The Australian Minister for Skills and Training, Brendan O’Connor, has outlined his plans to improve completion rates for Vocational Education and Training (VET) as part of the upcoming National Skills Agreement. O’Connor, alongside state and territory ministers, will look to implement various solutions to help address the low completion rate, which has remained around 50% for nearly a decade.
Improving VET Completion Rates Through The National Skills Agreement
During a recent speech in South Australia, O’Connor noted that the government would seek input from stakeholders on how best to improve completion rates. The minister stressed the need to invest in areas of existing and emerging demand, which could help people see a clear connection between their training and the job market. He also emphasised the importance of greater collaboration between industry and training providers, such as TAFEs, to increase the likelihood of finishing an apprenticeship.
Given the ongoing skills shortage in Australia, improving VET completion rates has become a national priority. It is hoped that the upcoming National Skills Agreement will provide a framework for addressing this issue and that the various proposed solutions will help create a more robust VET system. As the government continues to work towards its goal of a more skilled workforce, it is clear that improving VET completion rates will be a critical step in achieving this aim.
NCVER Research: Evaluating Machine Learning for Projecting Completion Rates for VET Programs
NCVER recently released the report titled “Evaluating machine learning for projecting completion rates for VET programs”, which explores the potential of using machine learning algorithms to predict completion rates of vocational education and training (VET) programs in Australia. The study evaluates the performance of four different machine learning models, comparing them to a traditional statistical model, and finds that the machine learning models outperformed the traditional model in predicting completion rates. The report concludes that machine learning algorithms can provide more accurate and timely completion rate projections for VET programs, which can help institutions make informed decisions about program design and resource allocation.
