Using Machine Learning for Labour Market Intelligence

PurposeApprenticeships in England are currently experiencing a revival. The purpose of this paper is to provide a general overview of apprenticeships in England, examine current government policy, and explore current issues around the further development of apprenticeships.Design/methodology/approachDesk research, including reviewing other research articles and labour market intelligence has been carried out to provide a general overview of the issues.FindingsApprenticeships have traditionally been regarded as the vocational route to stable employment. Although they have sometimes suffered from a poor reputation they are now becoming an increasingly popular option for both younger and older people. The knowledge economy is driving up the demand for higher level skills and concurrent with this is the notion that, in today's competitive labour market, experience is vital. Not only has this impacted on the popularity of apprenticeships but also upon more traditional “academic” routes such as higher education (HE). In addition it has raised questions about higher level skills and vocational education. The introduction of Higher Apprenticeships and work experience/real world interactions built into HE courses are establishing synergies between the two elements of the skills/education system; however, developing these synergies further is a critical issue for future consideration.Originality/valueThere is a proliferation of publications tracking the nature and value of apprenticeships. This paper traces apprenticeships and their evolution and examines how practices adopted can be applied to newer vocational options being integrated into HE. The paper considers apprenticeships and other vocational options, building on the author's own discussions with employers and recent graduates.

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Learning about Careers: Open data and Labour Market Intelligence

RIED Revista Iberoamericana de Educación a Distancia ◽

10.5944/ried.22.1.22289 ◽

2019 ◽

Vol 22 (1) ◽

pp. 81

Author(s):

Graham Attwell ◽

Deirdre Hughes

Keyword(s):

Labour Market ◽

Open Data ◽

Market Intelligence ◽

Toma De Decisiones ◽

Sistemas De Informacion ◽

Datos Abiertos ◽

Estudio De Caso

Las decisiones sobre el aprendizaje y el trabajo deben ubicarse en un contexto particular espacial, de mercado laboral: los individuos toman decisiones dentro de "estructuras de oportunidad" particulares y sus decisiones y aspiraciones se enmarcan dentro de su comprensión de tales estructuras. Este artículo examina formas en las que se puede aplicar el aprendizaje sobre carreras con datos abiertos e inteligencia del mercado laboral. Un estudio de caso ilustrativo del proyecto LMI for All en el Reino Unido muestra la viabilidad técnica del diseño y desarrollo de dichos sistemas y un modelo para su difusión e impacto. La tendencia hacia los datos abiertos y las aplicaciones cada vez más poderosas para procesar y consultar datos han cobrado impulso. Esto, combinado con la necesidad de información sobre el mercado laboral para la toma de decisiones en mercados laborales cada vez más inestables, ha llevado al desarrollo y pilotaje de nuevos sistemas de información del mercado laboral (LMI), que involucran a múltiples grupos de usuarios. Existen desafíos universales debido al uso cada vez mayor de LMI, especialmente en la asignación de empleos y el uso en rápida expansión de datos abiertos en diferentes entornos de educación y empleo. Destacamos seis temas emergentes que deben abordarse para que los datos abiertos y la inteligencia del mercado laboral puedan aplicarse de manera efectiva en diferentes contextos y entornos. Concluimos reflexionando sobre la urgente necesidad de ampliar el cuerpo de investigación y desarrollar nuevos métodos de co-construcción en asociaciones de colaboración innovadoras.

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The impact of optical character recognition artificial intelligence on the labour market

International Journal of Engineering and Management Sciences ◽

10.21791/ijems.2021.4.2. ◽

2021 ◽

Vol 6 (4) ◽

Author(s):

Zsófia Riczu ◽

Zsolt Krutilla

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Information Technology ◽

Labour Market ◽

Character Recognition ◽

Optical Character Recognition ◽

Employment Policy ◽

Labour Law ◽

Employee Competencies ◽

The Impact

Because of present day information technology, there is neither need to plant complicated computers for more millions price if we would like to process and store big amounts of data, nor modelling them. The microprocessors and CPUs produced nowadays by that kind of technology and calculating capacity could not have been imagined 10 years before. We can store, process and display more and more data. In addition to this level of data processing capacity, programs and applications using machine learning are also gaining ground. During machine learning, biologically inspired simulations are performed by using artificial neural networks to able to solve any kind of problems that can be solved by computers. The development of information technology is causing rapid and radical changes in technology, which require not only the digital adaptation of users, but also the adaptation of certain employment policy and labour market solutions. Artificial intelligence can fundamentally question individual labour law relations: in addition to reducing the living workforce, it forces new employee competencies. This is also indicated by the Supiot report published in 1998, the basic assumption of which was that the social and economic regulatory model on which labour law is based is in crisis.

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Machine Learning Methods of Providing Informational Management Support for Students’ Professional Development

The Education and science journal ◽

10.17853/1994-5639-2018-9-91-114 ◽

2018 ◽

Vol 20 (9) ◽

pp. 91-114 ◽

Cited By ~ 2

Author(s):

I. G. Zakharova

Keyword(s):

Professional Development ◽

Machine Learning ◽

Labour Market ◽

Learning Process ◽

Professional Community ◽

Information Support ◽

University Administrators ◽

Machine Learning Methods ◽

General Scientific ◽

Development Of Students

Introduction.Professional development of students requires effective interaction with teachers, scientists, university administrators, students, representatives of professional community and labour market. The effectiveness of this interaction resulted from its information support, based on reliable information, promptly provided to all the members of learning process.The aim of this paper was to study the machine learning methods potential for the effective management of learning process by the example of implementing information support component designed to diagnose and predict the professional development of students based on automatic text analysis.Methodology and research methods. The theoretical basis of the research involved modelling of students’ professional development using the analysis of textual informative and professional relevance in written works of students. To identify the characteristics of professional development, a computer cluster analysis of texts was carried out using the K-means method of clustering. The Bayes method was used to construct a model for classifying students from the standpoint of identified features.Results and scientific novelty. A computer analysis of texts relating to different stages of learning for the evaluation of general and special vocabulary was performed. Regularities in the dynamics of students’ use of general scientific and professional terminology were revealed. Accordingly, the groups with certain trends of educational behaviour of students were formed. It was shown how this differentiation, based on the complex of previously selected dynamic indicators characterising the changes of professional vocabulary, expands the possibilities for diagnostics and forecasting of professional growth of students. The author notes that the efficiency of similar intellectual systems is determined not only by the continued database up-dating, i.e. the amount of data in turn influence the accuracy of model of students’ classification and, consequently, the forecast of students’ professional development. Equally important is the improvement of knowledge base, which contains the criteria of professional development and complies with the requirement of basic dictionaries relevance. In addition, supportive procedures should be carried out with participating of the representatives of professional community.Practical significance. The information support provided for the management of professional development of students can be used both for operational decision making and developing content and technologies for educational process. This means students can evaluate the dynamics of own performance in comparison with earlier works, classmates’ work, target indicators of the use of general scientific and professional terminology. This information management component allows teachers to monitor the content of texts and easily determine the authorship of content of learner’s general frequency vocabulary and the dynamics of its change. The representatives of labour market along with access to information on the current progress of a student can define his or her prospects as a future worker. Heads of educational programmes, university administrators receive objective information about the content of disciplines as their study is reflected in the students’ professional development.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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