The skills-based approach in the learning field
In a post-covid context where distancing is encouraged at all levels of the company, digital learning and blended learning (a mix of distance and face-to-face learning) will become the most popular learning formats. This is an opportunity for us to reflect on how to improve the digital learning experience. The direct human link is distended, the screen is used as an interface with the learning content. How can we guarantee the employees’ commitment to their learning?
Part of the answer lies in the individualization of the paths. By taking into account the employee’s profile, personal objectives, learning rhythm and by pushing learning courses chosen according to these 3 criteria, you maximize the chances of engaging and motivating the employee, which are key factors for success.
Today, Artificial Intelligence allows us to scale the process of individualizing the employees’ learning paths (How companies have got to grips with Adaptive Learning technologies). But to put in place this personalized approach, it is necessary to have a repository to clearly identify the starting point of the employee and where he wants to go. In this repository, artificial intelligence will help determine the optimal path to reach the objectives set according to the learners’ profile.
The skills-based approach consists in using skills and occupations, within an organization, as a frame of reference.
The skills-based approach
According to Roegiers1 (2000), skill “is the ability of an individual to consciously and in an organized way, mobilize an integrated set of resources (including knowledge, skills and strategies) to solve a family of problem situations.” The skills-based approach therefore starts with the business modeling in the company. When it comes to the occupation, its day-to-day tasks and the complex situations it has to solve, we are able to identify all the knowledge, savoir-faire and social skills that it must be able to mobilize in a competent manner to successfully carry out its missions.
Initiatives are being taken at the level of countries, NGOs and multinationals to set up research commissions to build and align occupational-skills repositories. One example is the European Union’s ESCO project, which “identifies and categorizes abilities, skills, qualifications and professions relevant to the job market, education and learning in the EU”. Once this occupational-skills repository has been established, it appears that the same skill can be found in several occupations. However, the level of proficiency expected for the same skill is not the same depending on the jobs. Hence the importance of setting up a scale of levels, common to the company in order to align departments, business units and entities on the same standards. The job expectations will correspond to the combination of skills and levels of proficiency expected to carry out the tasks of the occupation correctly.
We recommend, for example, to define a scale of 4 levels of proficiency ranging from concept (minimum level) to expertise (maximum level).
In the skills-based approach, the job expectations represent the end point, the objective to be achieved. The employee must now be positioned in relation to his job expectations to know his starting point and to identify the skill gap to be filled, i.e. the gap between his expected level and his current level. To carry out this positioning, it is recommended to set up an assessment repository, based on the skills reference system. The assessment resources will make it possible to confirm or invalidate a given level of proficiency for one or more competencies.
This diagnostic can be done in different ways. It is possible to obtain it from data already possessed by the company, such as data from annual interviews, for instance. We will then speak of peer assessment, because it is the managers, colleagues or managed teams who assess the level of an employee on a panel of key skills. Positioning can also be done through self-assessment by the employee himself. Eventually, specialized tools can be used to assess skills, such as those using adaptive quizzes that adapt in real time the level of the questions asked according to the learner’s answers. (Benefits of Domoscio solutions #1: the learner)
Each assessment method is interesting. Some are more or less adapted to the type of skill being assessed. Self-assessment is a good way to evaluate skills. Peer assessment is particularly suitable for social skills and adaptive quizzes are very effective for assessing knowledge. Ideally, these assessments should be used together to position employees on all of their job skills.
The learning field
The traditional approach places learning at the heart of the construction of learning paths. In the skills-based approach, learning is seen as a means for the employees to upskill to achieve their job objectives and meet the company’s skills needs.
From a scaling perspective of the personalization of learning paths, we therefore need to build a final repository also based on the skills repository: the learning repository. This repository maps the impact of each learning course on the identified level of proficiency. By taking into account the initial positioning and the job expectations, an adaptive learning tool uses Artificial Intelligence and the learning repository to select the most relevant resources to guide the employees as best as possible in their upskiling.
Job repositories, assessment repositories, learning repositories… This work of skills-based modeling is used to improve the pedagogy and commitment of learners while meeting the needs of the company. This approach allows to gain visibility on the available skills and those missing within the structure. Combined with the right learning tools, it makes it possible to only push resources that are relevant and necessary to employees, and thus optimize learning costs and the time spent while learning.
Source1 : Xavier Roegiers is an engineer, teacher and doctor in educational sciences. Professor at the French Catholic University of Louvain-la-Neuve.