U.S. patent application number 16/556943 was filed with the patent office on 2019-12-19 for matching a job profile to a candidate.
The applicant listed for this patent is Pearson Education, Inc.. Invention is credited to Margo Erin GORDON, Alexander W. LOWRIE, Michael John MAYOR, Lindsay Noelle OISHI, Joseph John WORACHEK.
Application Number | 20190385469 16/556943 |
Document ID | / |
Family ID | 59788066 |
Filed Date | 2019-12-19 |
United States Patent
Application |
20190385469 |
Kind Code |
A1 |
GORDON; Margo Erin ; et
al. |
December 19, 2019 |
MATCHING A JOB PROFILE TO A CANDIDATE
Abstract
Systems, device configurations, and processes for integrating a
job profile describing an occupation into a talent management
system (TMS) by determining proficiency requirements from job
profile data are used to develop validated scores for competency in
one or more job skills. The resulting integrated job profiles may
be included as a component of TMS job offerings related to the
occupation. The systems and methods evaluate parameters of the job
profile using a predetermined scoring framework. Particular
implementations evaluate English language proficiency in one or
more frameworks. A job profile describes the occupation in the form
of tasks to be completed by an employee. The systems and methods
associate the tasks with scoring parameters in the framework, and
calculate an overall score for English proficiency from the
collection of scores associated with the tasks.
Inventors: |
GORDON; Margo Erin; (Menlo
Park, CA) ; LOWRIE; Alexander W.; (Davis, CA)
; MAYOR; Michael John; (London, GB) ; OISHI;
Lindsay Noelle; (Sunnyvale, CA) ; WORACHEK; Joseph
John; (Littleton, CO) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Pearson Education, Inc. |
Bloomington |
MN |
US |
|
|
Family ID: |
59788066 |
Appl. No.: |
16/556943 |
Filed: |
August 30, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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15069539 |
Mar 14, 2016 |
10438500 |
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16556943 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G09B 5/06 20130101 |
International
Class: |
G09B 5/06 20060101
G09B005/06 |
Claims
1. A method for a talent management system (TMS) of a company to
match a candidate, in a plurality of candidates, to a job profile,
in a plurality of job profiles, comprising the steps of:
generating, by the TMS, the plurality of job profiles, wherein each
job profile corresponds to a job in a plurality of jobs and wherein
each job profile comprises a minimum Global Scale of English (GSE)
score and a plurality of tasks that an individual must be able to
complete in performing the job that corresponds to the job profile;
hosting, by the TMS, the plurality of job profiles in an electronic
network database, wherein a user interface is configured to enable
the candidate to search through the plurality of job profiles and
review the minimum GSE score associated with each job profile;
evaluating a plurality of candidates and assigning, by the TMS, a
GSE score to each candidate in the plurality of candidates; and
matching the candidate, in the plurality of candidates, to the job
profile, in the plurality of job profiles, based at least in part
on a GSE score of the candidate and the minimum GSE score for the
job profile.
2. The method of claim 1, further comprising the step of:
transmitting, by the TMS, a job, that corresponds to the job
profile that matched the candidate, to the user interface for
viewing by the candidate.
3. The method of claim 2, further comprising the step of: grouping
a first plurality of job profiles, from the plurality of job
profiles, into a first personalized path and a second plurality of
job profiles, from the plurality of job profiles, into a second
personalized path.
4. The method of claim 3, further comprising the steps of:
determining, by the TMS, a particular personalized path for the
candidate, wherein the particular personalized path comprises the
job that corresponds to the job profile that matched the candidate;
and transmitting, by the TMS, the particular personalized path to
the user interface for viewing by the candidate.
5. The method of claim 3, further comprising the steps of:
determining, by the TMS, a particular personalized path for the
candidate, based at least in part on the GSE score of the
candidate; and transmitting, by the TMS, the particular
personalized path to the user interface for viewing by the
candidate.
6. The method of claim 4, wherein the particular personalized path
comprises two or more job profiles sorted in order of ascending GSE
score.
7. The method of claim 4, wherein the particular personalized path
comprises a course of learning and assessment that can build and
measure progress in a language skill.
8. The method of claim 4, wherein the particular path is a career
path and the career path comprises three or more job profiles or
three or more jobs.
9. A method for a talent management system (TMS) of a company to
match a candidate, in a plurality of candidates, to a a job
profile, in a plurality of job profiles, comprising the steps of:
generating, by the TMS, the plurality of job profiles, wherein each
job profile corresponds to a job in a plurality of jobs and wherein
each job profile comprises a minimum Global Scale of English (GSE)
score and a plurality of tasks that an individual must be able to
complete in performing the job that corresponds to the job profile;
hosting, by the TMS, the plurality of job profiles in an electronic
network database, wherein a user interface is configured to enable
the candidate to search through the plurality of job profiles and
review the minimum GSE score associated with each job profile;
evaluating a plurality of candidates and assigning, by the TMS, a
GSE score to each candidate in the plurality of candidates; and
matching the candidate, in the plurality of candidates, to the job
profile, in the plurality of job profiles, based at least in part
on: i) a comparison of the English proficiency score of the
candidate and the minimum English proficiency score in the job
profile and ii) a comparison of the plurality of tasks that the
candidate can complete and the plurality of tasks that an
individual must be able to complete in performing the corresponding
job.
10. The method of claim 7, further comprising the step of:
transmitting, by the TMS, a job, that corresponds to the job
profile that matched the candidate, to the user interface for
viewing by the candidate.
11. The method of claim 8, further comprising the step of: grouping
a first plurality of job profiles, from the plurality of job
profiles, into a first career path and a second plurality of job
profiles, from the plurality of job profiles, into a second career
path.
12. The method of claim 9, further comprising the steps of:
determining, by the TMS, a particular career path for the
candidate, wherein the particular career path comprises the job
that corresponds to the job profile that matched the candidate; and
transmitting, by the TMS, the particular career path to the user
interface for viewing by the candidate.
13. The method of claim 9, further comprising the steps of:
determining, by the TMS, a particular career path for the
candidate, based at least in part on the English proficiency score
of the candidate; and transmitting, by the TMS, the particular
career path to the user interface for viewing by the candidate.
14. The method of claim 10, wherein the particular career path
comprises two or more job profiles sorted in order of ascending
English proficiency score.
15. A method for a talent management system (TMS) of a company to
match a candidate, in a plurality of candidates, to a a job
profile, in a plurality of job profiles, comprising the steps of:
generating, by the TMS, the plurality of job profiles, wherein each
job profile corresponds to a job in a plurality of jobs and wherein
each job profile comprises a minimum English proficiency score and
a plurality of tasks that an individual must be able to complete in
performing the job that corresponds to the job profile; hosting, by
the TMS, the plurality of job profiles in an electronic network
database, wherein a user interface is configured to enable the
candidate to search through the plurality of job profiles and
review the minimum English proficiency score associated with each
job profile; evaluating a plurality of candidates and assigning, by
the TMS, an English proficiency score to each candidate in the
plurality of candidates; and matching the candidate, in the
plurality of candidates, to the job profile, in the plurality of
job profiles, based at least in part on an English proficiency
score of the candidate and the minimum English proficiency score
for the job profile.
16. The method of claim 13, further comprising the step of:
transmitting, by the TMS, a job, that corresponds to the job
profile that matched the candidate, to the user interface for
viewing by the candidate.
17. The method of claim 14, further comprising the step of:
grouping a first plurality of job profiles, from the plurality of
job profiles, into a first career path and a second plurality of
job profiles, from the plurality of job profiles, into a second
career path.
18. The method of claim 15, further comprising the steps of:
determining, by the TMS, a particular career path for the
candidate, wherein the particular career path comprises the job
that corresponds to the job profile that matched the candidate; and
transmitting, by the TMS, the particular career path to the user
interface for viewing by the candidate.
19. The method of claim 15, further comprising the steps of:
determining, by the TMS, a particular career path for the
candidate, based at least in part on the English proficiency score
of the candidate; and transmitting, by the TMS, the particular
career path to the user interface for viewing by the candidate.
20. The method of claim 16, wherein the particular career path
comprises two or more job profiles sorted in order of ascending
English proficiency score.
Description
CROSS-REFERENCES TO RELATED APPLICATIONS
[0001] This application is a continuation-in-part of U.S.
application Ser. No. 15/069,539, filed on Mar. 14, 2016, which is
hereby incorporated by reference in its entirety for all
purposes.
BACKGROUND
[0002] A company's talent management system (TMS) is an electronic
platform that the company uses to recruit, develop, and evaluate
employees. In one aspect, the TMS maintains job descriptions for
positions within the company. Job descriptions can include
parameters such as the job title, the situation within the
corporate hierarchy, a general description of the job, and a list
of tasks that a person holding the job would be expected to
perform. Job descriptions can be largely standardized, and
companies are motivated to adopt standard or common ways of
describing positions so that the TMS can inteface with external
occupational data stores. In a particular example, the United
States Bureau of Labor and Statistics provides the O*Net database
of occupational information to companies and job-seekers alike.
Using O*Net data structures, a TMS can access thousands of job
profiles that have already been created; the company can expand its
pool of available talent to include job seekers that describe
themselves according to O*Net parameters, either directly or
through a talent placement company or similar service.
[0003] Global companies increasingly view English language skill as
a core competency for their employees. However, most recruiters,
functional managers, and HR team members do not have the expertise
or tools needed to objectively evaluate their employees' English
skills. Companies can benefit from a better understanding of the
specific English skills required to perform a particular job and
the current skill level of those who seek to do that job. It would
be advantageous for a TMS to provide a consistent and precise
method for understanding and assessing English language skills,
both for evaluating particular employees and candidates, and for
more particularly specifying job requirements in a job
description.
BRIEF DESCRIPTION OF DRAWINGS
[0004] The detailed description is set forth with reference to the
accompanying drawings. The use of the same reference numbers in
different figures indicates similar or identical items or features.
Various embodiments in accordance with the present disclosure will
be described with reference to the drawings, in which:
[0005] FIG. 1 is a diagram of an example system configured to
provide integrated job profiles to a talent management system, in
accordance with the present disclosure;
[0006] FIG. 2 is a flowchart of an example method of integrating a
job profile using user input;
[0007] FIG. 3 is a flowchart of another example method of
integrating a job profile using user input;
[0008] FIG. 4 is a flowchart of an example method of integrating a
job profile using machine learning algorithms;
[0009] FIG. 5 is a flowchart of an example method of integrating a
job profile using keywords;
[0010] FIG. 6 is a flowchart of an example method of integrating a
job profile using employee data;
[0011] FIG. 7 is a diagram illustrating a comparison of the Global
Scale of English (GSE) scores for a number of potential candidates
to a GSE score associated with an integrated job profile;
[0012] FIG. 8 is a screenshot of a user interface enabling a user
to search through a number of job profiles and review GSE scores
associated with those job profiles;
[0013] FIG. 9 is a screenshot depicting details of a job profile in
which higher-level GSE scores and learning objectives are
shown;
[0014] FIG. 10 is a screenshot depicting details of a job profile
in which lower-level GSE scores and learning objectives are shown;
and
[0015] FIG. 11 is a screenshot depicting a potential career path
and GSE learning objectives associated with steps in the career
path.
DETAILED DESCRIPTION
[0016] The present disclosure provides systems, device
configurations, and processes for integrating a job profile
describing an occupation into a talent management system (TMS) by
determining proficiency requirements from job profile data and
developing validated scores for competency in one or more job
skills, to be included as a component of TMS job offerings related
to the occupation. The systems and methods may evaluate parameters
of the job profile using a predetermined scoring framework. The
exemplary embodiments are described with respect to evaluation of
English language proficiency, but it will be understood that the
systems and methods may be used to evaluate other job skills using
a relevant scoring framework, as desired.
[0017] In one embodiment, the disclosure provides a system that
includes: a first data store comprising a plurality of learning
objectives belonging to a framework for measuring English
proficiency, each of the learning objectives being associated with
a corresponding score; a second data store containing data used by
a talent management system (TMS) of a company; and a server
communicatively coupled to and configured to access the first data
store and the second data store. The server includes device logic
and a processor that executes the device logic to integrate job
profiles into the second data store via the following actions:
receive job profile data representing a job profile describing an
occupation, the job profile data including a plurality of tasks
that an individual holding the occupation must be able to complete;
generate an integrated job profile data structure representing the
job profile integrated with the framework; identify one or more of
the plurality of learning objectives in the first data store as
being required to complete one or more of the plurality of tasks;
associate, in the integrated job profile data structure, the one or
more learning objectives with each of the plurality of tasks that
requires the corresponding learning objective; calculate an overall
score from at least the associated scores of the one or more
learning objectives; store the overall score in the integrated job
profile data structure; and store the integrated job profile data
structure in the second data store.
[0018] The processor may further execute the device logic to
connect to a user device over a computer network, send to the user
device a user interface including a first task of the plurality of
tasks and a first learning objective of the plurality of learning
objectives, the user device displaying the user interface to a
user, receive from the user device a user input comprising a
selection of the first learning objective, and associate the first
learning objective with the first task. The processor may further
execute the device logic to: receive training data representing a
plurality of pre-integrated job profiles, the training data
comprising a plurality of integrated tasks each associated with one
or more of the plurality of learning objectives; determine that a
first task of the plurality of tasks is similar to one or more
similar tasks of the plurality of integrated tasks; determine that
one or more of the one or more similar tasks is associated with a
first learning objective of the plurality of learning objectives;
and associate the first learning objective with the first task.
[0019] The processor may further execute the device logic to
analyze the job profile data to obtain a plurality of keywords,
identify at least one first keyword of the plurality of keywords as
being obtained from a first task of the plurality of tasks,
determine that a first learning objective of the plurality of
learning objectives is associated with the at least one first
keyword, and associate the first learning objective with the first
task. The processor may further execute the device logic to
identify the occupation from the job profile data, obtain one or
more employee records each associated with an employee that held
the occupation, determine an English proficiency score of each
employee from the corresponding employee record of the one or more
employee records, and calculate the overall score from the English
proficiency score of each employee associated with one of the one
or more employee records.
[0020] In another embodiment, the disclosure provides a computing
device in electronic communication with a first data store. The
computing device has device logic and a processor that executes the
device logic to: receive job profile data representing a job
profile describing an occupation, the job profile data including a
plurality of tasks that an individual holding the occupation must
be able to complete; generate an integrated job profile data
structure representing the job profile integrated with a framework
for measuring English proficiency, the framework having a scale of
scores; determine that a first plurality of the scores is
associated with the occupation; calculate an overall score from at
least the first plurality of scores; store the overall score in the
integrated job profile data structure; and store the integrated job
profile data structure in the first data store.
[0021] The computing device may be communicatively coupled to and
configured to access a plurality of data stores including the first
data store, at least one of the plurality of data stores storing a
plurality of learning objectives belonging to the framework, each
of the learning objectives being associated with a corresponding
score in the scale. To determine that the first plurality of scores
is associated with the occupation, the processor may execute the
device logic to identify a first learning objective of the
plurality of learning objectives as being required to complete a
first task of the plurality of tasks, and associate, in the
integrated job profile data structure, the first learning objective
with each of the plurality of tasks that requires the corresponding
learning objective.
[0022] Alternatively, to determine that the first plurality of
scores is associated with the occupation, the processor may further
execute the device logic to: connect to a user device over a
computer network; send, to the user device, a user interface
including a first task of the plurality of tasks and a plurality of
learning objectives belonging to the framework, the user device
displaying the user interface to a user; receive from the user
device a user input comprising a selection of the plurality of
learning objectives; and determine that the first plurality of
scores is associated with the plurality of learning objectives. Or,
to determine that the first plurality of scores is associated with
the occupation, the processor further executes the device logic to:
receive training data representing a plurality of pre-integrated
job profiles, the training data comprising a plurality of
integrated tasks each associated with a corresponding plurality of
learning objectives belonging to the framework; determine that a
first task of the plurality of tasks is similar to a second task of
the plurality of integrated tasks; determine that the second task
is associated with a first plurality of the learning objectives;
and determine that the first plurality of scores is associated with
the first plurality of learning objectives. Or, to determine that
the first plurality of scores is associated with the occupation,
the processor may further execute the device logic to: analyze the
job profile data to obtain a plurality of keywords; identify at
least one first keyword of the plurality of keywords as being
obtained from a first task of the plurality of tasks; determine
that a plurality of learning objectives belonging to the framework
is associated with the at least one first keyword; and determine
that the first plurality of scores is associated with the plurality
of learning objectives.
[0023] Alternatively, to determine that the first plurality of
scores is associated with the occupation, the processor further
executes the device logic to: identify the occupation from the job
profile data; obtain one or more employee records each associated
with an employee that held the occupation; determine an English
proficiency score of each employee from the corresponding employee
record of the one or more employee records; and determine that the
first plurality of scores includes the determined English
proficiency scores.
[0024] In another embodiment, the disclosure provides a
computer-implemented method for integrating a job profile
describing an occupation into an electronic talent management
system (TMS). The method includes: receiving, from an electronic
data store, job profile data representing the job profile, the job
profile data including a plurality of tasks that an individual
holding the occupation must be able to complete; generating an
integrated job profile data structure representing a first
integrated job profile of a plurality of integrated job profiles,
wherein the first integrated job profile is the job profile
integrated with a framework for measuring English proficiency;
determining that a first task of the plurality of tasks requires
the individual to fulfill a first learning objective of a plurality
of learning objectives each having an associated score that
measures English proficiency; associating the first task with the
first learning objective in the integrated job profile data
structure; determining that a second task of the plurality of tasks
requires the individual to fulfill a second learning objective of
the plurality of learning objectives; associating the second task
with the second learning objective in the integrated job profile
data structure; calculating an overall score from at least the
associated scores of the first and second learning objectives;
storing the overall score in the integrated job profile data
structure; and storing the integrated job profile data structure in
a data store of the TMS. The job profile data may further include
an occupation parameter identifying the occupation, and the method
may further comprise storing the job profile data in the integrated
job profile data structure.
[0025] The method may further include connecting to a user device
over a computer network, and determining that the first task
requires the individual to fulfill the first learning objective may
include sending a user interface including the first task and the
first learning objective to the user device, the user device
displaying the user interface to a user, and receiving from the
user device a user input comprising a selection of the first
learning objective. The method may further include receiving
training data representing one or more of the plurality of
integrated job profiles, the training data comprising a plurality
of integrated tasks each associated with one or more of the
plurality of learning objectives; determining that the first task
requires the individual to fulfill the first learning objective may
thus include determining that the first task is similar to one or
more similar tasks of the plurality of integrated tasks and
determining that one or more of the one or more similar tasks is
associated with the first learning objective.
[0026] The method may further include analyzing the job profile
data to obtain a plurality of keywords; thus, determining that the
first task requires the individual to fulfill the first learning
objective may include identifying at least one first keyword of the
plurality of keywords as being obtained from the first task and
determining that the first learning objective is associated with
the at least one first keyword. Determining that the first learning
objective is associated with the at least one first keyword may
include: identifying a term associated with the first learning
objective, the term selected from an ontology; and determining that
at least one of the at least one first keyword is associated with
the term. The method may further include identifying the occupation
from the job profile data, obtaining one or more employee records
each associated with an employee that held the occupation, and
determining an English proficiency score of each employee from the
corresponding employee record of the one or more employee records,
wherein the overall score is further calculated from the English
proficiency score of each employee associated with one of the one
or more employee records. Calculating the overall score from the
English proficiency score of each employee associated with one of
the one or more employee records may include using the
corresponding English proficiency score to validate the overall
score calculated from the corresponding scores of at least the
first learning objective and the second learning objective.
[0027] The evaluation or assessment of job profile data in order to
develop and validate scores for the job profile and its associated
parameters, described below, may be performed using any suitable
scoring methodology or combination of methodologies that is
applicable to the job skill(s) being evaluated. English proficiency
scoring methodologies include the Common European Framework of
Reference for Languages (CEFR), the Pearson Test of English ("PTE
Academic"), the International English Language Testing System
(IELTS), and the Test of English as a Foreign Language
(TOEFL.RTM.). In one example, described in detail herein, the
systems and methods may apply the Global Scale of English (GSE)
scoring methodology. The GSE is a standardised, granular scale from
10 to 90, which measures English language proficiency in each of
four functional skills: listening, reading, speaking and writing.
The GSE is psychometrically aligned with the CEFR. Unlike other
frameworks, which describe attainment in wide bands, the GSE
identifies what a learner can do in a more granular way at each
point (i.e., integer value) on the scale. It is therefore possible
to much more precisely show whether a learner--or a learning
objective, as described below--is situated toward the top or
bottom, or somewhere in the middle, of a comparatively wide-banded
level (e.g., the six wide levels of the CEFR).
[0028] The CEFR and the GSE each include a framework of learning
objectives with which the scores on the scale are associated. The
CEFR model describes the development of proficiency as quantitative
(i.e., how many tasks someone can perform) and qualitative (i.e.,
how well they perform them). Hence, the quantitative dimension is
expressed in terms of communicative activities, while the
qualitative dimension is expressed in terms of communicative
competencies. The CEFR also models and scales communicative
strategies, viewed as the link between communicative competencies
and communicative activities. According to a user's knowledge and
abilities, he or she will employ different strategies when
performing a given activity. Each CEFR learning objective is
described in terms of the competency it tests, and is associated
with one of the six levels of the scale.
[0029] The GSE framework extends, and fills gaps in, the framework
of the CEFR and modifies the way in which the learning objectives
are presented. Descriptors for GSE learning objectives relate to
functional activities (i.e., specific language tasks) rather than
competencies. In particular, the descriptors are typically composed
of three consecutive elements: performance, describing the language
function itself (e.g., "Can answer the telephone [in English]");
criteria, describing the intrinsic quality of the performance,
typically in terms of the range of language used (e.g., "using a
limited range of basic vocabulary"); and conditions, describing any
extrinsic constraints on the performance (e.g., "with visual
support," or "if spoken slowly and clearly"). In order to create a
set of learning objectives that can support a more granular scale
of measurement, the same task frequently occurs at multiple levels
of quality; the quality indicators are included in the learning
objective itself (i.e., via the criteria). Sociolinguistic and
pragmatic competencies are also included in the wording of the
learning objectives themselves, rather than being presented as a
separate set.
[0030] In the GSE, each integer value, or "score," on the scale is
associated with one or more learning objectives in each of the four
functional skills. Generally, someone who can perform the learning
objectives for one of the functional skills has achieved the
associated score in that functional skill; the overall GSE score is
an average or weighted average of the highest scores achieved in
the four functional skills. That said, language learning is not
necessarily sequential, and a learner might be strong in one area,
where he has had a lot of practice or a particular need or
motivation, but quite weak in another. For that reason, to say that
a learner is `at` a certain level on the Global Scale of English
does not mean he has necessarily mastered every GSE learning
objective for every skill up to that point. Neither does it mean
that he has failed to master any learning objective at a higher GSE
score. If an individual is assessed as being at 61 on the scale, it
means s/he has a 50% probability of being able to perform learning
objectives at that level, a greater probability of being able to
perform learning objectives at a lower level, and a lower
probability of being able to perform learning objectives at a
higher level.
[0031] Implementations described in detail herein operate upon data
structured into a job profile, and generate and display user
interfaces for enabling users to interact with an electronic
system, such as a TMS, and create, modify, locate, access,
retrieve, and perform other actions upon job profiles that describe
occupations requiring certain competencies in the English language.
Referring to FIG. 1, a computing system 100 in accordance with the
present disclosure includes a primary-executing computing device,
such as a server 102 having a processor that executes device logic
within the processor or contained in memory of the server 102. The
server 102 may be a server computer or a system of interconnected
server computers, such as a web server, application server,
application platform, virtual server, cloud data server, and the
like, a personal computer, laptop computer, tablet computer,
e-reader, smartphone, personal data assistant, set-top box, digital
media player, microconsole, home automation system, or similar
computing device having a central processing unit (CPU),
microprocessor, or other suitable processor. It should be
understood that there could be several cooperating servers 102 of
homogenous or varying types, layers or other elements, processes or
components, which may be chained or otherwise configured, which can
interact to perform tasks such as obtaining data from an
appropriate data store that is accessible locally to the
cooperating server 102 or remotely over the network. The server 102
can include any appropriate hardware, software and firmware for
integrating with the data store as needed to execute aspects of one
or more applications for the client device, handling some or all of
the data access and business logic for an application. The server
102 may provide access control services and is able to generate
content including text, graphics, audio, video and/or other content
usable to be provided to the user, which may be served to
requesting devices 104, 106 in any suitable format, including
HyperText Markup Language (HTML), Extensible Markup Language
("XML"), JavaScript (including JavaScript Object Notation
("JSON")), Cascading Style Sheets (CSS), or another appropriate
client-side structured language. Content transferred to a
requesting device 104, 106 may be processed by the requesting
device 104, 106 to provide the content in one or more forms
including forms that are perceptible to the user audibly, visually
and/or through other senses including touch, taste, and/or
smell.
[0032] The handling of all requests and responses, as well as the
delivery of content between a requesting device 104, 106 and the
server 102, can be handled by the server 102, such as a web server
using an appropriate server-side structured language in this
example. It should be understood that operations described as being
performed by a single device may, unless otherwise clear from
context, be performed collectively by multiple devices, which may
form a distributed and/or virtual system. Additionally, the server
102 or another computing device can make the content available to
other devices and in other services; this includes distributing the
content in any form, such as the integrated job profiles created
using the methods herein, as well as non-integrated job profile
data, learning objective data, and other data used by or available
to the server 102. In some embodiments, the application programming
interface (API) 120 described below or another API (e.g., a
representational state transfer ("REST") API) may be configured to
enable or facilitate access to the content by another computing
device or system.
[0033] The server 102 typically will include an operating system
that provides executable device logic for the general
administration and operation of that server 102 and typically will
include a computer-readable storage medium (e.g., a hard disk,
random access memory, read only memory, etc.) storing instructions
that, when executed by a processor of the server, allow the server
to perform its intended functions. Suitable implementations for the
operating system and general functionality of the servers are known
or commercially available and are readily implemented by persons
having ordinary skill in the art, particularly in light of the
disclosure. The environment, in one embodiment, is a distributed
and/or virtual computing environment utilizing several computer
systems and components that are interconnected via communication
links, using one or more computer networks or direct
connections.
[0034] The device logic configures the processor, and thus the
server 102, to perform the processes described herein. In some
embodiments, the server 102 may be a web server remote from other
devices of the system 100 and communicating with such devices over
the internet or another suitable electronic network or combination
of networks. The server 102 may implement all or a portion of a TMS
operated by a company; the portion of the TMS operated by the
server 102 may include hardware and/or software modules that
implement the job profile integration and/or user interface
generation techniques described herein.
[0035] The server 102 may be in communication, such as via an
appropriate electronic communication network, with a TMS terminal
104 in control of the company operating the TMS. The TMS terminal
104 may be any suitable computing device, such as a desktop or
tablet computer, smartphone or other mobile device, and the like.
Communications between the server 102 and the TMS terminal 104 may
be encrypted; a user of the TMS terminal 104 may be required to
provide authentication credentials to use the TMS terminal 104. The
server 102 may be remote from the TMS terminal 104 as described
above, or the server 102 and the TMS terminal 104 may be the same
computing device or discrete computing devices physically connected
to each other. In some embodiments, the server 102 may be a TMS
server operating the entirety of the TMS, and a plurality of TMS
terminals 104 may access the server 102 to perform talent
management functions related to recruiting, training, human
resources, administration, and the like. In some embodiments, the
server 102 may be operated by a party other than the company, such
as when the TMS is implemented using third-party service provider
software.
[0036] A network, as used herein, can include any appropriate
network, including an intranet, the Internet, a cellular network, a
local area network, a satellite network or any other network and/or
combination thereof. Components used for such a system can depend
at least in part upon the type of network and/or environment
selected. Protocols and components for communicating via such a
network are well known and will not be discussed in detail.
Communication over the network can be enabled by wired or wireless
connections and combinations thereof.
[0037] The server 102 may also be in communication with a visitor
terminal 106 via any suitable communication network. The visitor
terminal 106 may be any suitable computing device, such as a
desktop or tablet computer, smartphone or other mobile device, and
the like. The visitor terminal may access public or semi-private
functionality of the TMS via the server 102. In an example
described further below, a job seeker may use the visitor terminal
106 to search for job profiles managed by the server 102.
[0038] The server 102 may include or be in communication, via an
electronic network, with one or more TMS data stores 110. Generally
as used herein, a data store may be any repository of information
that is or can be made freely or securely accessible by the server
102. Suitable data stores include, without limitation: databases or
database systems, which may be a local database, online database,
desktop database, server-side database, relational database,
hierarchical database, network database, object database,
object-relational database, associative database, concept-oriented
database, entity-attribute-value database, multi-dimensional
database, semi-structured database, star schema database, XML or
JSON data object database, file, collection of files, spreadsheet,
or other means of data storage located on a computer, client,
server, or any other storage device known in the art or developed
in the future; file systems; and electronic files such as web
pages, spreadsheets, and documents. Each of the data stores may be
temporary or permanently implemented. In one embodiment, the server
102 may access a TMS data store 110 using an application
programming interface (API) 120.
[0039] A TMS data store 110 may include information used in any TMS
operation. The TMS data store 110 can include several separate data
tables, databases, data documents, dynamic data storage schemes
and/or other data storage mechanisms and media for storing data
relating to a particular aspect of the present disclosure,
including without limitation the data structures and user interface
data described herein. It should be understood that there can be
many aspects that may need to be stored in the TMS data store 110,
such as user information and access rights information, which can
be stored in any appropriate mechanisms in the TMS data store 110.
The TMS data store 110 may be operable, through logic associated
therewith, to receive instructions from the server 102 and obtain,
update, or otherwise process data in response thereto. The server
102 may provide static, dynamic or a combination of static and
dynamic data in response to the received instructions. Dynamic
data, such as data used in web logs (blogs), web or mobile
applications and application interfaces, news services and other
applications may be generated by server-side structured languages
as described or may be provided by TMS or another content
management system ("CMS") operating on, or under the control of,
the server 102.
[0040] As illustrated, a TMS data store 110 may include a plurality
of integrated job profiles 130 created in accordance with the
present disclosure. An integrated job profile 130 may be a database
record or set of records, a file, a data stream, or another
suitable stored data structure that includes a job profile 132 and
one or more learning objectives 150 associated therewith. The
integrated job profile 130 may include a score 152 (e.g., a GSE
score) or another value serving as a quality indicator, and may
further include an audience 154, associated with each learning
objective 150.
[0041] A job profile 132 may be a data structure that is unique to
the system 100, or may be one that is partially or fully
standardized as described above. The job profile 132 may be stored
in a job profile data store 112, which may be a component of the
system 100 or may be a data source situated externally to the
system 100. In one embodiment, the job profile 132 may conform to
the data structure for job profiles provided by O*NET Online, a
service of the United States Bureau of Labor and Statistics.
Advantageously, this arrangement allows for job profiles 132 stored
in the TMS data store 110 to be populated with data from job
profiles in the O*NET database (which is an example of a job
profile data store 112). The job profile 132 may include all or a
subset of the parameters in a standardized data structure. In the
illustrated exemplary embodiment, which may facilitate
interoperability with certain job profile databases, the job
profile 132 includes at least the following parameters: occupation
134, which identifies an occupation and may be a job title, job
category, job type, or other suitable descriptor (e.g.,
"accountant,"; one or more work activities 136, which are
descriptors of abstract or generalized job functions used across
may different occupations (e.g., "gathering information," or
"analyzing data"); one or more detailed work activities 138 each
associated with one of the work activities 136 and providing more
detail with respect to the associated work activity 136 (e.g., for
a work activity 136 of "analyzing data" in the occupation 134
"biologist," a detailed work activity 138 may be "analyzing
biological samples"); and one or more tasks 140, each of which a
person with the occupation 134 is expected to accomplish, and each
of which may be related to a detailed work activity 138, such as by
being a condition precedent to accomplishing the detailed work
activity 138.
[0042] Where the job profile 132 is external to the system, the
server 102 may directly, or through the API 120, retrieve or
receive the job profile 132 from the job profile data store 112.
The server 102 integrates the job profile 132 with the present TMS
that tracks English proficiency requirements, by associating one or
more (e.g., two to three) learning objectives 150 with the job
profile 132, such as by establishing relationships 160 of learning
objectives 150 with each of the tasks 140. As described above, the
learning objectives 150 (e.g., in the CEFR and GSE frameworks) each
demonstrate a certain English proficiency. The learning objectives
150 and associated data (e.g., score 152) may be obtained from a
learning objectives data store 114, which may be a component of the
system 100 or may be a data source situated externally to the
system 100. Generally, the system 100 is used to identify one or
more learning objectives 150 as related to (e.g., needed to
perform) a task 140; when this relationship 160 is defined, the job
profile 132 data, the data for (or data references to) the
associated learning objectives 150, and the relational framework
are stored in the TMS data store 110 as a data record for an
integrated job profile 130, using any of the methods described
below. The learning objectives 150 may be obtained from a scoring
methodology, such as the GSE, and therefore each may have a score
152 (e.g., on a scale of 10 to 90 for GSE) associated therewith.
Furthermore, each learning objective 150 may identify an audience
154 to which it pertains. For example, the GSE framework of
learning objectives is divided into four audiences: young learners,
adult learners, academic english learners, and professional-use
english learners.
[0043] FIG. 2 illustrates a method 200 by which the server 102 may
integrate a job profile according to user input. The user providing
the user input may be using the TMS terminal 104 or another
suitable input device having a connection to the server 102. The
server 102 may provide a user interface to the TMS terminal 104,
which the TMS terminal 104 then displays to the user. The user
interface may enable the user to view data sent to the TMS terminal
104 by the server 102, and to enter user input that the TMS
terminal 104 transmits to the server 102, according to the method
200. At step 202, the server 102 may receive user input identifying
a job profile to be integrated into the TMS. The user may enter
data, such as a job title, or may select the job profile in a
display (e.g., in the user interface), to generate the user
input.
[0044] At step 204, the server 102 may receive the identified job
profile, such as by querying the job profile data store 112 using
the user input. All or a portion of the data for a job profile may
be received. In some embodiments, the job profile includes a
plurality of tasks as described above, and the server 102 may
receive a representative set of the tasks, rather than all of the
tasks (although all of the tasks may be received). In an exemplary
embodiment, an externally stored job profile (e.g., one provided by
O*NET Online) includes a plurality of "core" job tasks, and an
importance score representing how important the core job task is to
the performance of occupational duties is assigned to each core job
task; the server 102 may receive only a certain number (e.g., ten)
of the core job tasks having the highest importance scores.
Furthermore for this example, some or all of the core job tasks may
have supplementary tasks, and the server 102 may ignore the
supplementary tasks.
[0045] In some embodiments, such as when the server 102 does not
locally store a database of learning objectives (e.g., in a TMS
data store 110), the server 102 may, directly or through the API
120, access a remote learning objectives data store 114. However,
the server 102 may not be configured, or may have insufficient
information, to retrieve the relevant learning objectives.
Additionally, a learning objectives data store 114 may have a large
amount of content that is infeasible to be wholly retrieved by the
server, particularly when the entire database is not needed. For
example, the GSE framework at present includes over 1500 learning
objectives, and only a fraction of them may be relevant to the job
profile. Therefore, at step 206 the server 102 may establish a
communication interface 260 with the learning objectives data store
114 for the benefit of the TMS terminal 104. That is, the server
102 may connect the TMS terminal 104 to the learning objectives
data store 114 using any suitable channels, protocols, and
interfaces, so that the user can use the TMS terminal 104 to search
the learning objectives data store 114 and identify the relevant
learning objectives. In some embodiments, the interface 260 may
create a direct connection, such as by sending to the TMS terminal
104 an internet address for the learning objectives data store 114
or an API therefor. In other embodiments, the interface 260 may
indirectly connect the TMS terminal 104, such as by receiving
queries and commands from the user via the user interface on the
TMS terminal 104 and sending the queries and commands to the
learning objectives data store 114, then reversing the data flow to
deliver results to the user.
[0046] At step 208, the server 102 may select a task of the job
profile that has not yet been processed by the integration method
200. At step 210, the server 102 may send the selected task to the
user device (e.g., TMS terminal 104) for display in the user
interface. At that point, the communication interface 260
negotiates the user's search for and identification of learning
objectives that are needed and/or are preferred to be met by an
employee in order to accomplish the task. In embodiments where the
learning objectives are in the GSE framework, the server 102 may
direct the user to identify at least one learning objective in each
of the four functional skill categories (listening, reading,
speaking, writing). In some embodiments, the server 102 may direct
the user to identify a plurality of learning objectives having
different scores. For example, the user may be directed to select
learning objectives that are minimally necessary, learning
objectives that the company wishes to see achieved in a qualified
candidate, and learning objectives that an excellent candidate is
able to achieve; these three selections produce three different
scores that may be used to calculate the low, average, and high
scores (e.g., GSE scores) for the integrated job profile.
[0047] At step 212, the server 102 receives user input identifying
the selected learning objectives. The server 102 may receive each
selection by the user as it is made, or may receive all of the
selections at once when the user has finished identifying the
learning objectives. In some embodiments, the server 102 may
optionally determine whether each identified learning objective is
present in the TMS data store 110, at step 214. This may be
performed in implementations where the TMS data store 110 is
configured to contain associated data for each learning objective.
If the check (step 214) is performed, and the learning objective is
not in the TMS data store 110, the server 102 may, at step 216,
retrieve all or a desired subset of the learning objective data
from the learning objective data store 114 and add the learning
objective and its associated data to the TMS data store 110.
[0048] At step 218, the server 102 may associate each of the
identified learning objectives with the selected task. In one
embodiment, this association is performed by retrieving the
learning objective, the associated score(s), and any other desired
data elements associated with the learning objective, from the
learning objective data store 114, and adding the retrieved data
elements to a data structure representing the integrated job
profile. In adding the retrieved data, the server 102 may maintain
any relational information between data elements, and may further
create the relationship between the learning objective and the
task. In another embodiment, the association is performed by adding
a reference to the learning objective's physical location in a data
store, which may be the TMS data store 110 or the learning
objective data store 114. Advantageously, the data records for the
integrated job profiles are smaller in this embodiment because they
do not contain the learning objective data itself, avoiding
repetitious storage of such data.
[0049] At step 220, the server 102 may determine if there are any
more tasks in the job profile that have not been processed using
the integration method 200. If there are more tasks, the server 102
returns to step 208 to continue the integration. If all tasks have
been processed, at step 230 the server 102 may create a data record
for the integrated job profile in the TMS data store 110. In some
embodiments, the server 102 may simply write the data structure
generated through step 220 to memory. In other embodiments, the
server 102 may perform further processing at this step, such as
collecting keywords for the learning objectives to be used in other
processing methods (see below) and storing the keywords in the data
structure. At this step 230, the server 102 may compute additional
scores using the raw scores associated with the selected learning
objectives.
[0050] In an exemplary embodiment using GSE scores, the server 102
may calculate: the average score in each of the four functional
skill categories; from the raw scores or the functional skill
average scores, an overall GSE score for the occupation; from the
ranges of raw scores, the low and high scores in each function
skill category; and, from the raw scores or the low and high
functional skill scores, the low and high overall GSE scores. The
server 102 may further calculate the standard deviation of any of
the calculated average scores. The standard deviation, in some
embodiments, may represent the range of scores that indicate a
minimum required English proficiency; that is, a person scoring
below the range determined from the standard deviation could be
expected to face significant challenges performing the English
language tasks of the associated occupation. Any of the calculated
scores may be stored in the data record for the integrated job
profile.
[0051] Other processing at step 230 may include categorizing the
integrated job profile and/or associating the integrated job
profile with related integrated job profiles in the TMS data store
110. In embodiments where the non-integrated job profile was
obtained from a job profile data store 112, the job profile may
already contain data for categorizing or associating the integrated
job profile. For example, the O*NET database maintains occupation
categories (e.g., "finance") and stores the category of an
occupation as a field in the occupation's job profile; the
integrated job profile may retain the category field. Other
examples of associating the integrated job profile are described
below.
[0052] Referring to FIG. 3, another integration method 300 that is
directed by user input begins with the steps of receiving the
identification of a job profile (step 302), receiving the job
profile (step 304), and selecting a task for integration (step
306), which are described above with respect to steps 202-206 of
FIG. 2. At step 308, the server 102 may use the data associated
with the selected task or the job profile to identify a set of
learning objectives that are potentially related to the task. In
one example, the server 102 may determine that the category of the
job profile is identical, similar, synonymous with, or related to
one or more categories of learning objectives, and may determine
the learning objectives of the category(ies) to be potentially
related to the task. At step 310, the server 102 may send the task
and one, some, or all of the identified learning objectives to the
TMS terminal 104 to be presented in the user interface. At step
312, the server 102 may receive user input identifying the learning
objectives selected by the user. The server 102 may then associate
the learning objectives with the task (step 314), check for
additional un-integrated tasks (step 316), and then create the data
record for the integrated job profile (step 320) as described above
with respect to steps 218-230 of FIG. 2.
[0053] FIG. 4 illustrates a method 400 in which the server 102
applies machine learning algorithms to integrate a job profile. At
step 402, the server 102 may receive a training set of integrated
job profiles. The training set may include a number of profiles
that have already been integrated using one or more of the methods
described herein (e.g., by user-directed integration according to
the method 200 of FIG. 2). Thus, the training set represents a
plurality of learning objectives that are already linked to job
profiles and can be identified as relevant to a category, a work
activity, a task, or any other data element of a job profile. The
training set may further include additional data related to the
previous integration processing of the members of the set. For
example, the training set may track user validation data received
from a user device (e.g., in step 412 of the present method 400);
such user validation data may indicate data elements of learning
objectives that were misidentified as relevant, or that were not
identified as relevant and should have been. The training set may
be general, or may be specific to a particular category or other
suitable data element. The training set may be stored in the TMS
data store 110 as a list or table of references to the profiles of
the training set, or the training set may contain complete copies
of the integrated job profiles that belong to it.
[0054] At step 404, the server 102 may receive a job profile, and
at step 406 the server 102 may select a task for integration, as
described above with respect to steps 202 and 208, respectively, of
FIG. 2. At step 408, the server 102 may compare data elements of
the task to data elements of one or more of the tasks in the
training set to determine a similarity of the selected task to the
pre-integrated tasks. In one embodiment, the server 102 may use any
suitable text comparison technique to determine a degree to which
the selected task and the pre-integrated task contain the same
words. In another embodiment, the server 102 may apply term
frequency calculations to the training set to determine keywords
that appear with high frequency in the tasks, and the server 102
may determine whether the description of the selected task contains
any of the keywords. A predetermined threshold similarity may be
used to retain the most similar tasks.
[0055] At step 410, the server 102 may identify the learning
objectives that are associated with the similar tasks. At step 412,
the server 102 optionally may validate the identified learning
objectives by communicating them to one or more user devices, such
as the TMS terminal 104, and receiving back confirmation that
satisfaction of the identified learning objectives is needed and/or
desired to accomplish the task. The user input received from the
user devices may cause the server 102 to remove or add learning
objectives, in some embodiments. The server 102 may then associate
the learning objectives with the task (step 414), check for more
tasks to be integrated (step 416), and create the data record for
the newly integrated job profile (step 420), as described above
with respect to steps 218-230 of FIG. 2. Additionally, at step 420
the server 102 may add the newly created integrated job profile to
the training set, and/or may otherwise update the training set with
user validation data and/or other data obtained during the
integration process 400.
[0056] FIG. 5 illustrates another method 500 of integrating job
profiles, in which the server 102 automatically identifies learning
objectives that are relevant to a task using keyword comparison. At
step 502, the server 102 receives a job profile as described above
with respect to step 202 of FIG. 2. At step 504, the server 102 may
parse the job profile data to obtain a plurality of keywords. Any
suitable technique or combination of techniques for term extraction
may be used to identify descriptive words in the job profile data
as keywords, and any or all of the data elements (e.g., occupation,
job description, category, work activities, tasks, etc.) may be
analyzed to extract the keywords. The server 102 may use a suitable
database of words and phrases, including standard dictionaries,
keyword dictionaries, occupational glossaries, and the like, which
may be generated and/or stored in the TMS data store 110 or another
internal or external data store. The server 102 may further use the
word databases, such as a thesaurus, to determine synonyms of the
identified keywords, and may identify the synonyms as keywords as
well. If the server maintains a keyword dictionary, the identified
keywords may be added thereto. In some embodiments, the server 102
may track which keywords are identified from each of the tasks, for
use in step 510 described below.
[0057] At step 506, the server 102 may obtain some or all of the
learning objectives associated with some or all of the keywords
identified from the job profile data. This step 506 presumes an
existing data store in which the available learning objectives have
been associated with keywords. An external data store such as the
learning objective data store 114 may store the learning objectives
in this manner, and the server 102 may query the learning objective
data store 114 using the keywords. In addition, or alternatively,
the present system may maintain its own such data store within the
TMS data store 110 or another internal data store. Learning
objectives may be stored in a data structure with associated
keywords, or with terms that are ontologically related to other
words in an index, dictionary, or other suitable ontology that
facilitates keyword searching of the learning objectives in the
data store.
[0058] At step 508, the server 102 selects one of the tasks. At
step 510 the server 102 identifies, from the learning objectives
obtained at step 506, the learning objectives that are associated
with keywords identified from the particular task. The server 102
may use its own data generated to track the source of the
identified keywords, in order to delineate those that are relevant
to the task. If the source was not tracked, the server 102 may
perform any suitable text comparison to determine the keywords that
are relevant to the task. The server 102 then validates the
identified learning objectives (step 512), associates the learning
objectives with the task (step 514), checks for additional tasks to
integrate (step 516), and creates a data record for the integrated
job profile (step 520), as described above with respect to steps
412-420 of FIG. 4. In some embodiments, at step 520 the server 102
may further update the association of the selected learning
objectives with keywords of the associated tasks.
[0059] FIG. 6 illustrates yet another integration method 600.
Rather than building the English proficiency integration up through
analysis of tasks, in the exemplary method 600 the server 102
determines proficiency scores for an occupation using the scores of
employees who hold or have held the occupation. At step 602, the
server 102 receives a job profile to be integrated. At step 604,
the server 102 identifies the occupation of the job profile. At
step 606, the server 102 may query its own TMS data store 110, and
any other suitable internal or external data store, to identify one
or more employees (or former employees, or contractors, or other
individuals, collectively referred to as "employees") that
currently hold or previously held the identified occupation.
[0060] At step 608, the server 102 may determine the English
proficiency scores of the identified employees. This presumes that
the company scores its employees (e.g., at hiring or in periodic
evaluations), and that the TMS data store 110 or another internal
data store retains the scores. In embodiments using the GSE
framework, the server 102 may collect the overall GSE score and/or
one or more of the functional skill scores. At step 610, the server
102 may calculate one or more averages or weighted averages of the
collected GSE scores, using any suitable methodology. These scores
are the scores for the occupation, and thus for the integrated job
profile, and may include an overall score, sub-scores for
particular categories, and score ranges, in accordance with the
scores of the representative employees. For the GSE framework, the
scores may include one or more of an overall GSE score, GSE scores
for the functional skills, and high and/or low limit scores for the
overall and the functional skill categories.
[0061] At step 612, the server 102 may optionally identify one or
more learning objectives that are associated with the scores
produced in step 610. For example, in the GSE framework a subset of
the learning objectives produce a particular integer score; this
subset can be further limited to those learning objectives
belonging to a particular functional skill category and/or directed
at a particular audience. The server 102 may keep all of the
learning objectives in one of the subsets, or may further determine
those that are relevant to the occupation. For example, the server
102 may use the keyword comparison techniques described above, or
another suitable text comparison technique, to determine the
learning objectives that have the desired score and are relevant to
the occupation. At step 614, the server 102 may associate the
identified learning objectives with the job profile. Additionally
or alternatively, the server 102 may associate the calculated
score(s) with the job profile, creating an integrated job profile.
Finally, at step 620 the server 102 may create the data record for
the integrated job profile in the TMS data store 110 as described
above.
[0062] The described methods are exemplary and may be combined
and/or modified to improve the accuracy, efficiency, computing
resource overhead, or other aspect of the integration process. In
some embodiments, for example, the method 600 of FIG. 6 for
identifying scores for an occupation using existing employee data
may be applied to validate scores obtained using one or more of the
other described methods. Thus, the present systems may be evaluated
to determine whether the job profile integration methods are
producing reliable scores by comparing the produced scores to
scores of actual employees. Table 1 demonstrates an exemplary
result of any of the integration processes, whereby tasks common to
several analytical occupations are assigned learning objectives
from the GSE framework; the corresponding GSE score and functional
skill category of each learning objective is also listed. The
results may be understood with reference to an exemplary job
description: "Conduct organizational studies and evaluations,
design systems and procedures, conduct work simplification and
measurement studies, and prepare operations and procedures manuals
to assist management in operating more efficiently and effectively;
includes program analysts and management consultants."
TABLE-US-00001 GSE Learning Objective Skill Score Task Importance
Can write a report explaining in detail a Writing 71 Gather and
organize 70 work-related problem, the actions taken, information on
problems and the results of those actions. or procedures. Can scan
a long text or a set of related Reading 63 texts in order to find
specific information. Can interpret the main message from Reading
62 Analyze data gathered 70 complex diagrams and visual
information. and develop solutions or Can understand the main
information in Reading 53 alternative methods of technical
work-related documents. proceeding. Can use persuasive language to
convince Speaking 74 Confer with personnel 69 others to agree with
their recommended concerned to ensure course of action during a
discussion. successful functioning of Can take part in routine
formal discussions Speaking 60 newly implemented conducted in clear
standard speech in systems or procedures. which factual information
is exchanged. Can participate in extended, detailed Speaking 80
Review forms and 65 professional discussions and meetings reports
and confer with with confidence. management and users Can
understand complex technical work- Reading 79 about format,
distribution, related documents in detail. and purpose, and to
identify problems and improvements. Can carry out an effective,
fluent interview, Speaking 72 Interview personnel and 65
spontaneously following up on interesting conduct on-site replies.
observation to ascertain Can write a detailed report of
work-related Writing 69 unit functions, work events. performed, and
methods, equipment, and personnel used. Can link a logical series
of ideas leading to Writing 68 Document findings of 65 a suggested
conclusion in a written report. study and prepare Can carry out a
prepared interview, Speaking 57 recommendations for checking and
confirming information as implementation of new necessary. systems,
procedures, or organizational changes. Can describe in detail a
change in the way Speaking 70 Prepare manuals and 63 a business is
run. train workers in use of Can present information related to the
Speaking 68 new forms, reports, business in a formal discussion.
procedures or equipment, according to organizational policy. Can
write a report describing business Writing 76 Plan study of work 61
plans and strategies in detail. problems and Can describe in detail
how a change will Speaking 70 procedures, such as help the company,
its employees, or its organizational change, customers.
communications, information flow, integrated production methods,
inventory control, or cost analysis. Can write a detailed
structured report on Writing 70 Design, evaluate, 61 work-related
topics. recommend, and Can write a plan of action detailing a
Writing 67 approve changes of problem, how it will be fixed, and
when. forms and reports.
Table I: Learning Objectives and Tasks in Example Integrated Job
Profile
[0063] Any suitable number of job profiles may be integrated into
the TMS using the systems and methods of assessing necessary
English language competencies for the associated occupation and
associating learning objectives and scores with the tasks of the
job profile. In one example, the present system may integrate the
entire O*NET database of job profiles, creating an integrated job
profile for each occupation in the O*NET database. In another
example, the TMS of a company may include only job profiles that
are relevant to the company's operations. The system may be
configured to add, modify, and remove integrated job profiles, and
may retrieve and format the integrated job profiles for display in
a user interface, or for use in other data processing tasks.
Several types of users of the system may access the TMS database to
retrieve or perform other operations on one or more integrated job
profiles. Exemplary uses of an integrated job profile by different
types of users are illustrated in FIGS. 7-11 and are now
described.
[0064] FIG. 7 illustrates a relationship between the GSE scores for
a number of potential candidates to a GSE score associated with an
integrated job profile. Integrated job profile 702 includes a
number of details of the job profile including key skills and GSE
expectations associated with the job profile (in this example, an
accountant position). Specifically, integrated job profile 702
includes list 704 of learning objectives that have previously been
associated with the job. Typically, list 704 of learning objectives
includes a set of learning objectives that, once completed by a
particular candidate, would render that candidate potentially
qualified for the job profile.
[0065] The learning objectives could be associated with integrated
job profile 702, for example, by performing an automated or manual
analysis of a textual content of a job profile and, based upon that
analysis, associating the job profile with a set of learning
objectives. In various embodiments, a job profile may be processed
according to any of the methods of FIGS. 2-6 to associate the job
profile with various learning objectives.
[0066] Integrated job profile 702 also depicts a target GSE score
706 for the job profile. GSE score 706 may indicate a GSE score
that must be exceeded by a particular candidate before that
candidate can be considered for the position. Alternatively, GSE
score 706 may depict a GSE score that a typical candidate for the
position should have achieved. In various other examples, GSE score
706 could represent a score that an excellent candidate for the
position may have achieved.
[0067] FIG. 7 also depicts a number of different candidates 708 for
the position of the integrated job profile 702. Each candidate 708
has been previously evaluated and is now associated with a
particular GSE score. The GSE score for a particular candidate 708,
as described herein, may be established using any suitable
mechanism. For example, personal GSE scores may be established by
testing the candidates or by determining that the candidates have
completed particular learning objectives or classes associated with
particular learning objectives.
[0068] The GSE score of each candidate 708 may be used as a
mechanism for determining whether a particular candidate 708
qualifies for or may ultimately be successful in the position
associated with the integrated job profile 702. If, for example,
GSE score 706 of integrated job profile 702 represents a minimum
threshold GSE score that must be exceeded before a candidate will
be considered, candidates with GSE scores below 62 (in this
example) may automatically be disqualified. In other cases,
however, GSE score 706 of integrated job profile 702 may represent
a guide indicating that candidates with lower GSE scores may not be
strong candidates, or may have to make up for the lower GSE score
by presenting other skills and attributes that make them stronger
candidates.
[0069] In this manner, the GSE score associated with integrated job
profile 702 can be a useful guide for candidates and recruiters in
determining whether a particular candidate is well suited to a
position described by a particular job profile.
[0070] FIG. 8, for example, shows user interface 800 enabling a
user to search through a number of integrated job profiles and
review GSE scores associated with those integrated job profiles.
User interface 800 may be displayed by any suitable computing
device, such as a desktop or tablet computer, smartphone or other
mobile device, and the like. User interface 800 may be displayed
via a website hosted by a computer server operated by, for example,
an entity that maintains a database of job profiles.
[0071] User interface 800 provides a number of mechanisms by which
a user can search for and identify a particular integrated job
profile. For example, a user could enter a keyword search or other
type of search into search bar 802. After the keywords are entered
and the search is executed, a listing of job profiles 804 matching
the search keywords can be displayed in job profile listing
804.
[0072] Alternatively, a user may navigate through a hierarchical
listing of available job profiles in sidebar navigation 806. Within
sidebar navigation 806, a number of job profiles can be arranged in
groupings of related job profiles enabling the user to drill-down
through various categories of job profiles in order to identify a
desired job profile. Once a desired job profile or category of job
profiles is identified, it can be selected by the user and
additional information about the job profile or a listing of job
profiles falling within the selected category will display in
listing of job profiles 804.
[0073] Within listing of job profiles 804, each job profile entry
may display various details associated with a particular job
profile. For example, as shown in FIG. 8, job profile 808 includes
a title as well as a description of the category in which the job
profile belongs ("Business and Financial Operations
Occupations>Financial Specialists"). Job profile 808 is an
integrated job profile. Consequently, job profile 808 has
previously been analyzed and associated with a particular GSE score
810. GSE score 810 of integrated job profile 808 is displayed
within listing of job profiles 804 enabling a user to quickly
identify GSE score 810 associated with integrated job profile 808.
As such, a user using user interface 800 to search for and browse
details of various job profiles can quickly identify any GSE score
requirements that may be associated with a particular job
profile.
[0074] As such, user interface 800 may be used by a potential
candidate who knows his or her own GSE score to search for and
identify job profiles for which the candidate may be well
qualified. Similarly, a recruiter, searching for appropriate job
profiles on behalf of one or more candidates can identify job
profiles having GSE requirements suited to the recruiter's
candidates.
[0075] In some embodiments, the user, via user interface 800, can
select one of the job profiles listed in listing of job profiles
804 in order to learn more about that particular job profile. In
one example, the additional detail may be viewed by selecting
description link 812. To illustrate, FIGS. 9 and 10 depict
screenshots showing additional detail for a particular integrated
job profile. FIG. 9 shows additional detail of a job profile in
which higher-level GSE scores and learning objectives are shown,
while FIG. 10 shows a similar detail view of a job profile, but
with lower-level GSE scores and learning objectives.
[0076] In FIG. 9, detailed view 900 includes a depiction of general
GSE score 902 for the integrated job profile as well as a
description of tasks or skills that may be associated with the
integrated job profile. As described below, GSE score 902 of
detailed view 900 shows the GSE score based upon a collection of
high-level learning objectives that have previously been associated
with the integrated job profile. The listing of tasks or skills may
be associated with an original job profile, or may, in some
embodiments, be derived from the learning objectives that have
previously been associated with the integrated job profile.
[0077] Detailed view 900 also lists a number of learning objectives
906 that have previously been associated with the integrated job
profile. As described earlier, the tasks of a particular job
profile may be associated with different sets of learning
objectives, where a first set of learning objectives may be
associated with a first level of competency for the job profile and
a second set of learning objectives may be associated with a
different level of competency for the job profile. In this manner,
a set of "high-level" learning objectives and corresponding GSE
scores could be associated with a particular job profile. In that
case, a candidate that has met those learning objectives or has a
personal GSE score that meets or exceeds the combined score (e.g.,
average GSE score) for each of the high-level learning objectives
may represent an excellent, highly qualified candidate for the job
profile.
[0078] Additionally or alternatively, a set of low-level learning
objectives and corresponding GSE scores could be associated with a
particular job profile. The low-level learning objectives and GSE
scores may represent a minimum set of learning objectives or
minimum GSE score that must be achieved before a candidate may even
be considered qualified for a particular job profile. In that case,
a candidate that has met the low-level learning objectives or has a
personal GSE score that meets or exceeds the combined score (e.g.,
average GSE score) for each of the low-level learning objectives
may merely meet a lowest threshold for being qualified for the job
profile.
[0079] In FIG. 9, a set of high-level learning objectives for the
job profile is depicted. Each learning objective is shown with an
associated GSE score as well as a brief description of the learning
objective. Additionally, the functional skill associated with each
learning objective is shown in detailed view 900. If the user
wishes to learn more about one of the learning objectives 906 the
user can select one of the learning objectives 906. The user
interface could then provide the user not only with information
about the selected learning objective, but access to resource
enabling the user to take classes or review instructional material
meeting the selected learning objective.
[0080] Detailed view 900 shows additional information for the job
profile, such as listing of similar job titles 908. The job
profiles that are contained within listing of similar job titles
908 may include hyperlinks to other job profiles that are similar
to the job profile currently being viewed. Similar job profiles can
be identified by any suitable approach, including keyword
comparisons of the descriptions of the job profiles, as well as
comparison of the learning objectives that have been associated
with the other job profiles. In some cases, the job profiles
included within the listing of similar job titles 908 may only
include integrated job profiles having GSE scores that are within a
threshold amount (e.g., 5% or 10%) of the GSE score of the
integrated job profile currently being viewed within detailed view
900. Detailed view 900 may also include a summary of tasks 910
associated with the integrated job profile currently being
viewed.
[0081] FIG. 10 shows additional detail of a job profile in which
lower-level GSE scores and learning objectives are depicted.
Detailed view 1000 shown in FIG. 10 is similar to that illustrated
in FIG. 9, but, in FIG. 10, GSE score 1002 has been derived from a
set of lower-level learning objectives that have previously been
associated with the integrated job profile. Similarly, detailed
view 1000 lists a number of lower-level learning objectives 1004
that have previously been associated with the integrated job
profile. Each learning objective is shown with an associated GSE
score as well as a brief description of the learning objective.
Additionally, the functional skill associated with each learning
objective is shown in detailed view 1000. If the user wishes to
learn more about one of the learning objectives 1004 the user can
select one of the learning objectives. The user interface could
then provide the user not only with information about the learning
objective, but access to resource enabling the user to take classes
or review instructional material meeting the selected learning
objective.
[0082] The user interfaces illustrated in FIGS. 8-10 allow users
(e.g., job candidates, recruiters, human resources staff, and the
like) to search for and filter job profiles based upon GSE scores.
Using these systems a candidate can quickly identify job profiles
that, based upon the candidate's own GSE score, match the candidate
well.
[0083] In addition to providing for the analysis of a single
integrated job profile, the present system can enable a user to
browse through and review a number of job profiles that are all
grouped together within a particular career path. FIG. 11, for
example, shows user interface 1100 depicting a potential career
path 1102 including a number of different job profiles 1104 within
that career path 1102. User interface 1100 may be accessed, for
example, using the "see career path" buttons 912 and 1006 of FIGS.
9 and 10, respectively. When either of "see career path" buttons
912 or 1006 are executed, a career path associated with the job
profile currently being viewed is identified. This may involve
retrieving, for example, a career path notation that is stored in
associated with the job profile, or automatically determining a
career path for the job profile (e.g., based upon a textual
analysis of the contents of the job profile). Once a particular
career path is identified, a number of other job profiles belonging
to the same career path can be identified.
[0084] A number of job profiles may be grouped together into a
career path, as described herein, in any suitable manner. In some
cases, a database (e.g., hosted by an entity that maintains records
associated with each of the job profiles) may store one or more
linked lists associating a number of different job profiles
together in an ordered list. A single ordered list of job profiles
may then be associated with a particular career path. In other
cases, a group of job profiles may simply be associated with (e.g.,
tagged) with a particular career descriptor (e.g., finance or
marketing). Job profiles that share the same career descriptor can
then be associated into a particular career based upon that
descriptor.
[0085] Within user interface 1100, each job profile 1104 of the
career path is associated with a GSE score. Job profiles 1104 are
arranged as in a bar graph, where the height of each bar associated
with job profiles 1104 represents the GSE score associated with
each job profile 1104. In other embodiments, any suitable approach
may be used to depict a set of related job profiles, where that
depiction may or may not include a depiction or representation of
the GSE scores associated with each job profile.
[0086] In the case that job profiles are grouped together into a
career path by means of a descriptor, such an approach does not
provide information describing the order in which the job profiles
would tend to occur within that career. In that case (and with
reference to FIG. 11), the job profiles could simply be sorted in
order of ascending GSE score associated with the job profiles. In
that case, job profiles with lower GSE scores would be depicted
earlier within the career path 1102 than other job profiles having
higher associated GSE scores.
[0087] User interface 1100 also shows a summary 1106 of learning
objectives associated with the career path. The depicted summary
1106 may be determined, for example, by first identifying the
learning objectives that have previously been associated with the
job profiles of the career path. When all learning objectives for
all job profiles 1104 in the career path have been identified, a
statistical breakdown of the learning objectives (e.g., what
percentage the learning objectives fall into each of the functional
skills categories) can be calculated and depicted. If the user
wishes to see more information regarding the summary 1106 of
learning objectives, the user can, for example, click on one of the
functional skills categories (e.g., reading) to see summary 1108,
which depicts information describing the learning objectives that
fall within the selected functional skills category.
[0088] By reviewing the career path depiction of user interface
1100 a user, aware of his or her GSE score, can begin to make
longer term career plans. If the user should wish to move into a
new position associated with a new job profile, the user can review
the GSE score requirements for that new position and begin planning
how to complete the necessary learning objectives in order to meet
the GSE requirements for that new position. The user can also see
how completing new learning objectives may enable the user to
pursue new career opportunities, possibly resulting in increased
income or employment stability.
[0089] In another embodiment, a TMS of a company may match a
candidate, from a plurality of candidates to a job profile, from a
plurality of job profiles. The TMS may generate the plurality of
job profiles. Each job profile may corresponds to a job in a
plurality of jobs. Each job profile may comprises an indicator of
language proficiency. In a preferred embodiment, the indicator of
language proficiency is a minimum Global Scale of English (GSE)
score. Each job profile may also comprise a plurality of tasks that
an individual must be able to complete in performing the job that
corresponds to the job profile;
[0090] The TMS may host the plurality of job profiles in an
electronic network database. A user interface may be configured to
enable the candidate to search through the plurality of job
profiles and review the minimum language proficency score (or
minimum GSE score) associated with each job profile;
[0091] The TMS may evaluate a plurality of candidates and assign a
language proficiency score (or a GSE score) to each candidate in
the plurality of candidates. This evaluation may occur over any
given period of time and may be a continuous or ongoing
process.
[0092] The TMS may match a candidate, in the plurality of
candidates, to a job profile, in the plurality of job profiles,
based at least in part on a language proficiency score (or a GSE
score) of the candidate and the minimum language proficiency score
(or minimum GSE score) for the job profile. In preferred
embodiments, the TMS also compares a plurality of tasks that the
candidate can complete to the plurality of tasks that an individual
must be able to complete in performing the corresponding job. In
preferred embodiments, matches require that the candidate's
language proficiency (preferably measured by the GSE) is at or
above that required for the job (as indicated by the job profile)
and the candidate is able to peform the tasks that are in the job
profile for the job.
[0093] The TMS may transmit a job that corresponds to the job
profile that matched the candidate to the user interface for
viewing by the candidate.
[0094] In some embodiments, the TMS not only shows a job that has
been matched to a candidate, but the TMS may determine and transmit
an entire career path to the candidate. In this embodiment, the TMS
may group a first plurality of job profiles, from the plurality of
job profiles, into a first career path and a second plurality of
job profiles, from the plurality of job profiles, into a second
career path.
[0095] The TMS may determine a particular career path for the
candidate, wherein the particular career path comprises the job
that corresponds to the job profile that matched the candidate. The
TMS may transmit the particular career path to the user interface
for viewing by the candidate. The particular career path may
comprise two or more job profiles sorted in order of ascending GSE
score. In this manner, the candidate may advance through the
particular career path as the candidate's language proficiency (or
GSE score) improves over time.
[0096] The various embodiments further can be implemented in a wide
variety of operating environments, which in some cases can include
one or more user computers, computing devices or processing devices
that can be used to operate any of a number of applications. User
or client devices can include any of a number of general purpose
personal computers, such as desktop, laptop or tablet computers
running a standard operating system, as well as cellular, wireless
and handheld devices running mobile software and capable of
supporting a number of networking and messaging protocols. Such a
system also can include a number of workstations running any of a
variety of commercially available operating systems and other known
applications for purposes such as development and database
management. These devices also can include other electronic
devices, such as dummy terminals, thin-clients, gaming systems and
other devices capable of communicating via a network. These devices
also can include virtual devices such as virtual machines,
hypervisors and other virtual devices capable of communicating via
a network.
[0097] Various embodiments of the present disclosure utilize a
network that would be familiar to those skilled in the art for
supporting communications using any of a variety of
commercially-available protocols, such as Transmission Control
Protocol/Internet Protocol ("TCP/IP"), User Datagram Protocol
("UDP"), protocols operating in various layers of the Open System
Interconnection ("OSI") model, File Transfer Protocol ("FTP"),
Universal Plug and Play ("UpnP"), Network File System ("NFS"),
Common Internet File System ("CIFS") and AppleTalk. The network can
be, for example, a local area network, a wide-area network, a
virtual private network, the Internet, an intranet, an extranet, a
public switched telephone network, an infrared network, a wireless
network, a satellite network, and any combination thereof.
[0098] In embodiments utilizing a web server, the web server can
run any of a variety of server or mid-tier applications, including
Hypertext Transfer Protocol ("HTTP") servers, FTP servers, Common
Gateway Interface ("CGP") servers, data servers, Java servers,
Apache servers, and business application servers. The server(s)
also may be capable of executing programs or scripts in response to
requests from user devices, such as by executing one or more web
applications that may be implemented as one or more scripts or
programs written in any programming language, such as Java.RTM., C,
C# or C++, or any scripting language, such as Ruby, PHP, Perl,
Python or TCL, as well as combinations thereof. The server(s) may
also include database servers, including those commercially
available from Oracle.RTM., Microsoft.RTM., Sybase.RTM.,
cloudDNA.RTM., and IBM.RTM. as well as open-source servers such as
MySQL, Postgres, SQLite, MongoDB, and any other server capable of
storing, retrieving, and accessing structured or unstructured data.
Database servers may include table-based servers, document-based
servers, unstructured servers, relational servers, non-relational
servers or combinations of these and/or other database servers.
[0099] The present systems can include a variety of data stores and
other memory and storage media as discussed above. These can reside
in a variety of locations, such as on a storage medium local to
(and/or resident in) one or more of the computers or remote from
any or all of the computers across the network. In a particular set
of embodiments, the information may reside in a storage-area
network ("SAN") familiar to those skilled in the art. Similarly,
any necessary files for performing the functions attributed to the
computers, servers or other network devices may be stored locally
and/or remotely, as appropriate. Where a system includes
computerized devices, each such device can include hardware
elements that may be electrically coupled via a bus, the elements
including, for example, a central processing unit ("CPU" or
"processor"), an input device (e.g., a mouse, keyboard, controller,
touch screen or keypad), and an output device (e.g., a display
device, printer or speaker). Such a system may also include one or
more storage devices, such as disk drives, optical storage devices
and solid-state storage devices such as random access memory
("RAM") or read-only memory ("ROM"), as well as removable media
devices, memory cards, flash cards, etc.
[0100] Such devices also can include a computer-readable storage
media reader, a communications device (e.g., a modem, a wireless or
wired network card, an infrared communication device, etc.), and
working memory as described above. The computer-readable storage
media reader can be connected with, or configured to receive, a
computer-readable storage medium, representing remote, local,
fixed, and/or removable storage devices as well as storage media
for temporarily and/or more permanently containing, storing,
transmitting, and retrieving computer-readable information. The
system and various devices also typically will include a number of
software applications, modules, services, or other elements located
within a working memory device, including an operating system and
application programs, such as a client application or web browser.
It should be appreciated that alternate embodiments may have
numerous variations from that described above. For example,
customized hardware might also be used and/or particular elements
might be implemented in hardware, software (including portable
software, such as applets) or both. Further, connection to other
computing devices such as network input/output devices may be
employed.
[0101] Storage media and computer readable media for containing
code, or portions of code, can include any appropriate media known
or used in the art, including storage media and communication
media, such as, volatile and non-volatile, removable and
non-removable media implemented in any method or technology for
storage and/or transmission of information such as computer
readable instructions, data structures, program modules or other
data, including RAM, ROM, Electrically Erasable Programmable
Read-Only Memory ("EEPROM"), flash memory or other memory
technology, Compact Disc Read-Only Memory ("CD-ROM"), digital
versatile disk (DVD) or other optical storage, magnetic cassettes,
magnetic tape, magnetic disk storage or other magnetic storage
devices or any other medium which can be used to store the desired
information and which can be accessed by the system device. Based
on the disclosure and teachings provided, a person of ordinary
skill in the art will appreciate other ways and/or methods to
implement the various embodiments.
* * * * *