Embodiments of the present invention generally relate to education
assistance techniques, and more particularly to a method and apparatus to
provide customized educational content based on a student's preparation
levels.
Several education solutions exist for assisting students in
preparation for exams. Such education solutions include offline solutions, such
as books, contact lectures and the like, and digital solutions, for example,
tutorial videos or audios, computer tests and the like. Online solutions
9 significantly increase convenience, and some solutions also provide
interactivity, and access to large amounts of study material.
However, students get inundated with large amounts of study
material, and have little experience of which subjects have higher relative
importance, or could improve their performance. In some cases, interaction on
online social communities with similar groups or network of students, for
example, preparing for the same exam may be useful in sharing knowledge,
techniques and help in overall preparation. However, such social interaction
based on mutual benefit is unbalanced if one student is at an advanced level of
preparation, while another is at a basic level. Therefore, such digital or
^ internet education solutions are not particularly relevant to individuals, rather
a force-fit of available material or social community content to the students.
Therefore, there is a need for improved method and apparatus to
provide educational content in a productive manner.
BRIEF DESCRIPTION
The present invention generally relates to a method and apparatus
to provide customized educational content. In an embodiment, a computer
implemented method for providing customized educational content comprises
receiving preparation level data for a student, determining preparation level
3 rating (PLR) for the student and recommending educational content for the
student based on the PLR.
DRAWINGS
Figure 1 depicts a block diagram of a computing system for
providing customized educational content, according to one or more
embodiments;
Figure 2 depicts a flow diagram of a method for recommending
customized educational content as an example of implementation of
9 educational content recommendation module of Figure 1, according to one or
more embodiments;
Figure 3 depicts a flow diagram of a method for defining
performance level rating to provide customized educational content as an
example of implementation of PLR engine of Figure 1, according to one or more
embodiments;
Figure 4 depicts a flow diagram of a method for determining
appropriate content plug and appropriate sub-network as an example of
implementation of a course plan generator and sub-network recommendation
engine respectively of Figure 1, according to one or more embodiments; and
^ Figure 5 depicts a flow diagram of a method for sub-network
modification based on preparation level rating as an example of
implementation of the sub-network recommendation engine of Figure 1,
according to one or more embodiments.
Figure 6 depicts a computer system that can be utilized in various
embodiments of the present invention, according to one or more embodiments
While the method and apparatus to provide customized educational
content is described herein by way of example for several embodiments and
illustrative drawings, those skilled in the art will recognize that the method
and apparatus for providing customized educational content is not limited to
the embodiments or drawings described. It should be understood, that the
4
drawings and detailed description thereto are not intended to limit
embodiments to the particular form disclosed. Rather, the intention is to cover
all modifications, equivalents and alternatives falling within the spirit and
scope of the system and method for providing customized educational content
defined by the appended claims. Any headings used herein are for
organizational purposes only and are not meant to limit the scope of the
description or the claims. As used herein, the word "may" is used in a
permissive sense (i.e., meaning having the potential to), rather than the
mandatory sense (i.e., meaning must). Similarly, the words "include",
W "including", and "includes" mean including, but not limited to.
DETAILED DESCRIPTION
Various embodiments of a method and apparatus for providing
customized educational content are described. In one embodiment,
preparation level of a student is determined, for example, a preparation level
rating is determined based on various parameters, including but not limited to
a student's performance in tests, quizzes, consumption of study material,
activity or contribution on interaction communities, for example, social
networks. Based on the preparation level of a student, one or more course(s) is
A recommended for further study. In certain embodiments, a course plan may be
recommended, and then based on a student's continued performance, the
preparation level may be recalculated, and accordingly, the course plan is
readjusted. Therefore, the techniques disclosed herein adjust to a student's
preparation level and provides a course plan for the student achieving an
optimal performance.
Figure 1 depicts a computing system 100 for providing customized
educational content, according to one or more embodiments. The computing
system 100 illustrates an education application server 110, an education
consumption computer 140 and a communication network 130. The
communication network 130 communicably couples the education application
5
server 110 and the education consumption computer 140. The education
application server 110 provides customized educational content
recommendation that is consumed at the education consumption computer 140
by a student. The student, as referred to herein, is any person preparing for
an examination like an entrance examination for engineering or medical
colleges or other competitive examinations among others.
The education application server 110 is a type of computing device
known to one of ordinary skill in the art. The education application server 110
comprises a Central Processing Unit (CPU) 112, support circuits 114 and a
W memory 120. The CPU 112 may comprise one or more commercially available
microprocessors or microcontrollers that facilitate data processing and storage.
The various support circuits 114 facilitate the operation of the CPU 112 and
include one or more clock circuits, power supplies, cache, input/output circuits,
and/or the like. The memory 120 comprises at least one of Read Only Memory
(ROM), Random Access Memory (RAM), disk drive storage, optical storage,
removable storage and/or the like. The memory 120 comprises an operating
system (OS) 122, an educational content recommendation module 124 and
educational content 132. The educational content recommendation module
124 is configured to recommend customized educational content based on at
A least one of preparation level rating (PLR) and additional customization
parameters. The educational content recommendation module 124 comprises a
PLR engine 126, a course plan generator 128, and a sub-network
recommendation engine 130. The educational content 132 comprises digital
study material generally used by the student for preparing for examination.
Digital study material may be in the form of e-books, web-tutorials,
presentations, digital question banks and/or the like. The PLR engine 126
generates the preparation level rating (PLR),that is used by the course plan
generator 128 and the sub-network recommendation engine 130 to recommend
customized educational content. In some embodiments, preparation level
6
rating (PLR) is a measure of the level of preparation of the student for an
examination.
According to some embodiments, the operating system (OS) 122
generally manages various computer resources (e.g., network resources, data
storage resources, file system resources and/or the like). The operating system
122 is configured to execute operations on one or more hardware and/or
software modules, such as Network Interface Cards (NICs), hard disks,
virtualization layers, firewalls and/or the like. For example, the educational
content recommendation module 124 associated operations to provide
^ customized educational content recommendations and the like.
The education consumption computer 140, is a type of computing
device (e.g., a laptop, a desktop, a Personal Digital Assistant (PDA), a tablet, a
mobile phone and/or the like) known to one of ordinary skill in the art. The
education consumption computer 140 comprises a Central Processing Unit
(CPU) 142, support circuits 144, and a memory 150. The CPU 142 may
comprise one or more commercially available microprocessors or
microcontrollers that facilitate data processing and storage. The various
support circuits 144 facilitate the operation of the CPU 142 and include one or
more clock circuits, power supplies, cache, input/output circuits, and the like.
_ The memory 150 comprises at least one of Read Only Memory (ROM), Random
Access Memory (RAM), disk drive storage, optical storage, removable storage
and/or the like. The memory 150 comprises an operating system (OS) 152,
content plugs 156, preparation level data 154, and a sub-network participation
data 158. The preparation level data 154 provides a record of educational
content consumed by the student at the education consumption computer 140
and is used to calculate the PLR by a method 300 described with reference to
Figure 3.
Customized educational content includes the content plugs 156 and
a sub-network access. The content plugs 156 are digital study materials
identified from the educational content 132, for example, tutorials for a topic or
7
sub-topic, or a chapter from an e-book, practice tests or questions along with a
diagnostic test required for preparing for an examination. Further, each
content plug is associated with various tags or meta-tags. Such tags or metatags
may identify the level of difficulty, time required for consumption and
student recommendation rating for the content plug. Such tags or meta-tags
provide a means for categorizing the content plug 156 and selecting
appropriate content plugs for customized educational content. The content
plugs 156 are selected for recommendation by the course plan generator 128.
Method of recommendation of the content plugs 156 is described in detail with
^ reference to a method 400 in Figure 4.
The sub-network is an online community of students having same
educational requirements and having similar PLR. According to one
embodiment, the sub-network may connect to larger online communities such
as, for example, Facebook and use such larger online community platforms for
interaction. For example, a sub-network may comprise students preparing for
Indian Institute of Technology Joint Entrance Examination (IIT JEE) and
having similar PLR. Such a sub-network with students having similar PLR
ensures interaction among students who can benefit from each other and
contribute towards expediting each other's learning. The sub-network
A participation data 158 is a measure of activity of the student on the sub-
W
network. The sub-network participation data 158 may, for example, include
the time spent by the student interacting on the sub-network, number of subnetwork
members that the student has interacted with, number of members
liking a recommendation for a study material made by the student. The subnetwork
participation data 158 may partially reflect the preparation level of
the student. For example, if a recommendation for a study material made by a
student is liked by most other members of the sub-network, it is likely that the
student has a good grasp of the subject.
The communication network 130 is any network generally known in
the art, for example, the Internet, that allows for communicating the
8
customized educational content from the education application server 110 to
the education consumption computer 140. The communication network 130
also allows for communicating the preparation level data 154 and the subnetwork
participation data 158 from the education consumption computer 140
to the education application server 110. The customized educational content,
the preparation level data 154 and the sub-network participation data 158
may be delivered via alternate means, such as memory sticks, local wireless
networks, among several other generally known modes of communicating data.
Figure 2 depicts a flow diagram of a method 200 for recommending
w customized educational content as an example of implementation of
educational content recommendation module 124 of Figure 1, according to one
or more embodiments. The method 200 starts at step 202 and proceeds to step
204. At step 204, the method 200 receives preparation level data, similar to,
for example, the preparation level data 158 of Figure 1. At step 206,
preparation level rating (PLR) is determined, for example by the PLR engine
126 of Figure 1, using the preparation level data 154 and the sub-network
participation data 158. At step 208, additional customization parameters are
determined. Additional customization parameters are factors that enhance
effective consumption of the educational content by the student. Additional
.—, customization parameters may for example, include subject matter expert
(SME) recommendation, student recommendation rating (SRR), difficulty level
(D) of content plug, time required (TR) for consuming content plug and time
available (TA) for preparation.
At step 210, educational content based on at least one of PLR and
additional customization parameters is recommended. According to one
embodiment, a list of content plugs is selected by comparing the PLR of the
student and the difficulty level of content plugs. For example, if a student is at
the beginning level of the preparation, the PLR would be low for the student.
If the content plugs are for example, tagged as basic, medium and advanced
according to difficulty levels, content plugs tagged as basic are selected for a
9
beginner level student. Further, SME recommendation and SRR is taken into
consideration to further shortlist the appropriate content plugs. Finally, a
comparison is made between the TR for consumption and TA for preparation to
customize the educational content recommendation. The method 200 ends at
step 214. However, though not shown in Figure 2, for sake of simple
representation of the method 200, the student has an option of altering the
recommended content plugs. The student may for example, conduct an
independent search on the educational content 132 and choose to substitute
material for a particular topic comprising the recommended content plug with
w that of another author. The course plan generator 128, reconfigures the
content plug to accommodate for the change chosen by the student.
Figure 3 depicts a flow diagram of the method 300 for determining
PLR to provide customize educational content as an example of
implementation of the PLR engine 126 of Figure 1, according to one or more
embodiments. The method 300 starts at step 302 and proceeds to step 304. At
step 304, performance (P) in the diagnostic test is obtained. Performance in
the diagnostic test is for example, a comparative measure and recoded as a
percentile obtained by the student. At step 306, difficulty level (D) for of the
diagnostic test is obtained
^ At step 308, a record of content plugs consumed (C) by the student is
obtained. The content plugs represent study material consumed by the student
and need not necessarily be content plugs 156 recommended by the course plan
generator 128. If a student has consumed study material generated from an
independent source, the method 300 considers such study material at par with
content plugs recommended by the course plan generator 128, as long as the
study material from independent source prepares the student for the
examination in a manner as effective as the content plugs generated by the
course plan generator 128. According to one embodiment, record of content
plugs consumed by the student includes number content plugs consumed by
the student (n), ratings (difficulty level rating, popularity rating or
10
combination thereof) of the content plugs consumed and total duration for
which the content plug is consumed (d).
At step 310, participation rating (N) of the student on the subnetwork
is obtained. In one embodiment, participation rating on the subnetwork
is measured as points gathered on a 100 point scale. For example,
points may be gathered by answering questions posted by other members of
the sub-network and the points gathered for each such answer may depend on
how many members on the sub-network like the answer. At step 312 PLR for
the student is determined. In one embodiment, the PLR is computed using
^ following formula:
PLR = CEIL{(P*.45 + D*.2 + C*. 1 + N*.25)/20},
where, CEIL is ceiling, P is performance in diagnostic test, C is
record of content plugs consumed, and N is participation rating of the student
as described above.
Figure 4, depicts a flow diagram of the method 400 for determining
appropriate content plug and appropriate sub-network as an example of
implementation of a course plan generator 128 and sub-network
recommendation engine 130 respectively of Figure 1, according to one or more
embodiments, further detailing the step 210 of Figure 2. In one embodiment,
^ the method 400 customizes content plug based on at least one of PLR and
additional customization parameters. In another embodiment the method 400
determines appropriate sub-network recommendation based on PLR.
The method 400 starts at step 402, and proceeds to step 404. At step
404, PLR of the student is received. In subsequent steps, i.e. step 406 to step
414, additional customization parameters are received. At step 406, SME
recommendation is received. At step 408, content plug level is received. At step
410 TR for consuming content plug is received. At step 412, SRR for content
plug is received. At step 414, TA for student to prepare for the examination is
received. The TA may either be automatically calculated based on previously
received dates for the examination or may be input by the student.
*
11
At step 416, determination of appropriate content plug according to
PLR received at step 404 and additional customization parameters received
through steps 406 to step 414 is made. At step 418, determination of
appropriate sub-network based on PLR received at step 404 is made. The
method 400 ends at step 420. The order of steps described in method 400 for
recommending appropriate content plug and appropriate sub-network is only
by way of example. One skilled in the art will appreciate that the order may
be altered. For example the appropriate sub-network may be determined
immediately after step 404, where PLR is received. Further, the various
w parameters and measure received in the method 400 are only a means of
customizing educational content in a manner that is efficient for consumption
by a student. Receiving other variations of parameters that effect efficient
consumption of educational material by the student are within scope of the
invention.
According to an embodiment, multiple content plugs may be
recommended to be consumed by the student over a specific period of time.
Such multiple content plugs comprise a course plan that may be altered if
there is a change in the PLR of the student. PLR of the student is redetermined
when a preparation level deterministic event occurs. Preparation
^ level deterministic events may include a student taking the diagnostic test, the
student consuming the content plug, and a change in participation rating of
the student on the sub-network. For example, the PLR of a student may
become higher upon high performance on diagnostic test or the PLR may
become lower if a content plug is not consumed within the stipulated period of
time. When the PLR changes, the course plan is reconfigured according to the
re-determined PLR. Similarly, as described with reference to a method 500 in
Figure 5, the sub-network recommendation may be modified based on PLR.
In one embodiment, content plugs may be tagged as express and
include study material pertaining to most expected questions in the
examination. Such express tagged content plugs may be selected for a student
12
with low PLR when TR for consumption of a course plan exceeds TA for
preparation.
Figure 5 depicts a flow diagram of the method 500 for sub-network
modification based on PLR as an example of implementation of the subnetwork
recommendation engine 130 of Figure 1, according to one or more
embodiments. The method 500 starts at step 502, and proceeds to step 504. At
step 504, a preparation level deterministic event occurs. As described above,
with respect to Figure 4, preparation level deterministic events affect the PLR
of a student and therefore the PLR required to be re-determined for suitable
W customization of educational content. At step 506, re-determination of PLR is
made. At step 508, appropriate sub-network according to re-determined PLR is
determined. The method 500 ends at step 510.
The embodiments of the present invention may be embodied as
methods, apparatus, electronic devices, and/or computer program products.
Accordingly, the embodiments of the present invention may be embodied in
hardware and/or in software (including firmware, resident software, microcode,
etc.), which may be generally referred to herein as a "circuit" or "module".
Furthermore, the present invention may take the form of a computer program
product on a computer-usable or computer-readable storage medium having
^ computer-usable or computer-readable program code embodied in the medium
for use by or in connection with an instruction execution system. In the
context of this document, a computer-usable or computer-readable medium
may be any medium that can contain, store, communicate, propagate, or
transport the program for use by or in connection with the instruction
execution system, apparatus, or device. These computer program instructions
may also be stored in a computer-usable or computer-readable memory that
may direct a computer or other programmable data processing apparatus to
function in a particular manner, such that the instructions stored in the
computer usable or computer-readable memory produce an article of
13
manufacture including instructions that implement the function specified in
the flowchart and/or block diagram block or blocks.
The computer-usable or computer-readable medium may be, for
example but not limited to, an electronic, magnetic, optical, electromagnetic,
infrared, or semiconductor system, apparatus, device, or propagation medium.
More specific examples (a non-exhaustive list) of the computer-readable
medium include the following: hard disks, optical storage devices, a
transmission media such as those supporting the Internet or an intranet,
magnetic storage devices, an electrical connection having one or more wires, a
™ portable computer diskette, a random access memory (RAM), a read-only
memory (ROM), an erasable programmable read-only memory (EPROM or
Flash memory), an optical fiber, and a compact disc read-only memory (CDROM).
Computer program code for carrying out operations of the present
invention may be written in an object oriented programming language, such as
Java.RTM, Smalltalk or C++, and the like. However, the computer program
code for carrying out operations of the present invention may also be written in
conventional procedural programming languages, such as the "C"
programming language and/or any other lower level assembler languages. It
A will be further appreciated that the functionality of any or all of the program
modules may also be implemented using discrete hardware components, one or
more Application Specific Integrated Circuits (ASICs), or programmed Digital
Signal Processors or microcontrollers.
The foregoing description, for purpose of explanation, has been
described with reference to specific embodiments. However, the illustrative
discussions above are not intended to be exhaustive or to limit the invention to
the precise forms disclosed. Many modifications and variations are possible in
view of the above teachings. The embodiments were chosen and described in
order to best explain the principles of the present disclosure and its practical ;
applications, to thereby enable others skilled in the art to best utilize the
14
invention and various embodiments with various modifications as may be
suited to the particular use contemplated.
Example Computer System
Figure 6 depicts a computer system that can be utilized in various
embodiments of the present invention, according to one or more embodiments.
Various embodiments of a method and apparatus provide
customized educational content, as described herein, may be executed on one
or more computer systems, which may interact with various other devices.
One such computer system is computer system 600 illustrated by Figure 6,
w which may in various embodiments implement any of the elements or
functionality illustrated in Figures 1-5. In various embodiments, computer
system 600 may be configured to implement methods described above. While
the illustrated system demonstrates computer system 600 implementing
method 200, computer system 600 may be used to implement any other
system, device, element, functionality or method of the above-described
embodiments. In the illustrated embodiments, computer system 600 may be
configured to implement methods 200, 300, 400, and 500 as processorexecutable
executable program instructions 622 (e.g., program instructions
executable by processor(s) 610) in various embodiments.
A In the illustrated embodiment, computer system 600 includes one or
more processors 610 coupled to a system memory 620 via an input/output (I/O)
interface 630. Computer system 600 further includes a network interface 640
coupled to I/O interface 630, and one or more input/output devices 650, such as
cursor control device 660, keyboard 670, and display(s) 680. In various
embodiments, any of components may be utilized by the system to receive user
input described above. In various embodiments, a user interface (e.g., user
interface) may be generated and displayed on display 680. In some cases, it is
contemplated that embodiments may be implemented using a single instance
of computer system 600, while in other embodiments multiple such systems, or
multiple nodes making up computer system 600, may be configured to host
15
different portions or instances of various embodiments. For example, in one
embodiment some elements may be implemented via one or more nodes of
computer system 600 that are distinct from those nodes implementing other
elements. In another example, multiple nodes may implement computer
system 600 in a distributed manner.
In different embodiments, computer system 600 may be any of
various types of devices, including, but not limited to, a personal computer
system, desktop computer, laptop, notebook, or netbook computer, mainframe
computer system, handheld computer, workstation, network computer, a
^ camera, a set top box, a mobile device, a consumer device, video game console,
handheld video game device, application server, storage device, a peripheral
device such as a switch, modem, router, or in general any type of computing or
electronic device.
In various embodiments, computer system 600 may be a
uniprocessor system including one processor 610, or a multiprocessor system
including several processors 610 (e.g., two, four, eight, or another suitable
number). Processors 610 may be any suitable processor capable of executing
instructions. For example, in various embodiments processors 610 may be
general-purpose or embedded processors implementing any of a variety of
A instruction set architectures (ISAs), such as the x96, PowerPC, SPARC, or
MIPS ISAs, or any other suitable ISA. In multiprocessor systems, each of
processors 610 may commonly, but not necessarily, implement the same ISA.
System memory 620 may be configured to store program
instructions 622 and/or data 632 accessible by processor 610. In various
embodiments, system memory 620 may be implemented using any suitable
memory technology, such as static random access memory (SRAM),
synchronous dynamic RAM (SDRAM), nonvolatile/Flash-type memory, or any
other type of memory. In the illustrated embodiment, program instructions
and data implementing any of the elements of the embodiments described
above may be stored within system memory 620. In other embodiments,
16
program instructions and/or data may be received, sent or stored upon
different types of computer-accessible media or on similar media separate from
system memory 620 or computer system 600.
In one embodiment, I/O interface 630 may be configured to
coordinate I/O traffic between processor 610 , system memory 620, and any
peripheral devices in the device, including network interface 640 or other
peripheral interfaces, such as input/output devices 650, In some embodiments,
I/O interface 630 may perform any necessary protocol, timing or other data
transformations to convert data signals from one components (e.g., system
memory 620) into a format suitable for use by another component (e.g.,
processor 610). In some embodiments, I/O interface 630 may include support
for devices attached through various types of peripheral buses, such as a
variant of the Peripheral Component Interconnect (PCI) bus standard or the
Universal Serial Bus (USB) standard, for example. In some embodiments, the
function of I/O interface 630 may be split into two or more separate
components, such as a north bridge and a south bridge, for example. Also, in
some embodiments some or all of the functionality of I/O interface 630, such as
an interface to system memory 620, may be incorporated directly into
processor 610.
fh Network interface 640 may be configured to allow data to be
exchanged between computer system 600 and other devices attached to a
network (e.g., network 690), such as one or more external systems or between
nodes of computer system 600. In various embodiments, network 690 may
include one or more networks including but not limited to Local Area
Networks (LANs) (e.g., an Ethernet or corporate network), Wide Area
Networks (WANs) (e.g., the Internet), wireless data networks, some other
electronic data network, or some combination thereof. In various
embodiments, network interface 640 may support communication via wired or
wireless general data networks, such as any suitable type of Ethernet network,
for example; via telecommunications/telephony networks such as analog voice
17
networks or digital fiber communications networks; via storage area networks
such as Fibre Channel SANs, or via any other suitable type of network and/or
protocol.
Input/output devices 650 may, in some embodiments, include one or
more display terminals, keyboards, keypads, touchpads, scanning devices,
voice or optical recognition devices, or any other devices suitable for entering
or accessing data by one or more computer systems 600. Multiple input/output
devices 650 may be present in computer system 600 or may be distributed on
various nodes of computer system 600. In some embodiments, similar
w input/output devices may be separate from computer system 600 and may
interact with one or more nodes of computer system 600 through a wired or
wireless connection, such as over network interface 640.
In some embodiments, the illustrated computer system may
implement any of the methods described above, such as the methods
illustrated by the flowcharts of Figures 2 - 5 . In other embodiments, different
elements and data may be included.
Those skilled in the art will appreciate that computer system 600 is
merely illustrative and is not intended to limit the scope of embodiments. In
particular, the computer system and devices may include any combination of
£ hardware or software that can perform the indicated functions of various
embodiments, including computers, network devices, Internet appliances,
PDAs, wireless phones, pagers, etc. Computer system 600 may also be
connected to other devices that are not illustrated, or instead may operate as a
stand-alone system. In addition, the functionality provided by the illustrated
components may in some embodiments be combined in fewer components or
distributed in additional components. Similarly, in some embodiments, the
functionality of some of the illustrated components may not be provided and/or
other additional functionality may be available.
Those skilled in the art will also appreciate that, while various items
are illustrated as being stored in memory or on storage while being used, these
18
items or portions of them may be transferred between memory and other
storage devices for purposes of memory management and data integrity.
Alternatively, in other embodiments some or all of the software components
may execute in memory on another device and communicate with the
illustrated computer system via inter-computer communication. Some or all of
the system components or data structures may also be stored (e.g., as
instructions or structured data) on a computer-accessible medium or a portable
article to be read by an appropriate drive, various examples of which are
described above. In some embodiments, instructions stored on a computer-
" accessible medium separate from computer system 600 may be transmitted to
computer system 600 via transmission media or signals such as electrical,
electromagnetic, or digital signals, conveyed via a communication medium
such as a network and/or a wireless link. Various embodiments may further
include receiving, sending or storing instructions and/or data implemented in
accordance with the foregoing description upon a computer-accessible medium
or via a communication medium. In general, a computer-accessible medium
may include a storage medium or memory medium such as magnetic or optical
media, e.g., disk or DVD/CD-ROM, volatile or non-volatile media such as RAM
(e.g., SDRAM, DDR, RDRAM, SRAM, etc.), ROM, etc.
The present invention offers various advantages by customizing
educational content. Considering SME recommendation while customizing
educational material ensures that the student is presented with material that
is chosen by experienced minds. Further, customizing educational content on
preparation levels allows a guided study experience. Volume of material
encountered is also regulated by comparing the TA for preparation and TR for
consumption of the educational content. Furthermore, the method and
apparatus for providing customized educational content recommendation
allows access to an online community with members having similar
19
preparation levels. Members of such an online community can interact in a
balanced way and benefit from each other.
The methods described herein may be implemented in software,
hardware, or a combination thereof, in different embodiments. In addition, the
order of methods may be changed, and various elements may be added,
reordered, combined, omitted, modified, etc. All examples described herein are
presented in a non-limiting manner. Various modifications and changes may
be made as would be obvious to a person skilled in the art having benefit of
this disclosure. Realizations in accordance with embodiments have been
described in the context of particular embodiments. These embodiments are
meant to be illustrative and not limiting. Many variations, modifications,
additions, and improvements are possible. Accordingly, plural instances may
be provided for components described herein as a single instance. Boundaries
between various components, operations and data stores are somewhat
arbitrary, and particular operations are illustrated in the context of specific
illustrative configurations. Other allocations of functionality are envisioned
and may fall within the scope of claims that follow. Finally, structures and
functionality presented as discrete components in the example configurations
may be implemented as a combined structure or component. These and other
^ variations, modifications, additions, and improvements may fall within the
scope of embodiments as defined in the claims that follow.

20
Claims
1. An apparatus for providing customized educational content
comprising:
an education application server that receives preparation level data for a
student, determines preparation level rating (PLR) for the student and
recommends educational content for the student based on the
W preparation level rating (PLR); and
an education consumption computer that receives the educational
content.
2. The apparatus of claim 1 wherein the education apphcation server
further receives additional customization parameters, and recommends
educational content based on at least one of the preparation level rating
(PLR) and the additional customization parameters.
3. The apparatus of claim 2 wherein additional customization
^ parameters comprise at least one of subject matter expert (SME)
recommendation, time required (TR) to consume a content plug, student
recommendation rating (SRR) for the content plug, level of the content
plug (CPL) and time available (TA) for preparation.
4. The apparatus of claim 1 wherein preparation level data of the
student is based on customized educational content consumed by the
student.
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5. The apparatus of claim 1 wherein preparation level rating is redetermined
when at least one of a preparation level deterministic event
occurs.
6. A computer readable medium for storing processor executable
instructions that, when executed by a computing system, causes the
computing system to cause a method for providing customized
educational content, the method comprising:
receiving preparation level data for a student;
" determining preparation level rating (PLR) for the student; and
recommending educational content for the student based on the
preparation level rating (PLR).
7. The computer readable medium of claim 6, wherein the method
further comprising receiving additional customization parameters, and
recommending educational content based on at least one of the
preparation level rating (PLR) and the additional customization
parameters.
^ 8. The computer readable medium of claim 7, the additional
customization parameters comprise at least one of subject matter expert
(SME) recommendation, time required (TR) to consume a content plug,
student recommendation rating (SRR) for the content plug, level of the
content plug (CPL) and time available (TA) for preparation.
9. The computer readable medium of claim 6 wherein preparation level
data of the student is based on customized educational content consumed
by the student.
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10. The computer readable medium of claim 6 wherein the preparation
level rating (PLR) is determined using at least one of performance of the
student on a diagnostic test (P), level of the diagnostic test (D), the
content plug consumed by the student (c), weightage of the content plug
consumed, and participation rating (N) of the student on a sub-network.