DESC:FIELD
The present invention relates to the field of content management and more particularly to automatic content generation, upgradation and management.
BACKGROUND
The background information herein below relates to the present disclosure but is not necessarily prior art.

For the majority of academic institutes, educational course development and updating remains a challenging, expensive and time-consuming process often requiring repetitive hands-on involvement by experts. Only a fortunate number of academic institutions can afford to have specialized in-house curriculum development groups.

Irrespective of the grades or course levels, instructors with limited time and resources may preferably repeat lessons for each academic term with only minor modifications rather than put the time and substantial effort required in to develop new course materials and curriculums. As educational mandates are enforced and financial challenges are faced at all academic levels, it is more and more critical that an institution upgrades and manages course content to assure alignment with competency requirements to maintain and achieve competitive advantages over other institutions.

For example, a training center that keeps imparting skills to consecutive batches in a selected domain, needs to keep updating its curricula to keep the skills relevant so as to effectively apply the knowledge to solve day-to day problems surfacing in the domain.

As there is a rapid addition of skills to the body of knowledge, and reinvention of few old skills due to a new application, it becomes imperative for the center to continuously monitor and update its syllabus for the selected domain.

Methods of content upgradation disclosed in the prior art include manual updating of the syllabus. However, in some cases it is difficult to identify and comprehend updated/relevant skills manually and hence are ignored. Further, the duration of the course is manually decided. The reference materials are also manually searched and are browsed through manually to decide the best materials to refer.

There is, therefore, a felt need for a computer implemented system for content upgradation of a learning resource and a method thereof for automatically managing content related to a course and/or domain and/or subject and recommend a schedule for teaching the content and also relevant online and offline resources that describe or augment the content.

OBJECTS
Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows:
An object of the present disclosure is to provide a computer implemented system for content upgradation of a learning resource and a method thereof.
Another object of the present disclosure is to provide a system that automatically update an existing content using online and/or offline resources.

Yet another object of the present disclosure is to provide a system that generate content during the absence of any previous content.

Still another object of the present disclosure is to provide a system to prepare and recommend a teaching schedule including time for teaching each relevant content.

Another object of the present disclosure is to provide a system to recommend online and/or offline resources for detailing and/or supplementing the content.
Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.
SUMMARY
The present disclosure envisages a computer implemented system for content upgradation of a learning resource and a method thereof.
The computer implemented system for content upgradation of a learning resource comprises a receiver, a first parser, a repository, a first crawler and extractor, a second parser, a second crawler and extractor, and a scheduler.
The receiver is configured to receive a user’s request. The user’s request is a timer-based request or a topic based request.
The first parser is configured to cooperate with the receiver to parse the user’s request for identifying the request as deletion, addition or modification, and is further configured to read the identified request to identify relevant content to be added or modified. The first parser is configured to parse the request into a set of tokens and identify the request, and further includes a reader to read the tokens to identify the relevant content to be added or modified.
The repository is configured to store a list of addresses of a plurality of pre-selected sites, a set of pre-determined skill identifying rules, a set of pre-determined schedule planning rules, and a look up table having a list of pre-determined requisite skills and at least one pre-requisite skill for each of the requisite skills.
The first crawler and extractor is configured to cooperate with the first parser wherein the crawler is configured to crawl each of the pre-selected sites via the internet to mark the relevant content and the extractor is configured to extract and mirror the relevant content to a temporary repository.
The plurality of pre-selected sites comprises online forums, discussions on social networking sites/ social media, web based encyclopedia like Wikipedia, data from the online courses, research publications and the table of contents (ToC) of books pertaining to the course.
The second parser is configured to cooperate with the temporary repository to read the mirrored content, and is further configured to identify skill set required by the user to comprehend the mirrored content based on the skill identifying rules and the look up table.
The second parser includes a skill identifier, a pre-requisite skill identifier, a skill set generator, and a skill ranking module.
The skill identifier is configured to cooperate with the temporary repository and the repository to identify the requisite skills based on the mirrored content and the pre-determined skill identifying rules.
The pre-requisite skill identifier is configured to cooperate with the skill identifier and the repository to identify the pre-requisite skills for each of the requisite skills based on the look up table and the pre-determined pre-requisite skill identifying rules stored in the repository.
The skill set generator is configured to cooperate with the skill identifier and the pre-requisite skill identifier to form the skill set by combining the identified requisite skills with corresponding pre-requisite skills identified.
The skill ranking module is configured to cooperate with the skill set generator and repository to rank the identified requisite skills in the skill set based on the pre-determined skill ordering rules and the pre-determined parameters stored in the repository.
The second crawler and extractor is configured to cooperate with the second parser to crawl the pre-selected sites to identify and extract a plurality of topics matching the skill set required.
The scheduler is configured to cooperate with the second crawler and extractor and the repository to prepare a schedule for acquiring the skill set from the pre-selected sites for comprehending the extracted topics based on the pre-determined schedule planning rules.
The scheduler includes a source selector and a planner.
The source selector is configured to cooperate with the second crawler and extractor to select the pre-selected sites based on the extracted topics.
The planner is configured to cooperate with the source selector to plan the schedule acquiring the skill set from the pre-selected sites for comprehending the extracted topics based on the pre-determined schedule planning rules.
The receiver, first parser, first crawler and extractor, second parser, second crawler and extractor, and a scheduler are implemented using one or more processor(s).
The system further comprises a recommender configured to cooperate with the scheduler to recommend the schedule and the pre-selected sites to the user.
The system implements natural language processing, vocabulary and phrase matching (similar to use of a thesaurus), machine learning, and other artificial intelligence technologies.

The present disclosure envisages a method for content upgradation of a learning resource.
BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWING
A computer implemented system for content upgradation of a learning resource and a method thereof of the present disclosure will now be described with the help of the accompanying drawing, in which:
Figure 1 illustrates a block diagram of the computer implemented system for content upgradation of a learning resource and a method thereof; and
Figure 2a and 2b illustrate a flow diagram of a method for content upgradation of a learning resource.
LIST OF REFERENCE NUMERALS
100 System
102 receiver
104 first parser
106 repository
108 first crawler and extractor
110 temporary repository
112 second parser
114 second crawler and extractor
116 scheduler
118 skill identifier
120 pre-requisite skill identifier
122 skill set generator
124 skill ranking module
126 recommender
128 source selector
130 planner
132 reader
DETAILED DESCRIPTION
Embodiments, of the present disclosure, will now be described with reference to the accompanying drawing.
Embodiments are provided so as to thoroughly and fully convey the scope of the present disclosure to the person skilled in the art. Numerous details, are set forth, relating to specific components, and methods, to provide a complete understanding of embodiments of the present disclosure. It will be apparent to the person skilled in the art that the details provided in the embodiments should not be construed to limit the scope of the present disclosure. In some embodiments, well-known processes, well-known apparatus structures, and well-known techniques are not described in detail.
The terminology used, in the present disclosure, is only for the purpose of explaining a particular embodiment and such terminology shall not be considered to limit the scope of the present disclosure. As used in the present disclosure, the forms "a,” "an," and "the" may be intended to include the plural forms as well, unless the context clearly suggests otherwise. The terms "comprises," "comprising," “including,” and “having,” are open ended transitional phrases and therefore specify the presence of stated features, elements, modules, units and/or components, but do not forbid the presence or addition of one or more other features, elements, components, and/or groups thereof.
A computer implemented system for content upgradation of a learning resource and a method thereof of the present disclosure, is described with reference to Figure 1 through Figure 2b.
Referring to Figure 1, the computer implemented system for content upgradation of a learning resource (hereinafter referred as “system”) (100) comprises a receiver (102), a first parser (104), a repository (106), a first crawler and extractor (108), a second parser (112), a second crawler and extractor (114), and a scheduler (116).
The receiver (102) is configured to receive a user’s request. In an embodiment, the user is selected from the group of, but not limited to, an instructor, professor, teacher and/or faculty appointed by an educational institute or training institute for delivering the content. In another embodiment, the user sends the request via the user device. The user device is selected from group consisting of, but not limited to, a desktop computer, a laptop computer, a notebook, a netbook, a tablet personal computer (PC), a smart phone, a mobile phone, a personal digital assistant (PDA), and/or any other suitable device operable to send and receive data and display data.
In an embodiment, the user’s request is a timer-based request or a topic based request. In another embodiment, the time based request is generated after the lapse of a predetermined time interval set by the user whereas the topic/content based request is generated after monitoring an increase in activity, beyond a predetermined threshold, concerning a particular topic. In one embodiment, the topic based request is prioritized over the time based request.

Accordingly, in one exemplary embodiment, upon monitoring a sudden increase in the activity related to a new topic relevant to a course, a topic based request is generated for modifying the content associated with the course.

The first parser (104) is configured to cooperate with the receiver (102) to parse the user’s request for identifying the request as deletion, addition or modification, and is further configured to read the identified request to identify relevant content to be added or modified. The first parser (104) is configured to parse the request into a set of tokens and identify the request, and further includes a reader (132) to read the tokens to identify the relevant content to be added or modified.
In an embodiment, for the deletion request the old content is deleted and replaced with a new content. For the addition and the modification, new content is added.
In another embodiment, the content includes any written, electronic, photographic, video graphic, reading materials (e.g. textbooks), assessments (e.g. quizzes, tests, and exams); lesson plans; and observation guides.

The learning resource is selected from the group consisting of, but no limited to, a course, syllabus, curriculum, and books.

In an embodiment, the relevant content to be added or modified includes one or more questions that have been frequently viewed and/or liked and/or commented on the pre-selected sites, determining one or more new skills that are on demand and tracing the trend and determining the fundamentals that are still relevant.

The repository (106) is configured to store a list of addresses of a plurality of pre-selected sites, a set of pre-determined skill identifying rules, a set of pre-determined schedule planning rules, and a look up table having a list of pre-determined requisite skills and at least one pre-requisite skill for each of the requisite skills.
The first crawler and extractor (108) is configured to cooperate with the first parser (104) wherein the crawler is configured to crawl each of the pre-selected sites via the internet to mark the relevant content and the extractor is configured to extract and mirror the relevant content to a temporary repository (110).
In an embodiment, the plurality of pre-selected sites is monitored on a periodic basis. The plurality of pre-selected sites comprises online forums, discussions on social networking sites/ social media, web based encyclopedia like Wikipedia, data from the online courses, research publications and the table of contents (ToC) of books pertaining to the course.
In another embodiment, one or more website crawlers are used for crawling pre-selected sites. A website crawler is a software program used to scan various websites, reading the content (and other information) so as to generate content based on a search string. The website crawlers typically work on submissions made by web site owners and “crawl” new or recently modified sites and pages, to update the content.
In one embodiment, the relevant content is merged with the existing content to generate a course content. The existing content includes one or more text books, suggested books for reference and existing course material. The existing content further includes old versions of syllabus of the course and their time stamps. In an alternative embodiment, the relevant content itself serves as a course content during the absence of an existing content.
In an embodiment, the course content thus generated is stored in a structured format in a suitable database such as MongoDB TM (JSON format) which is an open source database that uses a document-oriented data model. Instead of using tables and rows as in relational databases, MongoDB TM is built on an architecture of collections and documents. Documents comprise sets of key-value pairs and are the basic unit of data in MongoDB. Collections contain sets of documents and function as the equivalent of relational database tables.
The second parser (112) is configured to cooperate with the temporary repository (110) to read the mirrored content, and is further configured to identify skill set required by the user to comprehend the mirrored content based on the skill identifying rules and the look up table.
The second parser (112) includes a skill identifier (118), a pre-requisite skill identifier (120), a skill set generator (122), and a skill ranking module (124).
The skill identifier (118) is configured to cooperate with the temporary repository (110) and the repository (106) to identify the requisite skills based on the mirrored content and the pre-determined skill identifying rules.
The pre-requisite skill identifier (120) is configured to cooperate with the skill identifier (118) and the repository (106) to identify the pre-requisite skills for each of the requisite skills based on the look up table and the pre-determined pre-requisite skill identifying rules stored in the repository (106).
In one exemplary embodiment, the requisite skills are computer programming language C Sharp for which Object Oriented Programming is a pre-requisite.
The skill set generator (122) is configured to cooperate with the skill identifier (118) and the pre-requisite skill identifier (120) to form the skill set by combining the identified requisite skills with corresponding pre-requisite skills identified.
The skill ranking module (124) is configured to cooperate with the skill set generator (122) and repository (106) to rank the identified requisite skills in the skill set based on the pre-determined skill ordering rules and the pre-determined parameters stored in the repository (106).
The second crawler and extractor (114) is configured to cooperate with the second parser (112) to crawl the pre-selected sites to identify and extract a plurality of topics matching the skill set required.
In an embodiment, each requisite skill is associated with one or more topics and the rank of the skills are determined based on the parameters such as frequency of the appearance of the associated topics within the additional content, the number of times a concept is taught or mentioned in the curricular materials, the quality of the source of the material (determined using, for example, citations), and the relevance to other curriculum terms. This may be performed using natural language processing, vocabulary and phrase matching (similar to use of a thesaurus), machine learning, and other artificial intelligence technologies.
A selected number of topics are included in the course content based on the ranking of the requisite skills.
The scheduler (116) is configured to cooperate with the second crawler and extractor (114) and the repository (106) to prepare a schedule for acquiring the skill set from the pre-selected sites for comprehending the extracted topics based on the pre-determined schedule planning rules.
The scheduler (116) includes a source selector (128) and a planner (130).
The source selector (128) is configured to cooperate with the second crawler and extractor (114) to select the pre-selected sites based on the extracted topics.
The planner (130) is configured to cooperate with the source selector (128) to plan the schedule acquiring the skill set from the pre-selected sites for comprehending the extracted topics based on the pre-determined schedule planning rules.
In an embodiment, the pre-selected sites are online and offline resources. Online resources are selected from the group, but not limited to, the Coursera TM, Skillshare TM and edX TM. Offline resources are selected from the group, but not limited to, one or more text books, reference books or newly published books. The table of contents for the online and offline resources are identified and accordingly selection of one or more resources that provide information on one or more topics related to the relevant content is selected.
In another embodiment, the schedule is prepared based on complexity and vastness of the one or more topics associated with the relevant content. The schedule is prepared considering the fact that a complex topic demands many hours of teaching schedule when compared to a comparatively simpler one.
The system (100) further comprises a recommender (126) configured to cooperate with the scheduler (116) to recommend the schedule and the pre-selected sites to the user. In an embodiment, the schedule and the selected resources are recommended to the user. The recommended resources comprise one or more online and offline resources that provide detailed information regarding the course content or augment the course content. The offline resources comprise printed material such as books, magazines or publications. The recommended offline resource includes a part of the printed material based on the table of content.
The receiver (102), first parser (104), first crawler and extractor (108), second parser (112), second crawler and extractor (114), scheduler (116), and a recommender (126) are implemented using one or more processor(s).
The processor may be a general-purpose processor, a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), and/or the like. The processor may be configured to retrieve data from and/or write data to the memory. The memory can be for example, a random access memory (RAM), a memory buffer, a hard drive, a database, an erasable programmable read only memory (EPROM), an electrically erasable programmable read only memory (EEPROM), a read only memory (ROM), a flash memory, a hard disk, a floppy disk, cloud storage, and/or so forth.
In an embodiment, the content is encrypted, and distributed or stored in a storage device such as portable storage, flash storage, Compact Disk (CD, Digital Video disk (DVD), pen drive, and cloud store. A decryption key is used for decrypting the content and distributed only to authorized users. The authorized users decrypt the encrypted content by using the distributed decryption key so as to access the content.
In another embodiment, access to the content is provided upon verifying a digital signature of an issuing entity of the content usage-right certificate.
Figures 2a and 2b illustrate a flow diagram of a method for content upgradation of a learning resource.
The steps include:
• Step 202: receiving (202), by a receiver (102), a user’s request;
• Step 204: parsing (204), by a first parser (104), the user’s request for identifying the request as deletion, addition or modification;
• Step 206: reading (206), by the first parser (104), the identified request to identify relevant content to be added or modified;
• Step 208: • storing (208), by a repository (106), a list of addresses of a plurality of pre-selected sites, a set of pre-determined skill identifying rules, a set of pre-determined schedule planning rules, and a look up table having a list of pre-determined requisite skills and at least one pre- requisite skill for each of the requisite skills;
• Step 210: crawling (210), by a first crawler and extractor (108), each of the pre-selected sites via the internet to mark the relevant content;
• Step 212: extracting and mirroring (212), by the first crawler and extractor (108), the relevant content to a temporary repository (110);
• Step 214: reading (214), by a second parser (112), the mirrored content, and further configured to identify skill set required by the user to comprehend the mirrored content based on the skill identifying rules and the look up table;
• Step 216: crawling (216), by a second crawler and extractor (114), the pre-selected sites to identify and extract a plurality of topics matching the skill set required; and
• Step 218: preparing (218), by a scheduler (116), a schedule for acquiring the skill set from the pre-selected sites for comprehending the extracted topics based on the pre-determined schedule planning rules.

TECHNICAL ADVANCEMENTS
The present disclosure described herein above has several technical advantages including, but not limited to, the realization of, a computer implemented system for content upgradation of a learning resource and a method thereof, which:
• automatically update an existing content using online and/or offline resources;
• generate content during the absence of any previous content;
• prepare and recommend a teaching schedule including time for teaching each relevant content; and
• recommend online and/or offline resources for detailing and/or supplementing the content.
The foregoing disclosure has been described with reference to the accompanying embodiments which do not limit the scope and ambit of the disclosure. The description provided is purely by way of example and illustration.
The embodiments herein and the various features and advantageous details thereof are explained with reference to the non-limiting embodiments in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
The foregoing description of the specific embodiments so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.
Throughout this specification the word “comprise”, or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element, step, or group of elements, steps, but not the exclusion of any other element, or step, or group of elements, or steps.
The use of the expression “at least” or “at least one” suggests the use of one or more elements or ingredients or quantities, as the use may be in the embodiment of the disclosure to achieve one or more of the desired objects or results.
While considerable emphasis has been placed herein on the components and component parts of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment as well as other embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.

,CLAIMS:WE CLAIM
1. A computer implemented system (100) for content upgradation of a learning resource, said system (100) comprising:
• a receiver (102) configured to receive a user’s request;
• a first parser (104) configured to cooperate with said receiver (102) to parse said user’s request for identifying said request as a requirement for deletion, addition or modification, and further configured to read said identified request to identify relevant content to be added or modified;
• a repository (106) configured to store a list of addresses of a plurality of pre-selected sites, a set of pre-determined skill identifying rules, a set of pre-determined schedule planning rules, and a look up table having a list of pre-determined requisite skills and at least one pre-requisite skill for each of said requisite skills;
• a first crawler and extractor (108) configured to cooperate with said first parser (104) wherein said crawler is configured to crawl each of said pre-selected sites via the internet to mark said relevant content and said extractor is configured to extract and mirror said relevant content to a temporary repository (110);
• a second parser (112) configured to cooperate with said temporary repository (110) to read said mirrored content, and further configured to identify at least one skill set required by said user to comprehend said mirrored content based on said skill identifying rules and said look up table;
• a second crawler and extractor (114) configured to cooperate with said second parser (112) to crawl said pre-selected sites to identify and extract a plurality of topics matching said skill set required; and
• a scheduler (116) configured to cooperate with said second crawler and extractor (114) and said repository (106) to prepare a schedule for acquiring said skill set from said pre-selected sites for comprehending said extracted topics based on said pre-determined schedule planning rules,
wherein said receiver (102), said first parser (104), said first crawler and extractor (108), said second parser (112), said second crawler and extractor (114) and said scheduler (116) are implemented using one or more processor(s).
2. The system (100) as claimed in claim 1, wherein said pre-selected sites are selected from the group of online forums, discussions on social networking sites/social media, web based encyclopedia, data from the online courses, research publications and the table of contents (ToC) of books.
3. The system (100) as claimed in claim 1, wherein said first parser (104) is configured to parse said request into a set of tokens and identify said request, and further includes a reader (132) to read said tokens to identify said relevant content to be added or modified.
4. The system (100) as claimed in claim 1, wherein said second parser (112) includes:
• a skill identifier (118) configured to cooperate with said temporary repository (110) and said repository (106) to identify said requisite skills based on said mirrored content and said pre-determined skill identifying rules;
• a pre-requisite skill identifier (120) configured to cooperate with said skill identifier (118) and said repository (106) to identify said pre-requisite skills for each of said requisite skills based on said look up table and said pre-determined pre-requisite skill identifying rules stored in said repository (106);
• a skill set generator (122) configured to cooperate with said skill identifier (118) and said pre-requisite skill identifier (120) to form said skill set by combining said identified requisite skills with corresponding pre-requisite skills identified; and
• a skill ranking module (124) configured to cooperate with said skill set generator (122) and repository (106) to rank said identified requisite skills in said skill set based on said pre-determined skill ordering rules and said pre-determined parameters stored in said repository (106).
5. The system (100) as claimed in claim 1, wherein said scheduler (116) includes:
• a source selector (128) configured to cooperate with said second crawler and extractor (114) to select said pre-selected sites based on said extracted topics; and
• a planner (130) configured to cooperate with said source selector (128) to plan said schedule acquiring said skill set from said pre-selected sites for comprehending said extracted topics based on said pre-determined schedule planning rules.
6. The system (100) as claimed in claim 1, wherein said user’s request is a timer-based request or a topic based request.
7. The system (100) as claimed in claim 1, wherein said system (100) further comprises a recommender (126) configured to cooperate with said scheduler (116) to recommend said schedule and said pre-selected sites to said user.
8. The system (100) as claimed in claim 1, wherein said system (100) implements natural language processing, vocabulary and phrase matching, machine learning, and other artificial intelligence technologies.
9. A method for content upgradation of a learning resource, said method comprises the following steps:
• receiving (202), by a receiver (102), a user’s request;
• parsing (204), by a first parser (104), said user’s request for identifying said request as deletion, addition or modification;
• reading (206), by said first parser (104), said identified request to identify relevant content to be added or modified;
• storing (208), by a repository (106), a list of addresses of a plurality of pre-selected sites, a set of pre-determined skill identifying rules, a set of pre-determined schedule planning rules, and a look up table having a list of pre-determined requisite skills and at least one pre-requisite skill for each of said requisite skills;
• crawling (210), by a first crawler and extractor (108), each of said pre- selected sites via the internet to mark said relevant content;
• extracting and mirroring (212), by said first crawler and extractor (108), said relevant content to a temporary repository (110);
• reading (214), by a second parser (112), said mirrored content, and further configured to identify skill set required by said user to comprehend said mirrored content based on said skill identifying rules and said look up table;
• crawling (216), by a second crawler and extractor (114), said pre- selected sites to identify and extract a plurality of topics matching said skill set required; and
• preparing (218), by a scheduler (116), a schedule for acquiring said skill set from said pre-selected sites for comprehending said extracted topics based on said pre-determined schedule planning rules.