BACKGROUND

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 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 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 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 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", "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 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

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 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 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 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, visualization 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 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 meta-tags 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 others learning. The sub-network participation data 158 is a measure of activity of the student on the sub-network. The sub-network participation data 158 may, for example, include the time spent by the student interacting on the sub-network, number of sub-network members that the student has interacted with, number of members liking a recommendation for a study material made by the student. The sub-network 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

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 sub-network 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 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 Figural. 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 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 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 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 sub-network is obtained. In one embodiment, participation rating on the sub-network 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 + CM + 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.

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 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 re-determined 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 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 subnet work 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 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 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 (CD-ROM).

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 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 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, 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 processor-executable executable program instructions 622 (e.g., program instructions executable by processor(s) 610) in various embodiments.

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 6QQ, may be configured to host 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 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, 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.

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 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 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 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 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 pombined 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.

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 preparation level rating (PLR); and an education consumption computer that receives the educational content.

2. The apparatus of claim 1 wherein the education application 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.

5. The apparatus of claim 1 wherein preparation level rating is re-determined 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.

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.