White Paper

The overall study has two corresponding phases of investigation. Phase 1, which is the subject of this report, represents the work that EPRI undertook to further develop the predictive model of student retention at Mohawk College; to test the predictive model and the predictions it generates; to use this model to generate predictions of the probability that any given incoming student would leave Mohawk early; to use these predictions to divide students into three different risk groups (high, medium, low); to analyze the distribution of students across these different risk groups according to a range of individual, program and other characteristics; and to then calculate participation rates in existing student advising programs across the three risk groups. Phase 2 (and the second report) will consist of an analysis of the specific advising interventions offered to students of the 2015 entry cohort, including how the effects of these interventions vary across student risk groups as identified by the predictive model. In addition, this paper not only goes through the development and application of the predictive model and related analysis, but also provides a primer on the use of predictive modelling within the context of predictive analytics and student retention. The motivation for such a primer comes from the wide use of the term “predictive modelling” as pertaining to student retention, and the potential usefulness of identifying the relevant concepts.

This study aims to know the skills needed for the future development and growth of Québec's financial sector. Aimed at educational institutions and financial organizations in the world, it aims to equip them to ensure the adequacy between these skills and training provision. To do this, we analyzed from a qualitative perspective transformations to come and identified future skills needed in the main business of the three major sub-sectors (banking, insurance, asset management) finance. We sought to understand how these trades are doomed to change and how these changes will be different depending on the role and function of business within organizations. [googletranslate_en]

Since 2007 (prior to the economic downturn of 2008/2009) the overall labour force participation of Canadians declined by about two percentage points. The first part of the study investigates the extent to which aging affected changes in labour market participation rates since 2007, based on data from the Labour Force Survey (LFS). In the second part, the reasons behind the increase in the participation rates of Canadians aged 55 and over, which have been trending upwards since 1996, are explored. In 2016, individuals aged 55 and over accounted for 36% of the working-age population, the highest proportion on record (since the compilation of comparable statistics in 1976). By 2026, that proportion could reach 40%. Because labour market participation starts to decline after age 55, population aging was the main factor behind the decline in the overall participation rate in the years following the recession. At the same time, the labour market participation of older Canadians increased. From 1996 to 2016, the labour force participation rate of individuals aged 55 and over increased from 24% to 38%, reaching a record high in 2016. The participation rate of individuals aged 55 and over increased for all levels of education. For instance, the participation rate of those with a high school diploma or less increased from 19% in 1996 to 29% in 2016. Changes in age, family structure and educational factors explained 44% of the increase in the labour market participation of older Canadians from 1996 to 2016.

This Evidence Report focuses on how training activity in the UK has fared in the 2008-09 recession and its aftermath. Using a combination of statistical analysis of large-scale surveys and in-depth telephone interviews with employers, some answers are provided. The Report’s substantive results are presented in four parts, which draw on data collected using a mixture of quantitative and qualitative methods: analysis of employer surveys and, in particular, data collected as part of the National Employer Skills Survey (NESS) 2009, a survey involving over 79,000 employers who were asked directly about the impact of the recession on training; analysis of individual-level data as collected by the Quarterly Labour Force Survey (QLFS) over the period 1995-2012, with the contemporary situation compared with the recession of the early 1990s, where data allow; in-depth telephone interviews with 60 private sector employers who participated in the 2009 NESS, most of whom we interviewed on two separate occasions (in mid-2010 and then late 2011), as a means of tracking the changing impact of the economic downturn; in-depth telephone interviews with 45 public sector employers, many of whom were interviewed twice in order to reveal what impact the deficit reduction programme was having on training as budgets were being squeezed.

This paper presents a multidisciplinary task approach for assessing the impact of artificial intelligence on the future of work. We provide definitions of a task from two main perspectives: socio-economic and computational. We propose to explore ways in which we can integrate or map these perspectives and link them with the skills or capabilities required by them, for humans and AI systems. Finally, we argue that in order to understand the dynamics of tasks, we have to explore the relevance of autonomy and generality of AI systems for the automation or alteration of the workplace.

To enable the Advanced Manufacturing sector to realise its potential, it is vital to understand how technological advances impact upon skills needs. This research aims to go beyond high-level skills data that is currently available and focuses on skills requirements of three technological areas within the aerospace and automotive sectors: Additive Manufacturing, Composites and Plastic Electronics. Addressing these skills challenges will help the sector better respond to performance challenges and opportunities. Additive Manufacturing (AM) is a term used to describe the manufacture of products using digitally controlled machine tools and is often termed 3D printing. The approach differs from traditional manufacturing in that all Additive Manufacturing processes use a layer-by-layer approach to build up components rather than through machining from solid, moulding or casting (Materials Knowledge Transfer Network, 2012). Composites have been defined as consisting of a bulk material (the ‘matrix’) and a reinforcement of some kind such as fibres, particles or flakes, usually added to increase strength and stiffness. This report focuses on what are termed advanced composites (Structural Fibre-reinforced Polymer Matrix Composites) used in automotive and aerospace applications and characterised as light weight higher performance materials. Plastic Electronics has been defined as devices on flexible surfaces that make it possible to produce flexible, bendable or stretchable electronic products, which may use printing techniques, but can also be deposited onto flexible surfaces in other ways. Plastic Electronics can also refer to the use of printing techniques in relation to devices on rigid surfaces.

With the arrival of new and powerful technologies and the declining costs of these technologies, some new possibilities are emerging for cities and their transport systems. For example, the Internet of Everything (IoE)1 can benefit cities by connecting people, processes, data and things as everything comes online. This is creating unprecedented opportunities for organizations, individuals, communities and countries to realize dramatically greater value from networked connections—including economic growth and improvements to environmental sustainability, public safety, the delivery of public services and productivity. The potential here is for cities to become truly transformed.

This White Paper on what we have called “Work¥4.0” is the culmination of a consultation process launched in April 2015 with the publication of a Green Paper by the Federal Ministry of Labour and Social Affairs. “Work¥4.0” is a necessary extension of the debate about the digitalisation of the economy, generally referred to as “Industry¥4.0”. The Federal Ministry of Labour and Social Affairs invited associations, trade unions and businesses to submit responses, held numerous specialised workshops and events, commissioned academic studies, and formed an impression of public opinion based on a direct dialogue with the public at local level – for example during the “Futurale” film festival. The overarching question was: how can we preserve or even strengthen our vision of quality jobs and decent work (Gute Arbeit) in an era of digital transformation and societal change?

Accelerated courses continue to be part of the changing academic landscape at Canadian universities. However, there is limited evidence to support their efficacy in relation to knowledge retention. A greater understanding of knowledge retention associated with accelerated courses (i.e., intensive full-day course for a one- or two-week duration) as compared to traditional courses (i.e., one three-hour lecture once a week for 12 weeks) will provide university stakeholders and administrators with evidence to determine whether quicker courses should be pursued in the postsecondary education environment. A longitudinal cohort design was developed with the objective of addressing the following research question: Does a difference in knowledge retention over time exist between students enrolled in traditional versus accelerated undergraduate courses?