US-12626319-B1 - Systems and methods for evaluating career interests through situation judgment test format

US12626319B1US 12626319 B1US12626319 B1US 12626319B1US-12626319-B1

Abstract

Systems and methods are provided for evaluating career interests through situational judgment test format. In embodiments, a career assessment is generated, using a first language based machine learning model and a set of criteria. The career assessment is administered to a user. A plurality of test answers for the user are received based on the career assessment. For a particular test answer a score is assigned for the particular test answer and at least one user career interest category based on the score is determined. The totals for the at least one user career interest category based on the determining is tabulated. At least one career suggestion is provided, using a second language based machine learning model, based on the totals for the at least one user career interest category.

Inventors

Kevin M. Williams
Devon M. Kinsey
Patrick C. Kyllonen

Assignees

EDUCATIONAL TESTING SERVICE

Dates

Publication Date: 20260512
Application Date: 20231115

Claims (20)

1 . A computer-implemented method comprising: training a first language based machine learning model with a plurality of training sets to receive a job setting and a career interest category and generate a job task, wherein each training set comprises an example job task corresponding to an example job setting and an example career interest category; using the first language based machine learning model, a list of career interest categories, and a list of job settings to generate a career assessment comprising a plurality of questions, each question comprising a job setting and a plurality of answer items; administering the career assessment to a user; receiving a plurality of test answers for the user based on the career assessment; for a particular test answer: assigning a score for the particular test answer; and determining at least one user career interest category based on the score; tabulating totals for the at least one user career interest category based on the said determining; and providing at least one career suggestion, using a second language based machine learning model, based on the totals for the at least one user career interest category.
2 . The method of claim 1 , wherein each question is in a situational judgment test format and the corresponding answer items are job tasks performable at the corresponding job setting.
3 . The method of claim 1 , further comprising: receiving the list of career interest categories; identifying the list of job settings; identifying answer items for each job setting in the list of job settings based at least on a first career interest category; and compiling the answer items for each career interest category in the list of career interest categories for a first job setting in the list of job settings to generate a first question of the plurality of questions.
4 . The method of claim 1 , further comprising: training the second language based machine learning model that provides the at least one career suggestion to identify a first career based at least on a first career interest category; receiving a third second career interest category; and identifying a second career, using the second language based machine learning model and the second career interest category, wherein the at least one career suggestion includes the second career.
5 . The method of claim 1 , wherein the career assessment is customized for the user by considering a set of criteria comprising work experience, education level, age, and/or location.
6 . The method of claim 1 , wherein the second language based machine learning model is trained on career training data, which comprises a plurality of careers and, for each career, a plurality of job tasks and a plurality of relevant career interest categories.
7 . The method of claim 1 , wherein the particular test answer corresponds to more than one relevant career interest category.
8 . The method of claim 1 , wherein the score comprises a relevance value for at least one career interest category, wherein the relevance value evaluates how closely related the test answer is to the at least one career interest category.
9 . The method of claim 1 , further comprising comparing the totals for the at least one career interest category to totals for the same career interest category from an alternate career assessment administered to the user.
10 . The method of claim 9 , further comprising retesting the user on the generated career assessment and the alternate career assessment; and comparing the totals from the first round of testing to the totals for the second round of testing to analyze a consistency of results of the career assessments.
11 . The method of claim 1 , further comprising: receiving a key, the key comprising directions for how to assign the score for the particular test answer.
12 . The method of claim 11 , wherein a scoring engine receives the plurality of test answers and the key and assigns the score for the particular test answer, wherein the scoring engine is configured to generate a matrix form data structure that interrelates potential answer items for the particular answer to the list of career interest categories, wherein each potential answer item-career interest category pair receives a value in the matrix form data structure to be applied to the tabulation of the total for that career interest category if the user chooses that potential answer item, and wherein the value in the matrix form data structure for each potential answer item-career interest category pair is based on whether that particular answer item recites a job task that corresponds to that particular career interest category.
13 . The method of claim 11 , wherein assigning a score for the particular test answer comprises: using the key to create a matrix of potential scores for each career interest category for each of the plurality of answer items, wherein the one or more career interest categories that correspond to the particular test answer receive a positive potential score.
14 . The method of claim 1 , wherein the career assessment comprises at least one video aspect.
15 . The method of claim 14 , further comprising: generating the at least one video aspect using a third language based machine learning model and user background criteria, wherein the user background criteria comprise physical characteristics of the user.
16 . The method of claim 15 , wherein the at least one video aspect includes an avatar generated based on the physical characteristics of the user.
17 . The method of claim 15 , wherein the third language based machine learning model is trained to generate a first video aspect using a first set of background criteria, a second job setting, and a list of answer items.
18 . The method of claim 15 , wherein the third language based machine learning model is trained on a plurality of job setting related videos with prompts for different career interests.
19 . The method of claim 15 , wherein the third language based machine learning model generates a video aspect for each question of the plurality of questions based on the generated plurality of answer items and the corresponding job setting.
20 . A system for evaluating career interest comprising: a processing system comprising one or more data processors; and a computer-readable medium encoded with instructions for commanding the processing system to execute steps comprising: training a first language based machine learning model with a plurality of training sets to receive a job setting and a career interest category and generate a job task, wherein each training set comprises an example job task corresponding to an example job setting and an example career interest category; using the first language based machine learning model, a list of career interest categories, and a list of job settings to generate a career assessment comprising a plurality of questions, each question comprising a job setting and a plurality of answer items; administering the career assessment to a user; receiving a plurality of test answers for the user based on the career assessment; for a particular test answer: assigning a score for the particular test answer; and determining at least one user career interest category based on the score; tabulating totals for the at least one user career interest category based on the said determining; and providing at least one career suggestion, using a second language based machine learning model, based on the totals for the at least one user career interest category.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Patent Application No. 63/425,727, filed Nov. 16, 2022, which is incorporated by reference herein in its entirety. TECHNICAL FIELD The technology described herein relates to evaluating career interests through situational judgment test format. BACKGROUND Career assessment can help job seekers glean valuable insights into their specific skills and abilities that may be relevant to different types of careers. Many job seekers lack career interest self-awareness, and a career assessment may provide an informed job search. Situational judgment tests (SJTs) require a test taker to respond to a variety of hypothetical scenarios. SJTs may increase test-taker engagement and decrease careless responding. Career interests can be classified in different ways. One example is using the RIASEC model, which breaks job tasks into six different categories: realistic, investigative, artistic, social, enterprising, and conventional. SUMMARY Systems and methods are provided for a computer-implemented method for evaluating career interests through situational judgment test format. An example system performs steps, including generating a career assessment, using a first language based machine learning model and a set of criteria. The example system further administers the career assessment to a user and receives a plurality of test answers for the user based on the career assessment. Then, for a particular test answer in the plurality of test answers, the system assigns a score for the particular test answer and determines at least one user career interest based on the score. The example system tabulates totals for the at least one user career interest category based on the determining step and provides at least one career suggestion, using a second language based machine learning model, based on the totals for the at least one user career interest category. As another example, a method for evaluating career interests through situational judgment test format is provided. A career assessment is generated, using a first language based machine learning mode and a set of criteria. The career assessment is administered to a user. A plurality of test answers for the user are received based on the career assessment. For a particular test answer in the plurality of test answers, a score is assigned for the particular test answer and at least one user career interest is determined based on the score. Totals for the at least one user career interest category based on the said determining are tabulated. At least one career suggestion is provided, using a second language based machine learning model, based on the totals for the at least one user career interest category. As a further example, a computer-readable medium is encoded with instructions for commanding one or more data processors to execute a method for evaluating career interests through situational judgment test format. The example method includes generating a career assessment, using a first language based machine learning model and a set of criteria. The example method further administers the career assessment to a user and receives a plurality of test answers for the user based on the career assessment. Then, for a particular test answer in the plurality of test answers, the method assigns a score for the particular test answer and determines at least one user career interest based on the score. The example method tabulates totals for the at least one user career interest category based on the determining step and provides at least one career suggestion, using a second language based machine learning model, based on the totals for the at least one user career interest category. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a diagram depicting an example system for evaluating career interests through situational judgment test format. FIG. 2 is a diagram depicting example details of an career assessment generation engine. FIG. 3 is a diagram depicting example details of a test generation engine. FIG. 4 is a diagram depicting example details of a scoring engine. FIG. 5 is a diagram depicting example details of a key. FIG. 6 is a diagram depicting example details of a career evaluation engine. FIG. 7 is a flow diagram depicting an example method for evaluating career interests through situational judgment test format. FIG. 8 is a diagram depicting an example job setting, careers and job tasks. FIG. 9 is an example career interest report. FIG. 10 is another example career interest report. FIG. 11 is a table depicting example high point and low point scores for a career interest evaluation at two instances at two different timepoints. FIG. 12a is a table depicting an example correlation between a career interest assessment and an OIF-SF assessment at a first instance at a first timepoint. FIG. 12b is a table depicting an example correlation between a career interest assessment and an OIF-S