Training and education of specialists in the field of labour law

Oleksii Kucher, Leonid Mohilevskyi

Article ID: 5966
Vol 8, Issue 6, 2024

VIEWS - 271 (Abstract) 155 (PDF)

Abstract


Objective/Aim: In the context of a constantly changing legislative environment and the necessity for professionals to develop their skills, the research focuses on identifying effective methods and tools that facilitate efficient learning and professional development in the field of labour law. This study aimed to propose a pedagogical technology for the preparation and training of specialists in the field of labour law and to assess the effectiveness of the training based on the specified technology. Method: The study involved 124 participants, with 63 in the experimental group and 61 in the control group. Statistical analysis was performed using Microsoft Excel. The student’s t-test indicated significant improvements in the experimental group’s training effectiveness, confirming the proposed pedagogical technology’s efficacy. Results: Consequently, implementing training and education technology for specialists in the labour law field was proposed to enhance the indicators. The criteria for the preparation of specialists in the field of labour law were delineated, including knowledge of labour legislation, consulting and support skills, analytical skills, communication skills, and continuous learning. According to the criteria above, levels of preparation for specialists in the field of labour law were established, namely high, medium, and essential. The proposed training and education technology for specialists in the field of labour encompasses the following tools: The utilisation of online platforms and educational resources, virtual classes and simulations, the incorporation of multimedia materials, the integration of adaptive learning technologies, the implementation of project- and problem-oriented teaching methodologies, the incorporation of interactive methodologies, the incorporation of cloud technologies and mobile applications, and the provision of assessment and feedback. Conclusion: The proposed pedagogical technology effectively enhances the training and education of labour law specialists. The experimental group’s significant improvement in learning outcomes confirms the technology’s efficacy. Implication: The findings of this research hold significant social implications. Improved training and education of labour law specialists leads to a more competent and effective legal workforce. This, in turn, ensures better protection of workers’ rights and fairer employer-employee relations, contributing to overall social stability.


Keywords


labour law; staff training; professional development; practical skills; specialised training programmes; new trends in labour law; current issues and challenges in labour law

Full Text:

PDF


References


Adjei, E. S., Osei, E., Edusei, A. K., et al. (2024). A systematic review of academic performance of Children with Disabilities (CWDs) in inclusive education schools in Low and Middle-Income Countries (LMICs). Heliyon, 10(3), e25216. https://doi.org/10.1016/j.heliyon.2024.e25216

Almalky, H. A., & Alwahbi, A. A. (2023). Teachers’ perceptions of their experience with inclusive education practices in Saudi Arabia. Research in Developmental Disabilities, 140, 104584. https://doi.org/10.1016/j.ridd.2023.104584

Batsurovska, I. (2021). Massive Open Online Courses in the System of E-learning of Masters in Electrical Engineering. 2021 IEEE International Conference on Modern Electrical and Energy Systems (MEES).

Bencheva, N., & Kostadinov, N. (2021). Using OER and teaching outside the classroom for enhancing STEM and ICT education. 2021 30th Annual Conference of the European Association for Education in Electrical and Information Engineering (EAEEIE). https://doi.org/10.1109/eaeeie50507.2021.9530862

Blau, I., Shamir-Inbal, T., & Avdiel, O. (2020). How does the pedagogical design of a technology-enhanced collaborative academic course promote digital literacies, self-regulation, and perceived learning of students? The Internet and Higher Education, 45, 100722. https://doi.org/10.1016/j.iheduc.2019.100722

Bock, D.E., Velleman, P.F., De Veaux R.D. (2007). Stats, Modeling the World. Pearson Addison Wesley.

Boesl, D. B. O., Achtenberg, T., & Bergler, L. (2023). Foundations of an AI-based, cross-plattform companion app for lifelong learning optimization. In: Proceedings of the 2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE).

Boyle, C., Barrell, C., Allen, K.-A., et al. (2023). Primary and secondary pre-service teachers’ attitudes towards inclusive education. Heliyon, 9(11), e22328. https://doi.org/10.1016/j.heliyon.2023.e22328

Bursten, J. R. (2020). Computer simulations. In: Between Making and Knowing. World Scientific Publishing Co.

Caratozzolo, P., Sirkis, G., Piloto, C., et al. (2020). Skills Obsolescence and Education Global Risks in the Fourth Industrial Revolution. In: Proceedings of the 2020 IFEES World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC).

Cui, D. (2022). Research on the Practical Education Path of Labor Concept Education Based on Big Data Technology. In: Proceedings of the 2022 3rd International Conference on Education, Knowledge and Information Management (ICEKIM).

Dotsenko, N. (2023). Interactive Posters as a Learning Tool for Practical Tasks in the Context of Electrical Engineering Education. In: Proceedings of the 2023 IEEE 5th International Conference on Modern Electrical and Energy System (MEES).

Espina-Romero, L. C., Guerrero-Alcedo, J. M., & Ossio, C. (2023). 7 topics that business ecosystems navigate: Assessment of scientific activity and future research agenda. Heliyon, 9(6), e16667. https://doi.org/10.1016/j.heliyon.2023.e16667

Jiang, D., Dahl, B., Chen, J., et al. (2023). Engineering Students’ Perception of Learner Agency Development in an Intercultural PBL (Problem- and Project-Based) Team Setting. IEEE Transactions on Education, 66(6), 591–601. https://doi.org/10.1109/te.2023.3273177

Delima, P. M., & Dachyar, M. (2020). Advancing the E-Tendering Information System to Counter Corruption by Proposing Anti-Corruption SMART Tools. In: Proceedings of the 2020 3rd International Conference on Applied Engineering (ICAE).

Landberg, M., & Partsch, M. V. (2023). Perceptions on and attitudes towards lifelong learning in the educational system. Social Sciences & Humanities Open, 8(1), 100534. https://doi.org/10.1016/j.ssaho.2023.100534

Li, H., Majumdar, R., Chen, M.-R. A., et al. (2021). Goal-oriented active learning (GOAL) system to promote reading engagement, self-directed learning behavior, and motivation in extensive reading. Computers & Education, 171, 104239. https://doi.org/10.1016/j.compedu.2021.104239

Maaß, S., Wortelker, J., & Rott, A. (2024). Evaluating the regulation of social media: An empirical study of the German NetzDG and Facebook. Telecommunications Policy, 48(5), 102719. https://doi.org/10.1016/j.telpol.2024.102719

Mebert, L., Barnes, R., Dalley, J., et al. (2020). Fostering student engagement through a real-world, collaborative project across disciplines and institutions. Higher Education Pedagogies, 5(1), 30–51. https://doi.org/10.1080/23752696.2020.1750306

Odilla, F. (2023). Bots against corruption: Exploring the benefits and limitations of AI-based anti-corruption technology. Crime, Law and Social Change, 80(4), 353–396. https://doi.org/10.1007/s10611-023-10091-0

Parker, S. C. (2023). Democracy, corruption, and endogenous entrepreneurship policy. Public Choice, 198(3–4), 361–376. https://doi.org/10.1007/s11127-023-01133-1

Piterska, V., Shakhov, A., Lohinov, O., et al. (2020). The Method of Human Resources Management of Educational Projects of Institution of Higher Education. In: Proceedings of the 2020 IEEE 15th International Conference on Computer Sciences and Information Technologies (CSIT).

Post, L. S., Guo, P., Saab, N., et al. (2019). Effects of remote labs on cognitive, behavioral, and affective learning outcomes in higher education. Computers & Education, 140, 103596. https://doi.org/10.1016/j.compedu.2019.103596

Quintero, R., Pertuz, L., Mosalvo, J., et al. (2024). Analysis of Self-efficacy and Attitude-mediated Inclusivity in Higher Education: A Case Study on the Colombian North Coast. Procedia Computer Science, 231, 539–544. https://doi.org/10.1016/j.procs.2023.12.247

Sosnickaya, N., & Kryvylova, O. (2020). Formation of Social Skills as a Step Towards Competitiveness in the Labor Market of Specialists of Energy Profile. In: Proceedings of the 2020 IEEE Problems of Automated Electrodrive.

Venegas, M. D., Brooks, J. M., Myers, A. L., et al. (2022). Peer Support Specialists and Service Users’ Perspectives on Privacy, Confidentiality, and Security of Digital Mental Health. IEEE Pervasive Computing, 21(2), 41–50. https://doi.org/10.1109/mprv.2022.3141986

Youssef, B. E., & Youssef, A. E. (2019). Mathematical modeling combined with machine learning for social networks to match children with learning disabilities and specialists. In: Proceedings of the 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON).

Zhang, J., & Zhu, S. (2021). Research on the Transfer of Rural Labor Force under the Construction of Intelligent Society. In: Proceedings of the 2021 International Conference on Public Management and Intelligent Society (PMIS).




DOI: https://doi.org/10.24294/jipd.v8i6.5966

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Oleksii Kucher, Leonid Mohilevskyi

License URL: https://creativecommons.org/licenses/by/4.0/

This site is licensed under a Creative Commons Attribution 4.0 International License.