Patent Application
20210056651
The present disclosure generally relates to managing workers in a professional environment. More specifically, the present disclosure generally relates to systems, methods, and computer readable medium for training, developing, and managing the occupational skills held by various workers across a large workforce. Even more specifically, the present disclosure relates to highly automated, artificial intelligence driven ways of improving human capital through the data-driven acquisition, development, and verification of multiple career related proficiencies.
In the modern workplace, increasingly rapid technological advances may often present challenges for workers who desire to maintain cutting-edge professional skills. Workers may often have to adapt existing skills in new ways, and develop new skills, just to keep pace with job requirements and customer expectations.
This may present challenges for both the workers, and also for management. In many organizations today, workers are often responsible for their own career development and skill acquisition. This burden often comes on top of the substance of the worker's already existing workload. Conversely, management also faces difficulty in deploying its workforce in the most effective manner. Often, organizations may require many levels of management in order to best match workers with certain skills to appropriate projects or customers. This may present cost and logistical challenges, especially to large professional organizations.
In this way, there exists a technical problem of how to best track, develop, and promote acquisition of human capital professional skills among a large and diverse workforce. With a more data-rich environment, and with advances in machine learning, organizations could better manage and improve their stock of human capital.
Accordingly, there is a need in the art for a system and method that addresses the shortcomings discussed above.
The disclosure provides highly automated, artificial intelligence driven ways of improving human capital through the data-driven acquisition, development, and verification of multiple career related proficiencies. This disclosure addresses the problem of how to thoroughly manage the many career skills held by various people within a professional organization and encourage those people to continue to develop additional skills, by abstracting trends within the organization through the use of skill recommendation models.
Specifically, for example, features related to comparing the user profile with the multiple additional user profiles in the skillset database to determine one or more recommended skills for the worker to add to their user profile solve a technical problem of how to provide feedback and guidance to workers in a large professional organization based on professional skill trends within the organization. Also, for example, features related to providing a proficiency test output descriptive of a proficiency test corresponding to the professional skill inputted solve a technical problem of how to verify the human capital professional skills held by each worker within a large organization, so that verification may be done at a large scale in a consistent manner. By using these features, and all of the features disclosed herein, an organization may ensure that its people maintain relevant and cutting-edge skillsets so as to best adapt to the needs of an ever changing marketplace.
In one aspect, this disclosure provides an artificial intelligence driven system for training and managing workers in a professional organization, the system comprising at least one computing device, the computing device including a processor; and wherein the computing device is configured to perform the steps of: (1) receiving a skill input descriptive of a professional skill held by at least one worker; (2) associating the skill input with a user profile for the at least one worker; (3) storing the skill input as associated with the user profile in a skillset database, the skillset database including multiple additional user profiles with respective associated skill inputs; (4) providing a proficiency test output descriptive of a proficiency test corresponding to the professional skill inputted; (5) receiving a result of the proficiency test and associating the result of the proficiency test with the user profile; (6) comparing the user profile with the multiple additional user profiles in the skillset database to determine one or more recommended skills for the worker to add to their user profile; and (7) sending an output to the worker of the one or more recommend skills.
In another aspect, the disclosure provides a method of using artificial intelligence for training and managing workers in a professional organization, the method comprising: (1) receiving from a worker a skill input, descriptive of a professional skill held by the worker; (2) receiving from the worker a skill descriptor input from the worker for each skill input, descriptive of whether the skill inputted is a primary skill or a secondary skill; (3) generating a user profile for the worker, and associating the skill input and skill descriptor with the user profile; (4) storing the user profile in a skillset database, the skillset database further including multiple secondary user profiles as their associated skill inputs and skill descriptors; (5) sending a proficiency test output to the worker, descriptive of a proficiency test corresponding to the professional skill inputted; (6) receiving a result of the proficiency test and associating the result of the proficiency test with the user profile; (7) generating and sending a learning output to the worker, the learning output being descriptive of one or more training opportunities corresponding to the professional skill; (8) generating a recommended skills output by comparing the user profile with the secondary user profiles in the skillset database that have the same primary skill as the user profile; and (9)sending the recommended skills output to the worker.
Finally, in another aspect, this disclosure provides One or more non-transitory computer readable storage media encoded with instructions that, when executed by a processor of a computing device, causes the processor to: (1) receive a skill input, descriptive of a professional skill held by at least one worker; (2) receive a skill descriptor input from the worker for each skill input, descriptive of whether the skill inputted is a primary skill or a secondary skill; (3) generate a user profile for the worker, and associate the skill input and skill descriptor with the user profile; (4) store the user profile in a skillset database, the skillset database further including multiple secondary user profiles as their associated skill inputs and skill descriptors; (5) generate a recommended skills output by comparing the user profile with the secondary user profiles in the skillset database that have the same primary skill as the user profile, the recommended skills output being descriptive of one or more skills not already associated with the user profile; and (6) send the recommended skills output to the worker.
Other systems, methods, features, and advantages of the disclosure will be, or will become, apparent to one of ordinary skill in the art upon examination of the following figures and detailed description. It is intended that all such additional systems, methods, features, and advantages be included within this description and this summary, be within the scope of the disclosure, and be protected by the following claims.
While various embodiments are described, the description is intended to be exemplary, rather than limiting and it will be apparent to those of ordinary skill in the art that many more embodiments and implementations are possible that are within the scope of the embodiments. Although many possible combinations of features are shown in the accompanying figures and discussed in this detailed description, many other combinations of the disclosed features are possible. Any feature or element of any embodiment may be used in combination with or substituted for any other feature or element in any other embodiment unless specifically restricted.
This disclosure includes and contemplates combinations with features and elements known to the average artisan in the art. The embodiments, features and elements that have been disclosed may also be combined with any conventional features or elements to form a distinct invention as defined by the claims. Any feature or element of any embodiment may also be combined with features or elements from other inventions to form another distinct invention as defined by the claims. Therefore, it will be understood that any of the features shown and/or discussed in the present disclosure may be implemented singularly or in any suitable combination. Accordingly, the embodiments are not to be restricted except in light of the attached claims and their equivalents. Also, various modifications and changes may be made within the scope of the attached claims.
The invention can be better understood with reference to the following drawings and description. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention. Moreover, in the figures, like reference numerals designate corresponding parts throughout the different views.
Generally, this disclosure uses data-driven artificial intelligence to provide human capital skills development. By so doing, this disclosure allows workers and management to effectively maintain, verify, and develop professional skills in the context of a large professional organization. Further details of the several aspects of this disclosure are discussed variously below.
First,
Skill proficiency testing component 104 includes information descriptive of one or more various types of skill proficiency testing. Namely, skill proficiency testing component 104 includes assessment tools for verifying that a worker has a particular professional skill. The proficiency testing may: come in various formats, be completed by the worker directly as a part of system 100 or offline from it, and draw from automated test building components, as described variously below with respect to
Skill guidance component 106 includes an artificial intelligence based skill guidance recommendation engine. Skill guidance component 106 generates recommendations of one or more professional skills that a worker may acquire. The skills guidance system within skill guidance component 106 is described in further detail below with respect to
The three components 102, 104, 106 are integrated into one single electronic platform by computing device 108. Broadly, computing device 108 may include one or more processors 110 and also a skillset database 112 that stores information about the workers and their associated professional skills. More detail about skillset database 112 is discussed below. Computing device 108 is generally configured to perform the various computing process steps as described herein.
In some embodiments, the computing device generates an interface that allows a user to interact with the system. For example, computing device 108 generates interface 114 that allows user 120 to interact with system 100. Interface 114 may be a web-based interface, such as an interactive web page. In some such embodiments, interface 114 may include login 116 that allows worker 120 to create a unique user profile with which to access system 100. In some embodiments, interface 114 may be a type of communication tool between management and workers—in that system 100 allows management to input certain parameters regarding certain types of skills (such as emerging skills, discussed below), and then workers may receive useful information in the form of outputs based on those parameters.
Of the three components 102, 104, 106, the first discussed herein is the skill guidance component 106. Generally, skill guidance component 106 uses artificial intelligence based analysis to find similarities in professional skills among a large dataset of workers in a professional organization, and uses those similarities to recommend which skills may be helpful to worker 120.
Next, in step 206, process 200 includes a step of receiving a skill descriptor input 208. Skill descriptor input 208 may generally be any type of information that modifies, explains, or describes skill input 204. For example, skill descriptor input 208 may label skill input 204 as a “primary skill” or a “secondary skill” in order to indicate whether the skill in question is central to worker 120's job and career or peripheral to it. Other skill descriptors 208 may include whether the skill is currently at a proficiency level of beginner, intermediate, or advanced. A wide range of types of skill descriptors 208 may generally be used in process 200, in order to give each skill 204 held by worker 120 any useful or appropriate contextual information.
System 100 executing process 200 may then generate a user profile for worker 120 at step 210, if one does not already exist for worker 120. User profile 214 may be a digital representation of the information associated with the worker's skills and career. Namely, step 210 includes associating skill input 204 and skill descriptor 208 with user profile 214. Of course, if a user profile 214 already exists for worker 120 then skill input 204 and skill descriptor 208 are associated with the already existing user profile 214 at step 210. Process 200 next stores user profile 214 and its related data in a skillset database 216 at step 212. Process 200 may repeat steps 202 and 206 in embodiments where worker 120 may wish to input multiple skills or skill descriptors.
Skillset database 216 acts as a central repository for multiple user profiles—it includes user profile 214 corresponding to worker 120, as well as a number of multiple other additional user profiles. Namely, a large number of workers in a large professional organization may interface with system 100 and go through process 200 to each create a user profile that includes associated skill inputs 204 and skill descriptors 208. Skillset database 216 may therefore include an extremely large data set, that may be analyzed computationally to reveal patterns, trends, and associations among the workers and their professional skills.
Specifically, in step 218 of process 200, system 100 may compare user profile 214 with the multiple additional user profiles in skillset database 216 to determine one or more recommended skills for the worker to acquire through training and add to their user profile. Step 218 of generating a recommended skills output is described in further detail below with respect to
Finally, in a last step of process 200, system 100 sends the recommended skills output generated in step 218 to user 120 or other recipient at step 220. In this way, process 200 may provide to user 120 a recommendation for one or more new professional skills that user 120 may develop in order to further their career.
Of note, the above steps in process 200 are discussed above with respect to a certain ordered sequence of the steps (202, 206, 210, 212, 218, 220)—as is also shown in
As discussed above, whether a skill is a “primary” skill or not may be described by skill descriptor 208. Broadly, a primary skill held by a worker 120 may be the single most important professional skill to the worker 120's career. In some embodiments, therefore, each user profile in skillset database 216 may include at most one skill that is described as primary for the user profile. User profiles may then also include one or more secondary skills, that are peripheral to the primary skill in the worker's career and skills.
Process 300 as shown in
In some embodiments of step 302 in process 300, the neighbor value may be based on user profiles that have only the same primary skill. For example, user profile 214 may have “coding” associated with it as the primary skill. Step 302 would then find all other user profiles in skillset database 216 that also have “coding” as the primary skill. The total number of these user profiles would then be the neighbor value. However, in other embodiments, a different similarity model may be used other than only the primary skill.
For example, in a second model, the number of neighbors may be calculated based on the number of secondary user profiles in the skillset database that have the same primary skill as user profile 214 and also have at least one secondary skill in common. This model would generally return fewer neighbors than the first model, based only the primary skill being held in common. This may be useful when the skillset database includes such a large set of data that using a tighter criteria for neighbor similarity might return results with higher relevancy to worker 120.
In yet another model for calculating the neighbor value in step 302, process 300 may determine the number of neighbors by comparing user profile 214 with all the secondary user profiles in skillset database 216 based on a similarity of a maximum number of skills held in common. This model may involve ranking all secondary user profiles according to a degree of similarity based on a maximum number of skills (primary or secondary) held in common, then applying a cutoff similarity value of (in some embodiments) 0.5. A cutoff value of 0.5 may be equivalent to having at least half of all skills in similar, as between the subject user profile 214 and the secondary user profile being compared. In other embodiments, a cutoff value of 0.4 may be used, or a cutoff value of 0.6, or 0.7.
All secondary user profiles that have a similarity to user profile 214 above the cutoff may then be considered neighbors. More details about several models on which the neighbor value may be based are discussed below with respect to
Next, in step 304, process 300 may include calculating a neighbor recommending value for each skill held by one or more secondary user profiles that are neighbors to the subject user profile 214 (and are not already associated with user profile 214). The neighbor recommending value may be based on the total quantity of neighbors that have the skill at issue. For example, user profile 214 may have a neighbor value per step 302 of 100—meaning that 100 secondary user profiles have (in one model) the same primary skill. Of these 100 neighbors, 60 of them may all have one new skill associated with them. The neighbor recommending value for the new skill would then be 60.
In step 306, process 300 may include calculating a recommendation score for each skill held by one or more of the neighbors. The recommendation score may be the ratio of the neighbor recommending value for each skill to the total neighbor value. In the example above, 60 neighbors have one skill in common out of 100 total neighbors—so the recommendation score for this skill is 0.6. Process 330 may include calculating whether the recommendation score exceeds a minimum threshold at step 308, in order to determine whether the skill at issue is relevant enough to output to the worker. In various embodiments, the minimum threshold for the recommendation score may be 0.3, or 0.4, or 0.5, or another value as may be determined to best narrow the results.
If the recommendation score exceeds the predetermined threshold, process 300 may proceed to step 310 of generating and sending a recommended skills output. The recommended skills output of step 310 may be sent to worker 120, or to management of the professional organization, or another recipient. As with other outputs discussed herein, recommended skills output of step 310 may be communicated to worker 120 via interface 114. Further discussion of this point is below with respect to
The above several calculations, regressions, and analysis steps in process 200 and process 300 may be done by computing device 108—and may be referred to as a type of machine learning or artificial intelligence data processing. Most broadly, computing device 108 includes an artificial intelligence based system 100 that evaluates the large set of data in skillset database 112/216 to find trends, similarities, distinctions, and optimizations among the various professional skills held by workers in a large professional organization.
Namely, a certain subset of all skills in skillset database 216 may be flagged as “emerging” skills. Emerging skills may be those skills that are the most cutting-edge professional skills within a professional organization—as determined by management of the professional organization, or by another deciding body. A skill may be flagged as emerging for a variety of reasons, such as: relation to a new project or customer, involvement in recent research and development efforts, or selected by automatic criteria as applied by using artificial intelligence to analyze trends within the skillset database 216. Process 400 may be referred to as the “emerging skills” model for determining the recommended skills output.
Namely, process 400 begins at step 402 of calculating the neighbor value for a subject user profile 214 by drawing data from the skillset database 216. Step 402 may be similar to step 302 in process 300. Second, step 404 may include calculating a neighbor recommending value for each skill held by one or more of the neighbors that is not already held by the subject user profile 214. Step 404 may be similar to step 304 in process 300. Next, at step 406 process 400 again calculates a recommendation score for each skill held by one or more neighbors. Step 406 may be similar to step 306 in process 300. Step 408 again applies a minimum score threshold to each skill—as was done in step 308 of process 300.
However, process 400 differs from process 300 at step 412. Step 412 involves filtering for skills that are flagged as emerging. Namely, in order to be included in the skills recommendation output created at step 414, a skill must be held by enough neighbors to achieve a minimum scoring and also be associated with the “emerging” descriptor. This step 412 will therefore more narrowly identify professional skills that might be helpful for the worker 120 to acquire. This may be helpful to worker 120 by identifying the most cutting-edge professional skills, that are not yet as widespread within the professional organization—allowing the worker opportunity to acquire the emerging skills in a time sensitive manner that might best promote the worker's career and the professional organization's strategic goals.
Within the emerging skills process 400, there may be more than one model for determining what constitutes a neighbor at step 402. As discussed above, in some embodiments, the neighbors may be defined as any secondary user profiles in the skillset database 216 that have only the same primary skill in common with the subject user profile 214. Alternatively, in other embodiments, neighbors may be defined as any secondary user profiles in the skillset database 216 having both the primary skill and also one secondary skill in common with the subject user profile 214.
Aside from the “skills of similar employees” model of process 300 and the “emerging skill” model of process 400, a third general model may also be used to generate a skills recommendation. Not otherwise shown in the figures, a third model for analyzing the skillset database may be referred to as the “proximity skills” or “near skills” model. In some embodiments, this model may be based on calculating a co-occurrence value between any two given pair of skills in the skillset database 216. The co-occurrence value may be the ratio of the number of secondary user profiles in the skillset database that include both of the two skills to the number of secondary user profiles in the skillset database that includes only a first one of the two skills.
For example, the co-occurrence value for skills “A” and “B” would be the number of user profiles in the skillset database with both skills A and B divided by the number of user profiles in the skillset database with only skill A. The co-occurrence value for any pair of two skills may be calculated with respect to either of the two skills—such as: (A+B)/(A) and also (A+B)/(B). In this way, the co-occurrence value measures the strength of how often two skills are found together in user profiles in the skillset database 216.
The near skills model then proceeds to rank the skills in the skillset database based on the co-occurrence value of each skill in relationship to the skills associated with the subject user profile 214. The system 100 then generates the recommended skills output, that includes a recommended skill when (a) the number of times the skill is associated with secondary user profiles in the skillset database exceeds a first predetermined threshold, and (b) the co-occurrence between the recommended skill and the skills associated with the user profile exceeds a second predetermined threshold. The first predetermined threshold may be at least 8 instances of the skill of the skillset database 216, or at least 10, or at least 20, or other threshold minimum value as may best narrow the results appropriately. The second predetermined threshold may be at least 0.3, or at least about 0.4, or at least about 0.5, or other threshold minimum value as may best narrow the results appropriately.
In another embodiment, a “near skills” model may be calculated using a predefined set of relationships among the skills in the skillset database that is external to the skillset database itself. For example, other calculations and analytics aside from those discussed above may be used to note certain valuable relationships between certain professional skills held by workers within the organization. These externally created sets of relationships may then be used to calculate which skills are proximate to any given skills held by the user.
As a result of the several models discussed above, system 100 may generate a recommended skills output that includes one or more professional skills that may be helpful to worker 120. In some embodiments, one of the above models (skills of similar employees model, emerging skills model, or proximity skills model) may be used to generate the recommended skills output. However, in other embodiments, a combination of one or more of these models may be used.
Process 500 may next include performing one or more processes like process 400 to calculate recommended skills using the emerging skills model 502. As discussed above with respect to
Third, process 500 may include the proximity skills 506 which is generally based on a co-occurrence value 520, as discussed above.
As a result of the several above models, process 500 may include creating up to six sets of recommended skills and then combining the up to six sets. Namely, process 500 may include:
(1) creating a first set of recommended skills 514 by comparing the user profile with the secondary user profiles in the skillset database that have the same primary skill as the user profile;
(2) creating a second set of recommended skills 516 by comparing the user profile with the secondary user profiles in the skillset database that have the same primary skill and one secondary skill as the user profile;
(3) creating a third set of recommended skills 518 by comparing the user profile with the secondary user profiles in the skillset database that have the same primary skill and at least half of all skills as the user profile;
(4) creating a fourth set of recommended skills 510 by comparing the user profile with the secondary user profiles in the skillset database that have the same primary skill as the user profile, and are flagged as emerging skills;
(5) creating a fifth set of recommended skills 512 by comparing the user profile with the secondary user profiles in the skillset database that have the same primary skill and one secondary skill as the user profile, and are flagged as emerging skills; and
(6) creating a sixth set of recommended skills 520 by calculating a co-occurrence value between each skill associated with the user profile and each skill in the skillset database and ranking the skills in the skillset database based on the co-occurrence value.
Process 500 may then include a step 522 of ranking skills across the first, second, third, fourth, fifth, and sixth sets of recommended skills according to one or more criteria to generate a final list of recommended skills 524. The one or more criteria may include examples like: commonality of a result across multiple models, weighting of one model relative to another model, weighting of one neighbor calculation type (primary only vs. primary plus one secondary), absolutely priority of results from one model over another (e.g. emerging skills ranked first), and others.
Finally, a part of generating the recommended skills output in step 524, or step 414, or step 310, may include generating and sending description of the one or more models used to generate the recommended skills output. Namely, system 100 may display on interface 114 not only the substance of which skills are recommended—but also display information allowing worker 120 to understand why those skills were recommended. System 100 may therefore be considered an “explainable” artificial intelligence system, because the result is explainable to the end user.
For example,
Namely,
Of note is that user VI 612, user VII 614, user VIII 616, and user IX 618 all share skill K 623. The graphical clustering of secondary users around skill K shows that this skill is held by a large number of similar employees. Therefore, according to the first calculation model discussed above (the similar employees model of
Therefore, in this way, as discussed at length above, the skill guidance component 106 of the digital platform system 100 uses explainable artificial intelligence to provide insights into career skill trends within a professional organization as they relate to a particular user.
Next, skill proficiency testing component 104 may also be incorporated into system 100. Skill proficiency testing allows worker 120 to verify that they do, in fact, possess a given professional skill. Proficiency testing may also allow a worker to catalogue and advertise their level of understanding of a skill. This may allow a worker to better advance their career, by allow them to match themselves to certain projects or customers—or by allowing management of the professional organization to so match them.
In some embodiments, proficiency test output as generated at step 904 may generally be information that is descriptive of, or otherwise associated with, a proficiency test—but not the substance of the test itself. For example, proficiency test output may be comprised of information such as: test description, test location, test time, or test level (beginner, intermediate, advanced, etc.).
However, in other embodiments, proficiency test output may comprise a proficiency test itself. Particularly, proficiency test output may comprise a proficiency test selected from the group consisting of: automated question-and-answer format drawn from pre-written questions, certifications, case studies, hands on tests, and evaluation by a credentialed reviewer. In such embodiments, step 904 may draw from a question database 906 to automatically build the proficiency test.
Namely, in some embodiments, the proficiency test may be an automated question-and-answer format questionnaire that is built by system 100 by drawing information from question database 906. Specifically, computing device 108 may generate the proficiency test by selecting multiple questions from a question database based on question meta-data. Question meta-data may be descriptive of one or more of each question's associated skill, complexity, average time spent on the question, history of usage in past proficiency tests, and others as may be appropriate. The various processes used to build a proficiency test in this way may be another example of artificial intelligence being used in system 100 to develop workers' professional skills.
Back to
However, in other embodiments, worker 120 may receive the proficiency test output at step 904 and then take the actual proficiency test offline from system 100. In such embodiments, system 100 may then receive the proficiency test result 910 via e.g. a manual upload to computing device 108.
Process 900 next includes comparing the result of the proficiency test to a predetermined threshold at step 912. The predetermined threshold may vary according to a variety of factors and may be automatically generated by system 100 in embodiments where the proficiency test was an automated question-and-answer format questionnaire built by system 100 as described above. Alternatively, in other embodiments, the predetermined threshold for the result of the proficiency test may be manually entered by management of the professional organization.
When the result of the proficiency test exceeds the predetermined threshold, the worker is considered to have passed the test. In step 914, process 900 associates the result of the proficiency test with the workers user profile and also assigns a skill level to the skill in the user profile to signify that the worker has achieved a certain level of master of the skill. In contrast, if the result of the proficiency test is below the predetermined threshold then merely the test result is associated with the user profile at step 916.
Namely, in a first step 1002 process 1000 allows worker 120 to add a recommended skill 1004 to their user profile. Process 1000 therefore also incorporate the skills guidance recommendation engine, as discussed variously above. In this context, the recommended skills output (310, 414, 524) that is the result of the skills guidance process (300, 400, 500) in turns becomes the skill input 1004 that starts process 1000 at step 1002. In this way, the skill guidance component 106 of system 100 works integrally with the other aspects of the artificial intelligence based system for managing workers.
Process 1000 includes three steps of generating proficiency test outputs. In step 1008, process 1000 generates a 1st proficiency test output. First proficiency test output may be an automatically generated question-and-answer format proficiency test that draws from question database 906, as discussed above. If the first proficiency test result exceeds a first minimum threshold, process 1000 proceeds to generating a second proficiency test output. Meanwhile, process 1000 also assigns a first skill level to the skill in the user profile. Otherwise, if the first proficiency test result does not exceed the first minimum threshold, process 1000 terminates at step 1030 and associates the first proficiency test result with the user profile. In this way, passing a test allows the worker to verify mastery of the skill at a certain level through the skill level assigned to their user profile.
At step 1014 of generating the second proficiency test out, the second proficiency test may be a certification or case study. This proficiency test may be integrated with system 100 or offline from it. Next, at step 1018 process 1000 again compares the most recent test result (the second proficiency test result) to a second minimum threshold. Again, if the worker passed, the process 1000 proceeds to generate a next, third, proficiency test output at step 1022. The third proficiency test 1016 may be in a format of evaluation by a credential reviewer. In this way, process 1000 may include a variety of formats for the multiple proficiency tests included therein. These formats may allow a worker to best demonstrate and verify their knowledge of a skill in various contexts and under different testing conditions.
In some embodiments, the first proficiency test output may be referred to as an objective assessment because it is an automatically generated question-and-answer format test that establishes a first base level of skill. The second proficiency test may then be referred to as an expert assessment, as it involves a more complicated proficiency test in form of a certification or case study that establishes a higher level of proficiency in the skill. Finally, the third proficiency test may be referred to a master assessment because it requires the most complicated form of proficiency test, and establishes a highest level of proficiency in the skill.
As mentioned, process 1000 also includes step 1006 of generating and sending a learning output. Learning output is an example of skill training component 102, the other one of the three major components (102, 104, 106) in system 100. Generally, the learning output may be descriptive of one or more training opportunities that correspond to a professional skill. In the context of process 1000, learning output may relate to the skill that is the subject of recommended skills output/input 1004. Learning output may allow worker 120 to received knowledge and training that would prepare them for a proficiency test, such as one or more of the three proficiency tests in process 1000.
Broadly, worker 120 may advance their knowledge of their existing skill through skill training component 102 of the digital platform system 100. The skill training component 102 provides to the worker 120 a path for developing one or more existing skills associated with the workers users profile 214. This section of the digital platform may include information such as recommended training courses for a given existing skill. Descriptions of the training course may also be provided, such as a skill level that the course is addressed to (beginner, intermediate, advanced), a mode of learning (virtual, class room), and a duration of the training course. By consuming the training courses along a skill path for an existing skill, a user may progress and improve that skill.
As with the proficiency test output, learning output may in some embodiments be data that is associated with of one or more training opportunities—and in other embodiments may be the substance of one or more training courses itself. In either embodiment, the learning output may also include training meta-data that is descriptive of the one or more training opportunities. Training meta-data may be selected from the group consisting of: mode of learning, duration of the training, skill level, and combinations thereof.
Finally,
Namely, process 1200 includes first step 1202 of receiving a skill input 1204, then receiving a skill descriptor 1208 at step 1206, then associating the skill input 1204 and skill descriptor 1208 with a user profile at step 1210, and storing the user profile 1214 in skillset database 1216 at step 1212. These steps may be substantially similar to steps 202, 206, 210, 212 in process 200 respectively.
In a side option available to the user once their user profile 1214 with associated skill is stored in the skillset database 1216, the embodiment shown in
Generally, features 1226, 1228, 1230, and 1232 work together to provide the user with skill training opportunities and confirmation that the user has learned from those opportunities to develop their already existing skills. The user may generally participate in these side option learning steps at any time after a skill has been associated with their user profile 1214 in the skillset database 1216. In this way, in process 1200 the user may receive a learning output, engage in the learning opportunity to train themselves on the skill, and generate the training confirmation to confirm to process 1200 that they have completed the training.
Step 1212 may then proceed to step 1218 of generating a proficiency test output. Process 1200 may then generate a proficiency test output at step 1218, and receive back a proficiency test result 1222 at step 1220, similar to steps 904 and 908 of process 900. Thus, the proficiency testing component 104 thereby verifies that a user has successfully mastered a skill. For example, a user may develop their knowledge of a skill through an optional learning opportunity described in 1226, and then test that knowledge at 1218. Process 1200 therefore allows users to build professional skill competency by offering skill training 102 and sill proficiency testing 104 together on one platform.
Otherwise, process 1200 next also includes step 1234 of assigning a skill level to the skill at issue in the user profile if the proficiency test was passed at step 1224. Subsequently, process 1200 may generate a recommended skills output at step 1236 by drawing from the skillset database (as described variously above). In the final step 1238, process 1200 sends the recommended skills output to worker 120. However, in this embodiment, the recommended skills output generated in step 1238 may become the skill input 1204 used in step 1202. This iterative process may allow worker 120 to input their professional skills, verify their proficiency with each skill through testing, receive training opportunities to increase their knowledge of their existing skills, and add new skills to their career development.
In this way, each worker within a professional organization may organize their existing career related skills, learn more about those skills, and verify that they have those skills—all within the digital platform system 100.
While various embodiments of the invention have been described, the description is intended to be exemplary, rather than limiting and it will be apparent to those of ordinary skill in the art that many more embodiments and implementations are possible that are within the scope of the invention. Accordingly, the invention is not to be restricted except in light of the attached claims and their equivalents. Also, various modifications and changes may be made within the scope of the attached claims.
