Notes taken at the webinar hosted by NCVER on 'skills anticipation' and presented by Phil Loveder, the Executive Manager for Inform and Engage.
As customary, began with an acknowledgement of country. The present provides an overview of research into new qualifications and competencies - part of the Bridging Innovation and Learning in TVET with UNESCO. see www.unevoc.org/bilt/The+BILT+Approach for information on the wider project.
Reiterated VET's role and the need to respond - as it is closest to the labour market, playing a key role in provision of skilled labour and the need to respond to current and future economic challenges.
Changing economy drives NQCs - accelerating digital technologies, new demands in sustainability environmental protection and increased processes with migration.
BILT framework focuses on Identification, Integration, and Implementation. Also on Macro (governances), meso (advocacy - stakeholders) and micro (delivery, innovative solutions etc.) levels. (the 3 Is and 3Ms).
Identification involves understanding labour supply and demand - for current and future skills in the labour market, has to occur across all the 3Ms. Range of approaches may be used to understand skills from prospective studies to shareholder consultation and analysis of skills and carerrs information. Skills anticipation and labour market information 'observatories'. It is a strategic and systematic process.
Important to provide insights into skills need, form the basis for new qualifications, inform updates of licencing, identify extent of new occupations and to provide policymakers and industry with evidence to assure training solutions.
Skill anticipation systems involve data collection, analysis, forecasting, recommendations and then monitoring and updating.
Examples of skill anticipation systems include skills observatories, skills and employment data modelling, stakeholder engagement and employer surveys, job vacancy analysis, leveraging jobs and careers databases etc. Skill observatories across Europe (LMIS), Australia )LMIP / LMI), Brazil (SENAI), USA (O-Net).
A range of modelling and forecasting approaches used to make sense of all the various streams of data. Shared example at macro/meso/micro level data-sources from Australia.
Example of projects include cross-sectoral projects focusing on new competencies (e.g. cybersecurity, supply chains, digital skills). Job and careers databases are useful to provide information on emerging skills needs. Consultations with employers and business membership organisations (EMBOs) important. 'Early detection' systems through monitoring of VET and occupational related trends and indicators (e.g. Germany) which help identify changes in technologies, ways of working, social trends.
Summarised the many challenges including data quality, availability, consistency, effects from unexpected 'shocks' (e.g. pandemic), better at assessing current rather than future, and challenges in translating analysis into policy and practice.
Detailed the need for integration of 4 approaches - cross cutting (e.g. Singapore Skills framework), sectoral (Basque TKNIKA), occupational (Finlnd and other European countries) and additional modular (Netherlands, Finland).
Implementation includes establishing frame conditions and buy-in by establishing confidence in NQCs by industry, learners and the community. Practitioners also require support to implement (toolboxes, processes etc.). Acceptance and assurance and clear indications of structural support are also important.
Final thoughts include ensuring VET is 'future ready and this requires continual evaluation of labour market data. Organising and dissemination of information and data to assist training and curriculum renewal is essential. Supportive environment for NQC requires resourcing and support.
Q & A followed.
Good summary of the process for establishing a data-based process for working out key competencies going forward.
No comments:
Post a Comment