FIELD OF INVENTION
[0001] Embodiments of a present disclosure relates to online learning system, and more particularly to a system for providing a personalized learning material and a method of operation.
BACKGROUND
[0002] A school or an institution prescribe curriculum that are fixed for every individual in same grade. Students with lower-grade marks or higher-grade marks must go through such same prescribed curriculum. So, improvement becomes very difficult as the level of content prescribed is considerably hard for the lower-grade students. Here, reading material prescribed to students are mainly static and same for everyone.
[0003] However, with the advent of information communication devices and wired and wireless communication networks, various schools or institutions have allowed students to access online learning content. Communication devices and wired and wireless communication networks have made such efforts very easy and cost effective.
[0004] More efficient approach would be to customize the learning content according to a student's result. Here, the student may be introduced again and again with the same concept, till such concept become clear. It is vital for any student to keep a track of improvement, hence automatic updating the profile of students is very important. Further, availability of personalized learning content in printed form may enable the student to study easily.
[0005] Hence, there is a need for an improved system for providing a personalized learning material online and in printed textbook form and a method to operate the same and therefore address the aforementioned issues.
BRIEF DESCRIPTION
[0006] In accordance with one embodiment of the disclosure, a system for providing a personalized learning material is disclosed. The system includes a registration module operable by one or more processors. The registration module is configured to register one or more users. The system also includes an evaluation

module operable by the one or more processors. The evaluation module is operatively coupled to the registration module. The evaluation module is configured to evaluate one or more registered users according to specific pre-determined questions.
[0007] The system also includes a profiling module operable by the one or more processors. The profiling module is operatively coupled to the evaluation module. The profiling module is configured to provide a dynamic profile of each of the one or more registered users in accordance to evaluated details in real time by a predictive technique. The profiling module is also configured to provide a year-end profile of each of the one or more registered users in accordance to the evaluated details and real time dynamic profile. Here, the year-end profile is provided by the predictive technique.
[0008] The system also includes a personalization module operable by the one or more processors. The personalization module is operatively coupled to the profiling module. The personalization module is configured to personalize content of a learning material in accordance to the profiling module and the evaluated details of each of the one or more registered users by an analysing technique.
[0009] The system also includes a storing module operable by the one or more processors. The system is operatively coupled to the evaluation module. The storing module is configured to store the evaluated details of each of the one or more registered users.
[0010] In accordance with one embodiment of the disclosure, a method of creating a personalized learning material is provided. The method includes registering one or more users. The method also includes evaluating one or more registered users according to specific pre-determined questions. The method also includes generating a dynamic profile of each of the one or more registered users in accordance to evaluated details in real time by a predictive technique.
[0011] The method also includes generating a year-end profile of each of the one or more registered users in accordance to the evaluated details and real time dynamic profile. The method also includes personalizing content of a learning material in accordance to the profiling module and the evaluated details of each of the one or more

registered users by an analysing technique. The method also includes storing the evaluated details of each of the one or more registered users.
[0012] To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:
[0014] FIG. 1 is a block diagram representation of a system for providing a personalized learning material in accordance with an embodiment of the present disclosure;
[0015] FIG. 2 is a schematic representation of an embodiment representing the system for providing a personalized learning material of FIG. 1 in accordance of an embodiment of the present disclosure;
[0016] FIG. 3 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure; and
[0017] FIG. 4 is a flowchart representing the steps of a method of creating a personalized learning material in accordance with an embodiment of the present disclosure.
[0018] Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that

will be readily apparent to those skilled in the art having the benefit of the description herein.
DETAILED DESCRIPTION
[0019] For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated online platform, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.
[0020] The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or subsystems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, subsystems, elements, structures, components, additional devices, additional subsystems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.
[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.
[0022] In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms "a", "an", and "the" include plural references unless the context clearly dictates otherwise.

[0023] Embodiments of the present disclosure relate to a system for providing a personalized learning material. The system includes a registration module operable by one or more processors. The registration module is configured to register one or more users. The system also includes an evaluation module operable by the one or more processors. The evaluation module is operatively coupled to the registration module. The evaluation module is configured to evaluate one or more registered users according to specific pre-determined questions.
[0024] The system also includes a profiling module operable by the one or more processors. The profiling module is operatively coupled to the evaluation module. The profiling module is configured to provide a dynamic profile of each of the one or more registered users in accordance to evaluated details in real time by a predictive technique. The profiling module is also configured to provide a year-end profile of each of the one or more registered users in accordance to the evaluated details and real time dynamic profile. Here, the year-end profile is provided by the predictive technique.
[0025] The system also includes a personalization module operable by the one or more processors. The personalization module is operatively coupled to the profiling module. The personalization module is configured to personalize content of a learning material in accordance to the profiling module and the evaluated details of each of the one or more registered users by an analysing technique.
[0026] The system also includes a storing module operable by the one or more processors. The system is operatively coupled to the evaluation module. The storing module is configured to store the evaluated details of each of the one or more registered users.
[0027] FIG. 1 is a block diagram representation of a system for providing a personalized learning material (10) in accordance with an embodiment of the present disclosure. In one embodiment, the personalized learning, or personalization, refers to a diverse variety of educational programs, learning experiences, instructional approaches, and academic-support strategies that are intended to address the distinct learning needs, interests, aspirations, or cultural backgrounds of individual students.

[0028] The system (10) includes a registration module (20) operable by one or more processors. The registration module (20) is configured to register one or more users. In one embodiment, the one or more users may be the referred to the students who seek education on a particular subject domain.
[0029] In operation, during registration, the one or more users might provide various details to registered in the learning platform or system (10). In such exemplary embodiment, various details include name, details about subject domain, duration of enrolment and the like.
[0030] The system (10) also includes an evaluation module (30) operable by the one or more processors. The evaluation module (30) is operatively coupled to the registration module (20). The evaluation module (30) is configured to evaluate one or more registered users according to specific pre-determined questions. In one embodiment, specific pre-determined questions relate to a particular domain in accordance to the content requirement of the learning material.
[0031] In above stated exemplary embodiment, a registered user after process of registration may evaluated through questions. For example, a high school mathematics student has to answer mathematic question pertaining to high school. Such evaluation will be vital for further profiling and understanding of the registered user. Here, the system (10) may uses Bloom's taxonomy to structure curriculum learning objectives, assessments and activities for each of the one or more registered users. As used herein, the term "Blooms Taxonomy" is a set of three hierarchical models used to classify educational learning objectives into levels of complexity and specificity.
[0032] The system (10) also includes a profiling module (40) operable by the one or more processors. The profiling module (40) is operatively coupled to the evaluation module (30). The profiling module (40) is configured to provide a dynamic profile of each of the one or more registered users in accordance to evaluated details in real time by a predictive technique.
[0033] The profiling module (40) is also configured to provide a year-end profile of each of the one or more registered users in accordance to the evaluated details and real time dynamic profile, wherein the year-end profile is provided by the predictive technique.

[0034] Here, the predictive technique may comprise of machine learning, artificial intelligence and the like. As used herein, "artificial intelligence" refers to sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals, such as visual perception, speech recognition, decision-making, and translation between languages. As used herein, "machine learning" refers to an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
[0035] Furthermore, the profiling module (40) implements a generated profile in a digital medium for real time analysis. Here, any time the one or more registered users may access the profile and understand the status of learning. In such embodiment, the digital medium may include any handheld device.
[0036] Moreover, at first dynamic profile is being created after each evaluation test related to any subject matter. And secondly, the year profile reflects the cumulative studying of any of the one or more registered users throughout the year. In such embodiment, both generated profiles are necessary yardstick for personalization level.
[0037] The system (10) also includes a personalization module (50) operable by the one or more processors. The personalization module (50) is operatively coupled to the profiling module (40). The personalization module (50) is configured to personalize content of a learning material in accordance to the profiling module and the evaluated details of each of the one or more registered users by an analysing technique.
[0038] Here, the learning material may be personalized notes, personalized questions and personalized concept videos. Learning material or course content material is auto decided by individual strength strengths and weaknesses. In one embodiment, the system (10) understand through profile and analyses what material is suitable for each of the one or registered users in real time. In such embodiment, the personalized learning material or course content material is printable for usage by each of the one or more registered users. Here, the analysing technique may comprise of machine learning, artificial intelligence and the like.

[0039] In one embodiment, the system (10) enables providing to one or more registered users with printed personalized books. Here, the printed personalized books are reproduced according to personalized content of a learning material as provided by the personalization module (50). Here, the printed personalized books take into consideration the data from profiling module (40) and the evaluated details of each of the one or more registered users. In one exemplary embodiment, a monthly personalized book may be provided after the one or more registered users undergoes monthly profiling or any dynamic profiling. Moreover, the printed personalized books may contain Uniform Resource Locator (URL) of the personalized concept videos as provided by the system (10).
[0040] The system (10) also includes a storing module (60) operable by the one or more processors. The storage module (60) is operatively coupled to the evaluation module (30). The storage module (60) is configured to store the evaluated details of each of the one or more registered users. In one embodiment, the storage module (60) may store the dynamic profile as well as year-end profile of each of the one or more registered users. Here, any registered may be permitted internally to view the generated profile. Moreover, the storage may be enabled in a remote storage or a local storage.
[0041] FIG. 2 is a schematic representation of an embodiment representing the system for providing a personalized learning material (10) of FIG. 1 in accordance of an embodiment of the present disclosure. At first instance, a student X (70) from a reputed school registers into the system (10) through a registration module (20). The student X (70) provide name, class details, course details etc.
[0042] The system (10) mainly classifies educational learning objectives into levels of complexity and specificity. Hence, through the evaluation module (30), the system (10) evaluates the student X (70) according to specific pre-determined questions. The questions are mainly built around the knowledge domain of course applied. The evaluation basically provides the standard of knowledge the student X (70) has on a required subject domain.
[0043] After evaluation, a dynamic profile of the student X (70) is created. The dynamic profile may be accessible by the student X (70) school or parent of the student

X (70). Moreover, according to the evaluation result, the system (10) personalizes the course content material (80). Here, the course content material (80) may be personalized notes, personalized questions and personalized concept videos (URL) in printed textbook form. Course content material (80) is auto decided by individual strength strengths and weaknesses.
[0044] In one exemplary embodiment, if the student X (70) performs good on first evaluation test, the curriculum learning objectives, assessments and activities are provided for a tougher level. In another exemplary embodiment, if the student X (70) performs bad on first evaluation test, the curriculum learning objectives, assessments and activities are provided for a simpler level, and hence more doubt clearing classes. The system (10) uses bloom taxonomy hierarchical models to provide proper course content material (80) and make the student X (70) understand every concept related to the course.
[0045] To track the year end performance, a year end profile is also generated by the profiling module (40). And personalization of the content of the learning material is done by a personalization module (50). All the evaluation test result of the student X (70), the dynamic profile as well as year-end profile are being stored by the storage module (60). Moreover, according to the dynamic profile as well as the year-end profile, the personalized course content material (80) are provided in printed book form. In another embodiment, the personalized course content material may be in electronic-book form.
[0046] The registration module (20), the evaluation module (30), the profiling module (40), the personalization module (50) and the storing module (60) in FIG. 2 is substantially equivalent to the registration module (20), the evaluation module (30), the profiling module (40), the personalization module (50) and the storing module (60) of FIG. 1.
[0047] FIG. 3 is a block diagram of a computer or a server (90) in accordance with an embodiment of the present disclosure. The server (90) includes processor(s) (120), and memory (100) coupled to the processor(s) (120).
[0048] The processor(s) (120), as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex

instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.
[0049] The memory (100) includes a plurality of modules stored in the form of executable program which instructs the processor (120) to perform the method steps illustrated in Fig 1. The memory (100) has following modules: the registration module (20), the evaluation module (30), the profiling module (40), the personalization module (50) and the storing module (60).
[0050] The registration module (20) is configured to register one or more users. The evaluation module (30) is configured to evaluate one or more registered users according to specific pre-determined questions.
[0051] The profiling module (40) is configured to provide a dynamic profile of each of the one or more registered users in accordance to evaluated details in real time by a predictive technique. The profiling module (40) is also configured to provide a year-end profile of each of the one or more registered users in accordance to the evaluated details and real time dynamic profile, wherein the year-end profile is provided by the predictive technique.
[0052] The personalization module (50) is configured to personalize content of a learning material in accordance to the profiling module and the evaluated details of each of the one or more registered users by an analysing technique. The storing module (60) is configured to store the evaluated details of each of the one or more registered users.
[0053] Computer memory elements may include any suitable memory device(s) for storing data and executable program, such as read only memory, random access memory, erasable programmable read only memory, electrically erasable programmable read only memory, hard drive, removable media drive for handling memory cards and the like. Embodiments of the present subject matter may be implemented in conjunction with program modules, including functions, procedures, data structures, and application programs, for performing tasks, or defining abstract

data types or low-level hardware contexts. Executable program stored on any of the above-mentioned storage media may be executable by the processor(s) (120).
[0054] FIG. 4 is a flowchart representing the steps of a method for creating a personalized learning material (130) in accordance with an embodiment of the present disclosure. The method includes registering one or more users in step 140. In one embodiment, registering the one or more users includes registering by a registration module.
[0055] The method (130) also includes evaluating one or more registered users according to specific pre-determined questions in step 150. In one embodiment, evaluating the one or more registered users according to the specific pre-determined questions includes evaluating the one or more registered users according to the specific pre-determined questions by an evaluation module.
[0056] In one embodiment, evaluating the one or more registered users according to the specific pre-determined questions includes evaluating the one or more registered users according to the specific pre-determined questions includes evaluating the one or more registered users according to the specific pre-determined questions relating to a particular domain in accordance to the content requirement of the learning material.
[0057] The method (130) also includes generating a dynamic profile of each of the one or more registered users in accordance to evaluated details in real time by a predictive technique in step 160. In one embodiment, generating the dynamic profile of each of the one or more registered users in accordance to evaluated details in real time by a predictive technique includes generating the dynamic profile of each of the one or more registered users in accordance to evaluated details in real time by a profiling module.
[0058] The method (130) also includes generating a year-end profile of each of the one or more registered users in accordance to the evaluated details and real time dynamic profile in step 170. In one embodiment, generating the year-end profile of each of the one or more registered users in accordance to the evaluated details and the real time dynamic profile includes generating the year-end profile of each of the one or more registered users in accordance to the evaluated details and the real time dynamic profile by the profiling module. In such embodiment, generating the year-

end profile of each of the one or more registered users includes a generated profile in a digital medium for real time analysis.
[0059] The method (130) also includes personalizing content of a learning material in accordance to the profiling module and the evaluated details of each of the one or more registered users by an analysing technique in step 180. In one embodiment, personalizing the content of the learning material in accordance to the profiling module and the evaluated details of each of the one or more registered users by an analysing technique includes personalizing the content of the learning material in accordance to the profiling module and the evaluated details of each of the one or more registered users by a personalization module.
[0060] In one embodiment, personalizing the content of a learning material in accordance to the profiling module and the evaluated details of each of the one or more registered users by an analysing technique includes personalizing the learning material comprising a personalised printed workbook and a personalized printed textbook.
[0061] The method (130) also includes storing the evaluated details of each of the one or more registered users in step 190. In one embodiment, storing the evaluated details of each of the one or more registered users includes storing the evaluated details of each of the one or more registered users by a storage module.
[0062] Present disclosure describes a personalized learning material which is personalized in real time according to evaluation results. Such system uses Bloom taxonomy to plan curriculum after the evaluation module evaluates any student. Moreover, a real time profile is maintained by such system for constant tracking. Here, personalized course content material may be personalized notes, personalized questions and personalized concept videos, the personalized course content may be in printed textbook and printed workbook.
[0063] While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.

[0064] The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependant on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.

WE CLAIM

1.A personalized learning material (10), comprising:
a registration module (20) operable by one or more processors, and configured to register one or more users;
an evaluation module (30) operable by the one or more processors, and operatively coupled to the registration module (20), and the evaluation module (30) is configured to evaluate one or more registered users according to specific pre-determined questions;
a profiling module (40) operable by the one or more processors, and operatively coupled to the evaluation module (30), wherein the profiling module (40) is configured to provide:
a dynamic profile of each of the one or more registered users in accordance to evaluated details in real time by a predictive technique;
a year-end profile of each of the one or more registered users in accordance to the evaluated details and real time dynamic profile, wherein the year-end profile is provided by the predictive technique;
a personalization module (50) operable by the one or more processors, and operatively coupled to the profiling module (40), wherein the personalization module (50) is configured to personalize content of a learning material in accordance to the profiling module and the evaluated details of each of the one or more registered users by an analysing technique; and
a storing module (60) operable by the one or more processors, and operatively coupled to the evaluation module (30), wherein the storing module (60) is configured to store the evaluated details of each of the one or more registered users.
2. The personalized learning material (10) as claimed in claim 1, wherein specific
pre-determined questions relates to a particular domain in accordance to the content
requirement of the learning material.

3. The personalized learning material (10) as claimed in claim 1, wherein the profiling module implements a generated profile in a digital medium for real time analysis.
4. The personalized learning material (10) as claimed in claim 1, wherein the learning material comprises a workbook, a video and a textbook.
5. The method (130) of creating a personalized learning material, comprises:
registering, by a registration module, one or more users (140);
evaluating, by an evaluation module, one or more registered users according to specific pre-determined questions (150);
generating, by a profiling module, a dynamic profile of each of the one or more registered users in accordance to evaluated details in real time by a predictive technique (160);
generating, by the profiling module, a year-end profile of each of the one or more registered users in accordance to the evaluated details and real time dynamic profile (170);
personalizing, by a personalization module, content of a learning material in accordance to the profiling module and the evaluated details of each of the one or more registered users by an analysing technique (180); and
storing, by a storage module, the evaluated details of each of the one or more registered users (190).
6. The method (130) as claimed in claim 5, wherein evaluating, by an evaluation module, the one or more registered users according to specific pre-determined questions relating to a particular domain in accordance to the content requirement of the learning material.
7. The method (130) as claimed in claim 5, wherein implementing, by the profiling module, a generated profile in a digital medium for real time analysis.

8. The method (130) as claimed in claim 5, wherein personalizing, by a
personalization module, the learning material comprises a workbook, a video and a textbook.