Using Data Science approaches to build Automated Job-role specific Skills Assessment to close the unemployment gap and handle applications surge post COVID-19 lockdown.
Using Data Science approaches to build Automated Job-role specific Skills Assessment to close the unemployment gap and handle applications surge post COVID-19 lockdown.
批准号:
59291
负责人:
金额:
$9.2万
依托单位:
依托单位国家:
英国
项目类别:
Feasibility Studies
财政年份:
2020
资助国家:
英国
项目状态:
已结题
起止时间:
2020 至 --
中文摘要
点击翻译按钮获取中文摘要
英文摘要
Knowledge Officer create personalised, dynamic e-learning paths to digital content for professionals based on their own career goals, to help them land jobs in tech-related roles. Our approach to career-oriented learning is based on real market-signals. At the core is skills and job titles taxonomy which provides a consistent way of helping users learn more about the skills they need and the value of those skills. We want our platform to reflect the exact and current market needs related to skills and requirements of the job roles of the highest demand. Based on this valuable data, our technology then recommends a personalised learning path, as unique to each learner as their own DNA, and also recommends job applications relevant to the career goal that they are after. Knowledge Officer aim to create the shortest and most efficient path to employment and career progression. Our e-learning platform technology builds a personalised learning path, helping the user to learn with a purpose to achieve their career goals. Understanding the true capabilities of people as they are going through learning through ongoing assessment is really essential to measure the impact of the learning experience. Pairing that with recommendations for jobs that one can apply to based on the specific job needs and one's capability is truly innovative.Our solution will measure the proficiency at the individual skill level grade and compare that with the skills needed for a certain job post of a given job role to measure the match score and the probability of someone's success on the job. Our scoring system will get smarter with more people going through assessments, applying to jobs, getting accepted/rejected and then measuring the duration they stay on the job as a true measure of the quality of the recommendation.This project will conduct challenging research to extend our technology so that given a job post, we will be able to generate an adaptive assessment on the fly based on the skills required of the post and generate a match/fit score after a job candidate finishes the assessment.A three month project extension will allow for more extensive testing activities to increase the impact and speed to market by further collaboration with potential partners, giving access to technical, design and business expertise. Beta testing with early adopters will ensure the product will have the maximum impact at launch.
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