Mathematical model for constructing an idustry-specific career guidance decision support system

The article addresses the challenges of developing an industry-specific decision support system for education and career guidance in engineering professions under conditions of limited data availability. The system aims to facilitate informed career choices by assessing students’ aptitudes for engineering and technical fields. To formalize these aptitudes, the authors propose a set of key factors and evaluation metrics that enable data-driven conclusions using information extracted from digital educational environments. These factors are designed to leverage immersive technologies and digital educational tools for data acquisition. The study introduces a generalized mathematical model that quantifies the manifestation of multiple parameters and aligns them with potential professional trajectories. The model incorporates weighted indices and significance assessments for predictive analytics, along with methods to integrate diverse evaluation approaches into the decision support framework. Parameters include psychological diagnostics and academic performance metrics. Additionally, the paper demonstrates the application of the generalized model to the mining industry, validated through empirical testing involving a control group of industry professionals. The results highlight the model’s adaptability to sector-specific requirements and its capacity to enhance objectivity in career aptitude assessment. This research contributes to the development of scalable, data-informed tools for engineering career guidance, emphasizing the integration of emerging technologies into educational ecosystems.

1. Makarova M.Yu. Printsipy funktsionirovaniya informatsionnykh sistem podderzhki proforientatsii. Algoritmy, metody i sistemy obrabotki dannykh. 2012;(2):44–51. (In Russ.).

2. Tarasova Yu.S., Chechin A.V., Andreev V.V. Automatization of vocational guidance testing using the software package ColourUnique Pro*. In: CPT2019 The International Scientific Conference of The Nizhny Novgorod State University of Architecture and Civil Engineering and of The Scientific and Research Center for Information in Physics and Technique: CPT2019 The Conference Proceedings, 13–17 May 2019, TzarGrad, Russia. TzarGrad: Nizhny Novgorod State University of Architecture and Civil Engineering, Scientific and Research Center for Information in Physics and Technique; 2019. pp. 231–236. (In Russ.).

3. Balogun V.F., Thompson A.F. Career Master: A Decision Support System (DSS) for Guidance and Counseling in Nigeria. The Pacific Journal of Science and Technology. 2009;10(2):337–354.

4. Mundra A., Soni A., Sharma S.K., Kumar P., Chauhan D.S. Decision Support System for Determining: Right Education Career Choice. In: Computer Networks and Security: International Conference on Communication and Computing (ICC-2014), 12–14 June 2014, Bangalore, India. 2014. pp. 8–17.

5. Holly M., Weichselbraun C., Wohlmuth F., Glawogger F., Seiser M., Einwallner P., Pirker J. VRChances: An Immersive Virtual Reality Experience to Support Teenagers in Their Career Decisions. Multimodal Technologies and Interaction. 2024;8(9). https://doi.org/10.3390/mti8090078

6. Simons A., Wohlgenannt I., Zelt S., Weinmann M., Schneider J., Vom Brocke Ja. Intelligence at play: Game-based assessment using a virtual-reality application. Virtual Reality. 2023;27(3):1827–1843. https://doi.org/10.1007/s10055-023-00752-9

7. Slavova Yo., Mu M. A Comparative Study of the Learning Outcomes and Experience of VR in Education. In: 2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), 18–22 March 2018, Tuebingen/Reutlingen, Germany. IEEE; 2018. pp. 685–686. https://doi.org/10.1109/VR.2018.8446486

8. Soliman M., Pesyridis A., Dalaymani-Zad D., Gronfula M., Kourmpetis M. The Application of Virtual Reality in Engineering Education. Applied Sciences. 2021;11(6). https://doi.org/10.3390/app11062879

9. Tanaka E.H., De Almeida L., De Freitas Gouveia G.S., Clerici R.P.S., Alves A.H.F., De Oliveira R.R. A collaborative, immersive, virtual reality environment for training electricians. Journal on Interactive Systems. 2023;14(1):59–71. https://doi.org/10.5753/jis.2023.2685

10. Zhao J., Lin L., Sun J., Liao Yu. Using the Summarizing Strategy to Engage Learners: Empirical Evidence in an Immersive Virtual Reality Environment. The Asia-Pacific Education Researcher. 2020;29(5):473–482. https://doi.org/10.1007/s40299-020-00499-w