14,145 research outputs found
Reconciling Contemporary Approaches to School Attendance and School Absenteeism: Toward Promotion and Nimble Response, Global Policy Review and Implementation, and Future Adaptability (Part 1)
School attendance is an important foundational competency for children and adolescents, and school absenteeism has been linked to myriad short- and long-term negative consequences, even into adulthood. Many efforts have been made to conceptualize and address this population across various categories and dimensions of functioning and across multiple disciplines, resulting in both a rich literature base and a splintered view regarding this population. This article (Part 1 of 2) reviews and critiques key categorical and dimensional approaches to conceptualizing school attendance and school absenteeism, with an eye toward reconciling these approaches (Part 2 of 2) to develop a roadmap for preventative and intervention strategies, early warning systems and nimble response, global policy review, dissemination and implementation, and adaptations to future changes in education and technology. This article sets the stage for a discussion of a multidimensional, multi-tiered system of supports pyramid model as a heuristic framework for conceptualizing the manifold aspects of school attendance and school absenteeism
A Combined Representation Learning Approach for Better Job and Skill Recommendation
Job recommendation is an important task for the modern recruitment industry. An excellent job recommender system not only enables to recommend a higher paying job which is maximally aligned with the skill-set of the current job, but also suggests to acquire few additional skills which are required to assume the new position. In this work, we created three types of information net- works from the historical job data: (i) job transition network, (ii) job-skill network, and (iii) skill co-occurrence network. We provide a representation learning model which can utilize the information from all three networks to jointly learn the representation of the jobs and skills in the shared k-dimensional latent space. In our experiments, we show that by jointly learning the representation for the jobs and skills, our model provides better recommendation for both jobs and skills. Additionally, we also show some case studies which validate our claims
A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics
In today's competitive and fast-evolving business environment, it is a
critical time for organizations to rethink how to make talent-related decisions
in a quantitative manner. Indeed, the recent development of Big Data and
Artificial Intelligence (AI) techniques have revolutionized human resource
management. The availability of large-scale talent and management-related data
provides unparalleled opportunities for business leaders to comprehend
organizational behaviors and gain tangible knowledge from a data science
perspective, which in turn delivers intelligence for real-time decision-making
and effective talent management at work for their organizations. In the last
decade, talent analytics has emerged as a promising field in applied data
science for human resource management, garnering significant attention from AI
communities and inspiring numerous research efforts. To this end, we present an
up-to-date and comprehensive survey on AI technologies used for talent
analytics in the field of human resource management. Specifically, we first
provide the background knowledge of talent analytics and categorize various
pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant
research efforts, categorized based on three distinct application-driven
scenarios: talent management, organization management, and labor market
analysis. In conclusion, we summarize the open challenges and potential
prospects for future research directions in the domain of AI-driven talent
analytics.Comment: 30 pages, 15 figure
e-Skills: The International dimension and the Impact of Globalisation - Final Report 2014
In todayâs increasingly knowledge-based economies, new information and communication technologies are a key engine for growth fuelled by the innovative ideas of highly - skilled workers. However, obtaining adequate quantities of employees
with the necessary e-skills is a challenge. This is a growing
international problem with many countries having an insufficient numbers of workers with the right e-Skills.
For example:
Australia: âEven though thereâs 10,000 jobs a year created in IT, there are only 4500 students studying IT at university, and not all of them graduateâ (Talevski and Osman, 2013).
Brazil: âBrazilâs ICT sector requires about 78,000 [new] people by 2014. But, according to Brasscom, there are only 33,000 youths studying ICT related courses in the countryâ (Ammachchi, 2012).
Canada: âIt is widely acknowledged that it is becoming inc
reasingly difficult to recruit for a variety of critical ICT occupations
âfrom entry level to seasonedâ (Ticoll and Nordicity, 2012).
Europe: It is estimated that there will be an e-skills gap within Europe of up to 900,000 (main forecast scenario) ICT pr
actitioners by 2020â (Empirica, 2014).
Japan: It is reported that 80% of IT and user companies report an e-skills shortage (IPA, IT HR White Paper, 2013)
United States: âUnlike the fiscal cliff where we are still peering over the edge, we careened over the âIT Skills Cliffâ some years ago as our economy digitalized, mobilized and further âtechnologizedâ, and our IT skilled labour supply failed to keep upâ (Miano, 2013)
Career Path Prediction using Resume Representation Learning and Skill-based Matching
The impact of person-job fit on job satisfaction and performance is widely
acknowledged, which highlights the importance of providing workers with next
steps at the right time in their career. This task of predicting the next step
in a career is known as career path prediction, and has diverse applications
such as turnover prevention and internal job mobility. Existing methods to
career path prediction rely on large amounts of private career history data to
model the interactions between job titles and companies. We propose leveraging
the unexplored textual descriptions that are part of work experience sections
in resumes. We introduce a structured dataset of 2,164 anonymized career
histories, annotated with ESCO occupation labels. Based on this dataset, we
present a novel representation learning approach, CareerBERT, specifically
designed for work history data. We develop a skill-based model and a text-based
model for career path prediction, which achieve 35.24% and 39.61% recall@10
respectively on our dataset. Finally, we show that both approaches are
complementary as a hybrid approach achieves the strongest result with 43.01%
[email protected]: Accepted to the 3nd Workshop on Recommender Systems for Human
Resources (RecSys in HR 2023) as part of RecSys 202
Big data for monitoring educational systems
This report considers âhow advances in big data are likely to transform the context and methodology of monitoring educational systems within a long-term perspective (10-30 years) and impact the evidence based policy development in the sectorâ, big data are âlarge amounts of different types of data produced with high velocity from a high number of various types of sources.â Five independent experts were commissioned by Ecorys, responding to themes of: students' privacy, educational equity and efficiency, student tracking, assessment and skills. The experts were asked to consider the âmacro perspective on governance on educational systems at all levels from primary, secondary education and tertiary â the latter covering all aspects of tertiary from further, to higher, and to VETâ, prioritising primary and secondary levels of education
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