Claims:1. A method for measuring and quantifying skills of a plurality of learners, the method comprising a processor implemented steps of:
computing a credit score indicating the skills of the plurality of learners, wherein the credit score is indicative of the learnings completed across a plurality of specializations;
updating the credit score of the each of the plurality of learners based on a new learning of the respective learner over a period of time using a decay rate function;
deriving a distributed weight for the plurality of learners based on the types of learnings completed by the respective learners across the breadth and depth of skills;
calculating a T-factor of each of the plurality of learners using the updated credit score and the distributed weight across the plurality of specializations; and
recommending a set of recommendations to the plurality of learners based on the calculated T-factor in order to improve the T-factor of the plurality of learners.

2. The method of claim 1, further comprising the step of utilizing the calculated T-factor to determine the usefulness of the plurality of learners in an organization.

3. The method of claim 1, wherein the set of recommendations include at least one of a learning activity to undertake, setting up mentoring sessions, participate in contests, answering technical queries, contributing technical content for learning or conducting training sessions for peers and customers.

4. The method of claim 1, further comprising the step of displaying the T-factor on a display device in the form of alphabet ‘T’ indicating breadth and depth.

5. The method of claim 1, wherein the calculation of the T-factor involves balancing the skew-ness in depth and breadth of the skills.

6. The method of claim 1, wherein the decay rate function is based on exponentially decay function, thereby deriving skill relevance.

7. The method of claim 1, wherein the type of learnings involve learning within an organization or the learnings undertaken outside the organization.

8. The method of claim 1, wherein the T-factor is in the range of zero to three.

9. The method of claim 1, further comprising the step of comparing the calculated T-factor with a predefined T-factor.

10. A system for measuring and quantifying skillset of a plurality of learners, the system comprising:
a user interface,
a memory;
a processor in communication with the memory, wherein the processor further comprising:
a credit framework for computing a credit score indicating the skills of the plurality of learners, wherein the credit score is indicative of the learnings completed across a plurality of specializations;
a credit decay engine for updating the credit score of the each of the plurality of learners based on a new learning of the respective learners over a period of time using a decay rate function;
a distributed weight calculation module for deriving a distributed weight for the plurality of learners based on the types of learnings completed by the respective learners across the breadth and depth of skills;
a T-factor calculation module for calculating a T-factor of the plurality of learners using the updated credit score and the distributed weight across the plurality of specializations; and
a bot recommender for recommending a set of recommendations to the plurality of learners based on the calculated T-factor in order to improve the T-factor of the plurality of learners; and
a display device for displaying the calculated T-factor in graphical form.

11. The system of claim 10, wherein the T-factor is in the range of zero to three.
, Description:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:
METHOD AND SYSTEM FOR MEASURING AND QUANTIFYING THE SKILLS OF LEARNERS

Applicant:
Tata Consultancy Services Limited
A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th Floor,
Nariman Point, Mumbai 400021,
Maharashtra, India

The following specification particularly describes the invention and the manner in which it is to be performed.
TECHNICAL FIELD
[001] The embodiments herein generally relates to the field of competency assessment of a plurality of learners, and, more particularly, to a method and system for measuring and quantifying skills of a plurality of learners and their usefulness, particularly in an organization.

BACKGROUND
[002] In the current scenario of software development and operations (DevOps) environment, there is a need of multi-skilled engineers with the right breadth and depth across a wide range of technologies they work upon. Test driven development (TDD), DevOps culture, infrastructure as code (IaC), build / deployment automation & cloud technologies have become a vital skill set for developers apart from their core area of technology expertise. This is also commonly referred as T-shaped skill / Ninja professional / full stack engineer.
[003] A number of organizations these days keep track of learning activities and skills of their employees not only related to the DevOps but also in general. The learning activity can be of any nature such as trainings, seminar, questionnaire etc. The lack of tracking of learning activity of employees in the organization may cause several issues, such as inability to find a staff the right person on the right project, inability to advice associates on what learning to complete in order to achieve such a profile for any data distribution that has outliers.
[004] In view of this, organizations increasingly employ various applications / software to assist in the tracking of such information. However, there is no clear method in the industry, for measuring and quantifying the multi-faceted nature of this skill. Industry uses basic linear, siloed methods such as levels of proficiency for each individual skill. There is also a lack of a good system / application which can recommend courses or other related activities to the learners so that they can enhance their skill set and their usability in the organization. In addition to this, a quantified metric scale is also required to indicate completeness of the learners profile providing a right view of breadth and depth across their skills.

SUMMARY
[005] The following presents a simplified summary of some embodiments of the disclosure in order to provide a basic understanding of the embodiments. This summary is not an extensive overview of the embodiments. It is not intended to identify key/critical elements of the embodiments or to delineate the scope of the embodiments. Its sole purpose is to present some embodiments in a simplified form as a prelude to the more detailed description that is presented below.
[006] In view of the foregoing, an embodiment herein provides a system for measuring and quantifying skillset of a plurality of learners. The system comprises a user interface, a memory, processor in communication with the memory and a display device. The processor further comprises a credit framework, a credit decay engine, a distributed weight calculation module, a T-factor calculation module and a bot recommender. The credit framework computes a credit score indicating the skills of the plurality of learners, wherein the credit score is indicative of the learnings completed across a plurality of specializations. The credit decay engine updates the credit score of the each of the plurality of learners based on a new learning of the respective learners over a period of time using a decay rate function. The distributed weight calculation module derives a distributed weight for the plurality of learners based on the types of learnings completed by the respective learners across the breadth and depth of skills. The T-factor calculation module calculates a T-factor of the plurality of learners using the updated credit score and the distributed weight across the plurality of specializations. The bot recommender recommends a set of recommendations to the plurality of learners based on the calculated T-factor in order to improve the T-factor of the plurality of learners. The display device displays the calculated T-factor in graphical form.
[007] In another aspect the embodiment provides a method for measuring and quantifying skills of a plurality of learners. Initially, a credit score indicating the skills of the plurality of learners is computed, wherein the credit score is indicative of the learnings completed across a plurality of specializations. In the next step, the credit score of the each of the plurality of learners is updated based on a new learning of the respective learner over a period of time using a decay rate function. In the next step, a distributed weight for the plurality of learners is derived based on the types of learnings completed by the respective learners across the breadth and depth of skills. In the next step, a T-factor of each of the plurality of learners is calculated using the updated credit score and the distributed weight across the plurality of specializations. And finally a set of recommendations are recommended to the plurality of learners based on the calculated T-factor in order to improve the T-factor of the plurality of learners.
[008] It should be appreciated by those skilled in the art that any block diagram herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computing device or processor, whether or not such computing device or processor is explicitly shown.

BRIEF DESCRIPTION OF THE DRAWINGS
[009] The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:
[0010] Fig. 1 illustrates a block diagram for measuring and quantifying skills of a plurality of learners according to an embodiment of the present disclosure;
[0011] Fig. 2A-2B shows a flowchart illustrating the steps involved in measuring and quantifying skills of the plurality of learners according to an embodiment of the present disclosure; and
[0012] Fig. 3 shows a schematic diagram of an example for measuring and quantifying skills of the plurality of learners in an organization according to an embodiment of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS
[0013] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. 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.
GLOSSARY – TERMS USED IN THE EMBODIMENTS
[0014] The expression “DevOps” sensor in the context of the present disclosure refers to software engineering practice that aims at unifying software development (Dev) and software operation (Ops).
[0015] The expression “learners” or “plurality of learners” or “employees” or “person” in the context of the present disclosure refers to any person who is going to be involved in any kind of learning activity. The learning activity could within the organization or outside the organization in any form.
[0016] Referring now to the drawings, and more particularly to Fig. 1 through Fig. 3, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.
[0017] According to an embodiment of the disclosure, a system 100 for measuring and quantifying the skills of a plurality of learners is shown in Fig. 1. The system 100 can be used in an organization, a corporate firm, any university or any other place. The plurality of learners include any person who is going to be involved in some kind of learning activity. The plurality of learners could be employees of an organization, students of a university or any other person. The system 100 is configured to generate a competency score that evaluates the profile of the plurality of learners across depth and breadth and quantifies it.
[0018] According to an embodiment of the disclosure, the system 100 consists of a user interface 102, a memory 104 and a processor 106 as shown in the block diagram Fig. 1. The processor 106 is in communication with the memory 104. The processor 106 is configured to execute a plurality of algorithms stored in the memory 104. According to an embodiment of the disclosure, the processor 106 further includes a plurality of modules for performing various functions. The processor 106 may include a credit framework 108 or credit score generation module 108, a credit decay engine 110, a distributed weight calculation module 112, a T-factor calculation module 114 and a bot recommender 116. The system 100 also includes a display device 118.
[0019] According to an embodiment of the disclosure, the user interface 102 is configured to receive input from a user. The user can provide various information to the system 100 such as the trainings or learnings which have been undertaken by the plurality of learners etc. The type of trainings / learnings involve learnings undertaken within an organization or the learnings undertaken outside the organization. The user interface 102 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite.
[0020] According to an embodiment of the disclosure, the system 100 includes a credit framework 108 or credit score calculation module 108 to compute the credit score indicating the skills of the plurality of learners. The credit score is indicative of the learnings achieved across a plurality of specializations. The computation of the credit score also takes into balancing the skew-ness in both depth and breadth thereby providing equal opportunity to both someone who’s is cross-skilling across in non-adjacent skills (like Front-end, Backend) vs. someone who is up-skilling himself in the same skills (multiple technologies in Front-end).
[0021] According to an embodiment of the disclosure, the system 100 further includes the credit decay engine 110. The credit decay engine 110 regularly updates the credit score of the plurality of learners based on the new learning of the person over a period of time using a decay rate function. In the current scenario, learning can’t be discrete one-off activity and has to be continuous. In order to keep this as a function that demonstrates learner’s skill profile, it was intended to decay the knowledge measure of credit score as an infinite decay thereby maintaining system of records of acquisition of skills. In an example of the disclosure, the decay rate function is based on exponential decay function, thereby deriving skill relevance. Though it should be appreciated that the use of any other similar decay function is well within the scope of this disclosure.
[0022] According to an embodiment of the disclosure, the system 100 includes the distributed weight calculation module 112. The distributed weight calculation module 112 derives a distributed weight for the plurality of learners based on the types of learnings completed by the respective learners across the breadth and depth of skills. The plurality of learners undertake a different types of learning over a period of time. The breadth defines the different types of learning undertaken by the learners, whereas the depth defines the difficulty level or detail in which the trainings have been covered. The distributed weight calculation module 112 uses alternating distributed weights to measure the knowledge (credits) of the plurality of learners across various specialization pertaining to the skills.
[0023] According to an embodiment of the disclosure, the system 100 includes the T-factor calculation module 114. The T-factor calculation module 114 calculates a T-factor of the plurality of learners using the updated credit score and the distributed weight across the plurality of specializations. The T-factor is the measure of the skills of the plurality of learners. The T-factor involves balancing the skew-ness in depth and breadth of the skills. The T-factor also quantifies the skills of the plurality of learners. Each of the plurality of learners will have their own T-factor. The calculated T-factor can be used in multiple ways. In an example, the T-factor can be used to determine the usefulness of the plurality of learners in the organization. In an example of the present disclosure, the T-factor is in the range of zero to three. Though it should be appreciated that the use of any other range of the T-factor is well within the scope of this disclosure.
[0024] The alignment of skills that enterprise or organization needs or goals with skills that learners learn is absolute necessity for enterprise success and hence the T-factor is a key consideration to the system 100. As a part of the computation of T-Factor, the weight distribution varies for employee who have enough skills on categories that the enterprises has acute demand vs. enough skills on categories that the enterprise doesn’t have acute demand for. The system 100 also lays the foundation for a much superior, intelligence-driven identification of right talent for the right job in the organization, leading to better fulfilment rates, reduced opportunity loss and hence better revenues.
[0025] According to an embodiment of the disclosure, the system 100 also includes the bot recommender 116. The bot recommender 116 is configured to recommend a set of recommendations to the plurality of learners based on the calculated T-factor in order to improve the T-factor of the plurality of learners. The set of recommendations include at least one of a learning activity to undertake, setting up mentoring sessions, participate in contests such as Hackathon, answering technical queries, contributing technical content for learning or conducting training sessions for peers and customers.
[0026] According to an embodiment of the disclosure, the T-factor can further be displayed on the display device 118. In an example, the display device 118 can display the T-factor of respective learners in the form of alphabet “T”. In the alphabet “T”, the horizontal line represents the breadth of the skills of the learner, whereas the vertical line represents the depth of the skills of the learner. The visualization is tightly coupled to the score obtained through breadth based skill or depth based skill.
[0027] According to an embodiment of the disclosure, the calculated T-factor of the learner can also be compared with a predefined T-factor. The predefined T-factor can be decided by the organization which can be used as a threshold for qualifying the learner for a certain activity or task within the organization. For example, if the learners have the T-factor of more than or equal to two then he/she can be involved in certain project.
[0028] In operation, a flowchart 200 illustrating the steps involved for measuring and quantification of skills of a plurality of learners is shown in Fig. 2A-2B. Initially at step 202, a credit score is computed indicating the skills of the plurality of learners. The credit score is indicative of the learnings completed across a plurality of specializations. The types of learning can be either within the organization or outside the organization. In the next step 204, the credit score of the each of the plurality of learners is updated based on a new learning of the respective learner over a period of time using a decay rate function. In an example, the decay rate function is based on exponential decay function, thereby deriving skill relevance. Though it should be appreciated that the use of any other similar decay function is well within the scope of this disclosure. In the next step 206, the distributed weight for the plurality of learners is derived based on the types of learnings completed by the respective learners across the breadth and depth of skills. In the next step 208, the T-factor of each of the plurality of learners is calculated using the updated credit score and the distributed weight across the plurality of specializations. The calculated T-factor is nothing but a measure of the skills of the learner. The T-factor can further be used for other applications. And finally at step 210, a set of recommendations are recommended to the plurality of learners based on the calculated T-factor in order to improve the T-factor of the plurality of learners. The set of recommendations could be at least one of a learning activity to undertake, setting up mentoring sessions, participate in contests, answering technical queries, contributing technical content for learning or conducting training sessions for peers and customers.
[0029] The system 100 can also be explained with the help of an example as shown in Fig. 3. The figure shows the schematic representation of the system 100 for measuring and quantifying the skills of the person in the organization according to an embodiment of the disclosure. The figure shows with three learners. Learner 1 reads a lot of learning content, completes various assignments related to his skills and completes hands on related to his skills. These three activities result in earning credit score for the learner 1. Similarly, learner 2 reads technical news related to his skills, answer technical queries and participates in the technical contests such as Hackathon. This results in stopping the decay of his credit score.
[0030] Further as shown in Fig. 3, based on the activities of learner 1 and learner 2, the distributed weight is derived, credit progression is applied followed by computing the category skill. And finally the overall skill level of the learner is calculated in terms of T-factor. The calculated T-factor is then can be used for two purpose. First, based on the T-factor the bot recommender 116 can recommend the set of recommendations to the learner to improve his T-factor. Second, the talent head can discover the right talent within the organization to perform certain tasks.
[0031] The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.
[0032] The embodiments of present disclosure herein addresses unresolved problem of meaningful quantification of skills possessed by the learner. The embodiment, thus provides a method and system for measuring and quantifying the skills of the plurality of learners especially in the organization.
[0033] It is, however to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g. hardware means like e.g. an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. Thus, the means can include both hardware means and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g. using a plurality of CPUs.
[0034] The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various modules described herein may be implemented in other modules or combinations of other modules. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
[0035] The medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. Examples of a computer-readable medium include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk. Current examples of optical disks include compact disk-read only memory (CD-ROM), compact disk-read/write (CD-R/W) and DVD.
[0036] A data processing system suitable for storing and/or executing program code will include at least one processor coupled directly or indirectly to memory elements through a system bus. The memory elements can include local memory employed during actual execution of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution.
[0037] Input/output (I/O) devices (including but not limited to keyboards, displays, pointing devices, etc.) can be coupled to the system either directly or through intervening I/O controllers. Network adapters may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modem and Ethernet cards are just a few of the currently available types of network adapters.
[0038] A representative hardware environment for practicing the embodiments may include a hardware configuration of an information handling/computer system in accordance with the embodiments herein. The system herein comprises at least one processor or central processing unit (CPU). The CPUs are interconnected via system bus to various devices such as a random access memory (RAM), read-only memory (ROM), and an input/output (I/O) adapter. The I/O adapter can connect to peripheral devices, such as disk units and tape drives, or other program storage devices that are readable by the system. The system can read the inventive instructions on the program storage devices and follow these instructions to execute the methodology of the embodiments herein.
[0039] The system further includes a user interface adapter that connects a keyboard, mouse, speaker, microphone, and/or other user interface devices such as a touch screen device (not shown) to the bus to gather user input. Additionally, a communication adapter connects the bus to a data processing network, and a display adapter connects the bus to a display device which may be embodied as an output device such as a monitor, printer, or transmitter, for example.
[0040] The preceding description has been presented with reference to various embodiments. Persons having ordinary skill in the art and technology to which this application pertains will appreciate that alterations and changes in the described structures and methods of operation can be practiced without meaningfully departing from the principle, spirit and scope.