In recent years Machine Learning (ML) qualifications have gained importance in the workplace market. Increasingly companies seek to hire employees with a grasp of the fundamental principles of working with (big) data and applying machine learning methods to design and train intelligent systems for deployment. This need goes beyond traditional employers of skilled information technology (IT) labor, i.e. tech companies, as more and more fields are affected by the power of artificial intelligence (AI). For example, law offices see an increasing trend of the use of AI for the efficient analysis of court documents and their efficient summary, medical professionals have been outperformed by AI in the analysis of medical images and tumour detection, telecom operators generate large quantities of data that needs to be mined and analysed, banks and supermarkets use ML to track their customers and adapt their services, etc. In fact, almost all companies can benefit from a firm grasp of machine learning principles by their employees. On the other hand, there is a persistent and worsening lack of higher-skilled labor in the tech industry at a European level, an issue that has been reflected in the Innovation Union research and innovation policy. The goal of the VALENCE project is to enhance the capabilities of students and shape them to be the future leaders in the ML industry employment market. To achieve this goal the project leverages the expertise of the higher education partners and the training experience by the vocational education partners towards advancing the penetration and coverage of machine learning in the classroom, targeting both technical high school students and adults seeking career advancement or change. At the core of the project is the design, development, and deployment of an online machine learning education platform and fine-tuned to the educational needs of the target groups. The platform will be based on free software in line with the current main European policies on open innovation, open science, and open to the world. The platform will be: 1. rich in that it will cover different aspects of ML including data mining, signal processing and recognition, and artificial intelligence in robotics, 2. customizable in that it will allow educators to set the involvement level of students, e.g. using graphical blockchains for juniors, and writing code for seniors, 3. collaborative in that it will be built around team collaboration both within and between the educational partner institutions, and 4. extensible in that by being free software, a community would be able to enhance and maintain it further. Through this, the deployment of the platform in the education process will increase the value, or valence if you will, of the target groups of students. The final impact of the VALENCE project is in the enhancement of the career perspectives of the next and future generations of highly skilled ML-enabled employees that will strengthen Europe’s perspectives as a global leader in technology.
Our primary goal was to create a platform that integrates with vocational study courses and teaches young vocational students the basics of data science, visualization, and machine learning. We aimed to bridge the gap between traditional IT knowledge and interdisciplinary knowledge needed in the modern data-driven economy. Our hope was to benefit both students and employers by providing students with the skills to be competitive in the job market and employers with a pool of qualified candidates. We wanted to create a scalable platform to be used in any vocational setting, targeting several vocational schools in Southeastern Europe. Our aim was to contribute to the development of a more skilled workforce, machine learning, and data science, and support the growth of the modern data-driven economy. Additionally, we wanted to tailor our platform to the realistic needs of the modern-day labor market, which is increasingly demanding qualified personnel, machine learning, and data science. We aimed to achieve this by taking feedback from both VET students and teachers into account and creating a platform that adapts to their needs.
We applied for this Erasmus+ project because we recognized the urgent need to bridge the gap between the current demands of the labor market and the vocational education programs available to young students. Specifically, we saw a critical need for vocational students to gain basic skills and knowledge in the areas of data science, data visualization, and machine learning, which are increasingly in demand in today's rapidly evolving economy and technological landscape. We recognized that many young professionals emerging from vocational training programs are not adequately prepared to work with big and complex data, leaving them at a disadvantage in today's job market. Our project aimed to address this gap by developing a platform that integrates with vocational study courses to provide efficient and effective teaching of the basics of data science, data visualization, and machine learning. Additionally, we recognized the need to address the persistent and worsening shortage of skilled labor in the tech industry at a European level, which has been identified as an issue in the European Union research and innovation policy.