COVID-19 has displaced labor in mass and made people face the challenge of moving between jobs to find new work. Problems in the transitions might occur when workers fail to move smoothly to a new position using their existing skills.
Researchers from the University of Technology Sydney (UTS) and UNSW Sydney have found a solution to this problem by using a machine learning-based method that helps people identify jobs based on the skills they have or acquired during their current occupation.
"By focusing on skill sets, rather than occupations, this new approach helps workers, organizations, and businesses like retraining advisory services discover the new skills a person would need to acquire to obtain a new in-demand job and assess the associated training investment required. In addition, organizations can use our skill similarity measure to design completely new or hybrid occupations that increase the likelihood of finding people with the necessary skill set," says Professor Mary-Anne Williams, the Michael J Crouch Chair in Innovation at UNSW Business School.
After all these processes, the recommender system was able to predict occupational transitions with a 76% accuracy and suggest whether moving in one direction is easier.
While emerging technologies like machine learning seem to disrupt labor markets, Dr. Dawson from the UTS Data Science Institute has a different opinion. He says, "If you look back in history, it's almost never the case that there are fewer jobs due to automation, but rather new jobs are created at the same time old ones disappear."
"So, it is fundamental that people have the ability to build the requisite skills and transition smoothly into these new jobs. The ability to undertake micro-credentials in specific skill areas, customized for the individual, will likely be a key part of this future."
Further insights into the technology can be found from the peer-reviewed publication of this study entitled Skill-driven Recommendations for Job Transition Pathways in the international journal PLOS ONE.