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How to improve Micro-credentials through the Learning Ecosystem?

Updated: May 7, 2024

Micro-credentials have emerged in recent years as a response to labor market demands transitioning from traditional degrees to a skills-based approach. However, in this transition, the learning ecosystem faces numerous challenges in reducing the skill mismatch between job workers and market demands. These challenges include qualification level disparities and issues with the quality of learning content, among others. This raises questions such as: How can educational institutions improve micro- credentials? What tools are available to enhance skills identification? And how can the quality of learning content be improved across diverse micro-credentials?


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What are micro-credentials?

Micro-credentials are the record of the learning outcomes acquired after a small volume of learning certified by various providers within the learning ecosystem or industry, focusing on specific skills relevant to industries. They aim to certify individual skills or sets of skills and can be accumulated as part of a career pathway towards higher education qualifications. (Wheelaha, Moodie, 2021, Council of EU, 2022). Over time, micro-credentials have evolved into diverse units of learning with varying quality, providers, and qualification standards.


Challenges of Micro-credentials

The rapidly changing landscape of technology has resulted in continuous modifications of job descriptions, skills demand, and learning services. Consequently, micro-credentials, as a recent response from the learning ecosystem and industry, have led to a proliferation of learning solutions facing two main challenges:


i) Lack of quality in the learning content: The multiplicity of new learning providers has created a high volume of solutions, including micro-credentials. However, the quality of content generated by these providers often fails to meet learners' needs due to factors such as low qualifications of internal teams, lack of quality standards among providers, and absence of impact indicators for learning content.


ii) Level of qualification mismatch: Micro-credentials are industry-oriented and contribute to lifelong learning qualifications. However, unclear identification of skill requirements in micro- credential development has resulted in a mismatch between supply and demand. For example, learners may obtain certifications for highly specialised industry skills when their needs are more generic, leading to a saturation of learning solutions without a clear skill pathway. In contrast, a highly skill professional who is looking for up- skilling in a particular industry may obtain a generic certification due to this saturation.


Improving Micro-credentials through the Learning Ecosystem

Nowadays, the learning content designed for the micro-credentials is based in different and imprecise readings of the job demand. However, improving micro- credentials can be achieved by obtaining detailed insights into job market requirements in terms of skill sets. In addition, utilising new technological tools, such as systematic job posting analysis with Large Language Models, enables the creation of skill profiles based on detailed job postings. These profiles show relations among skills according to the job postings and relate the sets to occupational levels. (Labussière, Bol, 2024).


Better information about detail skill profiles according to a great number of job postings leads to a stronger foundation for curricula development, learning content as well as skill pathways related to the occupational levels.


Regarding the enhancement of quality, there is a significant opportunity for learning providers and governmental institutions to collaboratively develop minimum quality guidelines for skill-based learning content. This can be based on more accurate and detailed information derived from skill profiles created using new technological tools in a systematic manner. In addition, a transparent relation between qualifications, macro-credentials, micro-credentials, learning outcomes, and skills is needed for learners to have more accurate and higher-quality learning paths.


ACTUA contributes to the improvement of this and other related topics, opening the discussion among stakeholders to generate better quality guidelines as well as encouraging the use of more accurate data for the learning content development by the learning providers. Do not hesitate to contact ACTUA for more information!


By: Juliana Lopez


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