining whether all of a
group of desired typologies have been evaluated to generate a
correlation value for those desired typologies. If one of the group
of desired typologies has not been evaluated to generate a
correlation value, then the process 1300 returns to block 1302 and
proceeds as outlined above. If it is determined that no additional
typologies should be evaluated to generate a correlation value,
then the process 1300 proceeds to block 1320 and returns to block
1218 of FIG. 12.
[0190] A number of variations and modifications of the disclosed
embodiments can also be used. Specific details are given in the
above description to provide a thorough understanding of the
embodiments. However, it is understood that the embodiments may be
practiced without these specific details. For example, well-known
circuits, processes, algorithms, structures, and techniques may be
shown without unnecessary detail in order to avoid obscuring the
embodiments.
[0191] Implementation of the techniques, blocks, steps and means
described above may be done in various ways. For example, these
techniques, blocks, steps and means may be implemented in hardware,
software, or a combination thereof. For a hardware implementation,
the processing units may be implemented within one or more
application specific integrated circuits (ASICs), digital signal
processors (DSPs), digital signal processing devices (DSPDs),
programmable logic devices (PLDs), field programmable gate arrays
(FPGAs), processors, controllers, micro-controllers,
microprocessors, other electronic units designed to perform the
functions described above, and/or a combination thereof.
[0192] Also, it is noted that the embodiments may be described as a
process which is depicted as a flowchart, a flow diagram, a swim
diagram, a data flow diagram, a structure diagram, or a block
diagram. Although a depiction may describe the operations as a
sequential process, many of the operations can be performed in
parallel or concurrently. In addition, the order of the operations
may be re-arranged. A process is terminated when its operations are
completed, but could have additional steps not included in the
figure. A process may correspond to a method, a function, a
procedure, a subroutine, a subprogram, etc. When a process
corresponds to a function, its termination corresponds to a return
of the function to the calling function or the main function.
[0193] Furthermore, embodiments may be implemented by hardware,
software, scripting languages, firmware, middleware, microcode,
hardware description languages, and/or any combination thereof.
When implemented in software, firmware, middleware, scripting
language, and/or microcode, the program code or code segments to
perform the necessary tasks may be stored in a machine readable
medium such as a storage medium. A code segment or
machine-executable instruction may represent a procedure, a
function, a subprogram, a program, a routine, a subroutine, a
module, a software package, a script, a class, or any combination
of instructions, data structures, and/or program statements. A code
segment may be coupled to another code segment or a hardware
circuit by passing and/or receiving information, data, arguments,
parameters, and/or memory contents. Information, arguments,
parameters, data, etc. may be passed, forwarded, or transmitted via
any suitable means including memory sharing, message passing, token
passing, network transmission, etc.
[0194] For a firmware and/or software implementation, the
methodologies may be implemented with modules (e.g., procedures,
functions, and so on) that perform the functions described herein.
Any machine-readable medium tangibly embodying instructions may be
used in implementing the methodologies described herein. For
example, software codes may be stored in a memory. Memory may be
implemented within the processor or external to the processor. As
used herein the term "memory" refers to any type of long term,
short term, volatile, nonvolatile, or other storage medium and is
not to be limited to any particular type of memory or number of
memories, or type of media upon which memory is stored.
[0195] Moreover, as disclosed herein, the term "storage medium" may
represent one or more memories for storing data, including read
only memory (ROM), random access memory (RAM), magnetic RAM, core
memory, magnetic disk storage mediums, optical storage medium