Beyond Academic Credentials: Competency Assessment and the Future of Work
Shaunna-Marie Kerr, Senior Manager, Global Talent Bridge, WES
In Canada, as in other high-income countries around the world, globalization and technological advances are reshaping the workplace. Automation and artificial intelligence (AI), among other technological innovations, are transforming the skills that workers need to fuel the country’s economy. New skill requirements arise daily, while others quickly become obsolete. In many ways, the COVID-19 pandemic has accelerated these trends, forcing businesses to identify and adopt new digital technologies—and the workers qualified to use them—almost overnight.
This transformation is also reshaping hiring practices. Employers are reporting difficulty recruiting, hiring, and retaining workers who have the skills that new roles require. This skills gap, experts warn, puts the nation’s economic prosperity and future competitiveness at risk.
Given Canada’s ageing population and low birth rates, immigrants will play an important role in filling that gap. A 2018 study from the Conference Board of Canada noted that immigrants already account for around 90 percent of the country’s labour force growth. Recognizing the importance of immigrants, the Canadian government has made it a priority to attract those who are highly skilled. The government recently increased its immigration targets to more than 1.2 million over the next three years, the highest targets in the country’s history. In the government’s “vision for the future,” as Marco Mendicino, minister of Immigration, Refugees and Citizenship Canada (IRCC), told Reuters, immigration will be “one of the keys to our economic recovery and our long-term prosperity.”
But welcoming high-skilled immigrants is only half the battle. A 2019 WES report, Who Is Succeeding in the Canadian Labour Market, found evidence of “brain waste”—the underutilization of education and skills in the workplace, among economic class new arrivals to Canada. Less than half were working in the same sector as they were prior to emigrating. The gap between the skills of immigrants and the recognition of those skills by employers will need to be closed in order to fulfill the government’s “vision for the future.”
As the leading provider of academic credential assessments in Canada and the United States, World Education Services (WES) has long pushed for academic institutions, regulatory bodies, local and national governments, employers, and others to accept and recognize international academic qualifications. Countless internationally educated individuals have used WES credential evaluations to attain access to academic institutions and the workplace. To supplement their résumés, these individuals have used their academic credential assessments to help employers understand their educational qualifications.
Employers looking to recruit talent in today’s fast-paced economy need to understand more than a job seeker’s education, however. With technology rapidly reshaping the workplace, they also need to assess and recognize a job seeker’s professional skills and competencies.
To help address the challenges outlined above, WES is exploring the potential of competency assessments. Our 2019 paper, Beyond Academic Credentials: Toward Competency-Informed Hiring, defined this assessment type as an approach that looks “holistically at an individual’s ability to apply knowledge and skills with appropriate judgement in a defined setting.” A competency-based assessment can supplement an academic credential evaluation by identifying the individual’s skills and behaviours that employers value. The assessment can also facilitate the consideration of these skills in hiring decisions.
The results of our initial explorations will be published in a series of articles in WENR. By working to identify effective competency assessment tools, we hope to empower immigrant job seekers, career service providers, and employers to promote the hiring of immigrants in positions commensurate with their skills. WES believes that developing and improving competency assessment tools is not only critical to the health of the economy, it is also a moral obligation.
History of Competency Assessment
The theoretical underpinnings of competency assessments are more than a century old. In the late 1800s, John Dewey, an American philosopher and education reformer, argued that knowledge is only valuable to the extent that it can be successfully leveraged as an instrument to predict, control, or guide future experiences. A learner attains this practical knowledge not by passively receiving fixed ideas and theories, but through a variety of educative “experiences” which allow learners to actively acquire knowledge and skills as a means of achieving a goal. Viewing knowledge as an instrument for lived experience suggests that possession of it can be determined by examining an individual’s functionally observable behaviours, and not just their academic qualifications. Dewey’s concepts remain central to modern educational theory and, increasingly, to employment and recruitment practices. As the global economy evolves, his concepts have prompted educators and enterprises around the world to seek out and prioritize methods that identify and evaluate practical competence.
In the 1970s, academic institutions began to examine the ways in which the systematic assessment and recognition of an individual’s prior learning, whether formal, informal, non-formal, or experiential, could be considered in admissions decisions. Although what came to be known as Prior Learning Assessment and Recognition (PLAR) remained at the service of purely academic pursuits for a long time, it was eventually extended to employment decisions as well. An important milestone in the recognition of prior learning for employment purposes was reached in 2006, when the province of Ontario passed the Fair Access to Regulated Professions and Compulsory Trades Act. The Act was intended to ensure that registration practices in regulated professions were “transparent, objective, impartial and fair.”
The same year, a Canadian Association for Prior Learning Assessment (CAPLA) study, Recognizing the Prior Learning of Immigrants to Canada, funded by the Government of Canada, was released. The study brought into greater focus the barriers to labour market inclusion that international talent faced, and the need for adequate assessment and acceptance of their experience. In 2015, CAPLA subsequently released the Recognition of Prior Learning Quality Assurance Manual, which provides “a basic framework for the development and implementation of RPL [Recognition of Prior Learning].”
The 1997 Lisbon Recognition Convention, officially known as the Convention on the Recognition of Qualifications concerning Higher Education in the European Region, is an example of a largely successful recognition agreement. It establishes the principle that, among countries that signed the agreement, immigrants should be integrated into the workplace in positions that are commensurate with their knowledge and skills. Even so, the period from treaty signing to ratification in Canada was more than 20 years, finally occurring in 2018, and the practical application of this treaty is not always fully understood.
Although many stakeholders agree that competency assessments are needed, there is no uniform framework which is accepted and implemented by all. The varied approaches currently practiced are reflected in the multiple terms used to describe similar assessment processes. Prior Learning Assessment (PLA), Prior Learning Assessment and Recognition (PLAR), and Recognition of Prior Learning (RPL) are all in common use. As researchers have pointed out, these umbrella terms can “engender confusion,” making competency assessment tools less likely to be accepted by educators, career service professionals, or end users, such as employers.
These previous initiatives and theories of competency assessment and prior learning helped inform a series of exploratory projects WES undertook to identify practices that could help job seekers, career service professionals, employers, and other stakeholders to comprehend, recognize, identify, and assess an individual’s prior learning, experience, and competencies. The aim was to successfully leverage this prior learning and integrate immigrant skills professionally.
WES Exploratory Pilots
To test our early assumptions about the usefulness of skills-based solutions, WES compiled a high-level inventory of skill identification and competency assessment tools that were used globally. To test whether these tools enabled employers to hire highly skilled immigrants, we partnered with organizations that used relevant technology-enabled approaches. Each of the organizations stood out because of their unique and ethical applications of technology, their belief in the need to improve labour market outcomes during periods of disruption, and their focus on skills-commensurate employment.
WES – SkillLab Project: Identifying and Exploring Unrecognized Skills
SkillLab, an Amsterdam-based social enterprise, developed an intriguing technology-based system aimed at promoting local level labour market participation of displaced individuals. Designed with a refugee population in mind, SkillLab’s mobile app enables people to identify and share their skills, knowledge, and abilities regardless of their background, language, or formal education and work experience. With a focus on empowering individuals through skills-based employment, SkillLab uses AI to generate comprehensive skill profiles which can be translated and applied to local labour contexts. Developed in Europe, the app draws on the occupational taxonomies developed in the European Skills/Competences, Qualifications and Occupations Framework (ESCO).
WES engaged SkillLab and three career service delivery partners (ACCES, COSTI, and TCET, now known as Achēv) that participate in the WES Gateway Program to explore the feasibility of applying this software in Canada, initially in Ontario. Working together with these career service providers, we conducted a process mapping exercise, identifying dozens of discrete “activities” which span our partners’ client journey—from initial contact through the end of their employment services programming. From these activities, our partners selected career action planning, employment counselling, and career aptitude assessments as key areas of potential for the SkillLab tool to increase capacity and improve the overall results of employment services programming.
A central component of the WES–SkillLab collaboration was the use of the software in three skill identification workshops held at each of the three career service organizations mentioned above. Each workshop included a small group of participants who self-identified as being unemployed or underemployed and stated that a barrier to skills-commensurate employment was a lack of recognition of their skills and experience. During the skill identification workshops, participants used the SkillLab mobile app to input their education, employment, and informal experiences. They then answered a series of dynamic questions customized by SkillLab’s AI technology about the various skills they used in those settings. These questions helped to construct a more accurate and complete skill profile than what could have emerged through the participants’ self-reporting alone.
By the end of the workshops, the SkillLab app had created a skill profile for each participant that identified the participant’s skills and potential aptitude for various occupations. Generated in the language of the participant’s choosing (in this case, Arabic), the skill profile and occupational aptitudes report were immediately available to the service provider in the language of their choosing (in this case, English). These skill profiles could then be used by both the individual and the employment counsellor or service provider to better focus employment services through skills-based job searches and career counselling.
Although providers agreed that the skill profile offered a more holistic view of an individual’s skills than methods that measured only educational and employment experience, they highlighted the need for job seekers to use it in combination with a career service provider that can offer information on local labour markets and realistic career pathways. Both providers and participants were interested in applying SkillLab’s technology to career exploration. The AI’s ability to identify an individual’s previously unrecognized skills helped participants to consider exploring new careers and opportunities.
Providers saw a great value proposition for this technology in youth programming as well as in geographic areas where fewer employment service providers exist, because of the digital nature of the tool and the possibility of remote use. While the scope of the WES–SkillLab project was limited in terms of regional representation, delivery partners, and active participants, the project’s overall findings—regarding skills-based approaches to employment service provision and the ability of a digital and AI-enhanced tool to add capacity to the immigrant employment service sector—were encouraging.
WES – SkyHive Project: Getting the Most Out of Academic Credential Assessments
While exploring the potential programmatic applications of the SkillLab mobile app, we were also engaged in discussions with a Canadian corporation, SkyHive Technologies Inc., that focused on the intersection between credential recognition, competency assessment, and supply and demand in the Canadian labour market. Founded in 2016 with a goal of bringing economic empowerment to marginalized, equity-seeking groups (such as women and people of colour), SkyHive developed a proprietary method of labour market analysis that it calls Quantum Labour Analysis. Using machine learning, deep learning, and natural language processing, SkyHive is able to identify and assess skill sets on a large scale, providing to employers insight into the skills of their workforce and the larger labour market from which they recruit talent.
Machine Learning, Deep Learning, and Natural Language Processing
Machine learning is a linear, self-adaptive algorithm that provides better analysis as it receives more data. Put simply, it gets smarter the more information it has. An example of machine learning is an algorithm that progressively learns to identify patterns in data, improving its ability to detect disruptions to the pattern.
Deep learning is a subset of machine learning that is non-linear and self-adaptive. It is an artificial intelligence function that mimics the way the human brain processes and creates patterns in data for use in decision-making using hierarchical leveling of artificial neural networks.
Natural language processing, sometimes called text mining, can be viewed as the interaction and intersection of human languages and computers. It uses techniques like vectorization to process and analyze large sets of data.
SkyHive’s technological sophistication also allows it to operate effectively on a smaller scale, for example, at the level of the individual job seeker. The SkyHive platform is able to identify the individual’s skill sets, applicable training opportunities, and specific, real-time labour market openings, as well as project dynamic career pathways. While the technology’s potential applications are practically endless, WES and SkyHive planned and implemented a pilot project aimed specifically at testing the feasibility of converting academic credential information into skills-based data.
The WES–SkyHive project sought to better understand immigrant underemployment and identify period-specific indicators of the supply and demand level of certain skills in the Canadian labour market. In the first stage of the project, with stringent privacy protections in place, WES exported five years’ worth of aggregate and anonymized applicant data from a single country for processing and analysis through SkyHive’s proprietary technology. SkyHive analyzed structured and unstructured data to identify and catalog skills from the credential evaluation data WES provided. It then juxtaposed the resulting list of skills with those needed to fill Canada’s 100 most in-demand occupations over the same period as determined by its analysis of labour market supply and demand. Data were clustered and ranked to determine not only the types of skills clusters represented in the Canadian labour market, but also to rank those skills according to how much they were in demand, and by the level of supply available among the sample of skilled immigrants WES had provided.
In the second stage of the project, 25 individual applicants from the aggregate data set were recruited and referred directly to SkyHive for onboarding to the platform. This was done to examine pre- and post-arrival skill sets at a deeper level, specifically looking at post-arrival skill use in employment, loss of skills due to lack of use, and further skills development.
This project provided a data touchpoint for the conversation around the mismatch between labour market supply and demand, suggesting a practical application. It also illustrated a novel use of traditional academic evaluations—to further skills recognition for potential employment purposes. WES’ support for the tech-enabled approaches necessary to catalyze progress in the future of work, and SkyHive’s demonstrated commitment to innovative and ethical uses of AI combined to suggest the possibility of greater traction on labour market inclusion through cross-sector collaboration.
This pilot was, in many ways, pioneering. WES shared data and information about its operations and business processes at large scale; and, in line with its “tech for good” ethos, SkyHive contributed significant resources and expertise in kind. The combined results of the first and second stages of the project highlighted the need to address inefficiencies in the labour market, and to develop tools to unify currently fragmented competency identification and assessment processes. While there have long been calls for the creation of a pan-Canadian competency framework to increase labour mobility, this pilot also highlighted the need to ensure accurate, real-time renderings of what is in reality a highly fluid and dynamic labour market.
Employing Untapped Talent
Throughout WES’ preliminary exploration in the area of skill identification and competency assessment, the dichotomy between the ease with which employers understand and recognize skills assessments and the difficulty with which job seekers express their skills was a common theme. Also highlighted by our research, and the abovementioned pilots, was the idea that a skills-based approach can be valuable for job seekers at all skill levels. Referring back to Dewey’s assertion that knowledge and experience are inextricably linked, it seems natural to quantify and qualify each by the other, especially with regard to employment and hiring. As parts two and three of this series will make even clearer, a competency-informed approach to skills assessment can enable internationally educated job seekers, career services providers, and employers to better identify, assess, and leverage a vast store of untapped talent.
The views and opinions expressed in this article are those of the author(s) and do not necessarily reflect the official policy or position of World Education Services (WES).