Journal of Intelligent Management
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Journal of Intelligent Management
Edited By: Editorial Office | Online ISSN: 3080-2350 | Print ISSN: 3008-1742
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Latest IssueVolume 2, Issue 1June 2026

Volume 1 | Issue 1 | Published 2025-05-15

A study on the construction of digital portraits of human resource management students

DOI: https://doi.org/10.64025/j.lmjim.25.119030

Received: 2025-04-04 | Revised: 2025-04-16 | Accepted: 2025-04-19 | Published: 2025-05-15

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Abstract

With the deepening of digital transformation, the field of human resource management (HR)is experiencing unprecedented changes. In the face of this trend, how to cultivate HR talents who can adapt to the needs of the future workplace has become an urgent problem. The purpose of this paper is to explore the construction of digital portraits of advanced analysis technology, deepening the application scenarios, and proposing innovative breakthrough directions. This study not only provides methodological support for HR talent cultivation but also provides practical reference for education HR management students, through the integration of a multi-dimensional theoretical foundation, designing innovative data dimensions and collection methods, using digital transformation

Highlight

* Proposes a closed-loop process for the construction of digital portraits for human resource management major students, "data collection-index modeling-algorithm analysis-visualization presentation-education application". * Combined with educational data mining and the job competency of human resource management major students it forms a student's comprehensive quality portrait that can be quantified and evaluated. * Constructs a student portrait system of "5 major dimensions and 15 indicators".

Keywords

Human Resource Management, Digital Portrait, Data Analysis, Talent Development

Authors & Affiliations

Citation

Yan, Y., Liu, H., & Li, X. (2025). A study on the construction of digital portraits of human resource management students. Journal of Intelligent Management, 1(1), 19-30. https://doi.org/10.64025/j.lmjim.25.119030

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Full Content

1 Introduction

In the era of digitalization, data has become a key element driving the development of all industries. The education field is no exception, especially in the face of human resource management, a discipline that is highly dependent on data and information for decision-making, how to effectively use data to optimize the process of talent cultivation has become a common issue for educators and researchers. (Huan Lv, 2024) As one of the important applications of big data technology in the field of education, digital portraits can comprehensively and accurately depict individual characteristics through multi-dimensional data analysis, providing powerful support for personalized teaching and talent training.

2 Background and significance of the study

2.1 Change in HR talent demand in the context of digital transformation

The impact and reshaping of artificial intelligence and big data on traditional HR positions: with the rapid development of artificial intelligence and big data technology, traditional HR positions are experiencing profound changes (Kolb & Kolb, 2005). Automated recruitment processes, intelligent talent matching systems, employee behavior analysis, and other emerging applications continue to emerge, greatly improving the efficiency and accuracy of HR work. However, this also puts forward new requirements for HR talents, that is, not only do they need to master solid professional knowledge, but also need to have data analysis, technology application, and other cross-field capabilities.

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2.2 Innovations in student assessment in the era of Education 4.0

Analysis of the limitations of the traditional academic evaluation system: The traditional academic evaluation system mainly relies on quantitative indicators such as test scores and coursework, ignoring the individual differences and overall development of students (Wu et al., 2024). This evaluation system is often difficult to fully reflect the real ability and potential of students limiting the implementation of personalized teaching.

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2.3 Multidimensional representation of the value of research

For Institutions, they can carry out precise talent training program design, through the construction of student digital portraits, institutions can more accurately grasp the needs and characteristics of students, and design targeted training programs for different student groups (Li, 2024). This helps to improve the quality and efficiency of talent training and promote the sustainable development of institutions.

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6 Conclusions

The highlights of this article are as follows: First, it proposes a closed-loop process for the construction of digital portraits for human resource management major students, "data collection-index modeling-algorithm analysis-visualization presentation-education application". Second, combined with educational data mining and the job competency of human resource management major students it forms a student's comprehensive quality portrait that can be quantified and evaluated. Third, it constructs a student portrait system of "5 major dimensions and 15 indicators".

The construction, visualization, and educational application of the student profile of HR managers is a complex and detailed process.(Gao et al., 2024) By making full use of data visualization technology and educational psychology principles, we can better understand the learning needs and characteristics of students, and provide powerful support for formulating personalized teaching plans, optimizing the allocation of teaching resources, providing career development guidance, and evaluating teaching effects. Through this system, we can realize the change from 'score evaluation" to "competency mapping", and accurately support the cultivation of human resource management talents and job matching.

Acknowledgments

This work was supported by Research on Personalized Teaching Practice Based on Learner Profile (Grant No.2024hxxy012). Research on AIGC Enabling Innovative Talent Cultivation Mode for New Media Operation (Grant No.2023cxcy348)

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

Abdurrahman, A., & Syahrullah, M. (2022). Student management implementation in forming student character. Managere: Indonesian Journal of Educational Management, 4(2), 136–147. DOI: https://doi.org/10.52627/managere.v4i2.119

Binfet, J.-T., Godard, R. J. P., Green, F. L. L., & Willcox, A. A. (2025). High school students’ conceptualizations of kindness: A mixed-methods portrait. Social and Emotional Learning: Research, Practice, and Policy, 5, 100089. DOI: https://doi.org/10.1016/j.sel.2025.100089

Carbonara, V. (2023). The effects of multilingual pedagogies on language awareness: A longitudinal analysis of students’ language portraits. Linguistics and Education, 78, 101244. DOI: https://doi.org/10.1016/j.linged.2023.101244

Das, T., & Teng, B.-S. (2000). A resource-based theory of strategic alliances. Journal of Management, 26(1), 31–61. DOI: https://doi.org/10.1016/s0149-2063(99)00037-9

Dumont, J., Shen, J., & Deng, X. (2016). Effects of Green HRM Practices on Employee Workplace Green Behavior: The Role of Psychological Green Climate and Employee Green Values. Human Resource Management, 56(4), 613–627. DOI: https://doi.org/10.1002/hrm.21792

Epstein, R. M., & Hundert, E. M. (2002). Defining and Assessing Professional Competence. JAMA, 287(2), 226. DOI: https://doi.org/10.1001/jama.287.2.226

Eraut, M. (2007). Learning from other people in the workplace. Oxford Review of Education, 33(4), 403–422. DOI: https://doi.org/10.1080/03054980701425706

Gao, W.-W., Ma, H.-F., Zhao, Y., Wang, J., & Tian, Q.-H. (2024). Enhancing personalized exercise recommendation with student and exercise portraits. Journal of Electronic Science and Technology, 22(2), 100262–100262. DOI: https://doi.org/10.1016/j.jnlest.2024.100262

Grant, R. M. (1991). The resource-based theory of competitive advantage: Implications for strategy formulation. California Management Review, 33(3), 114–135. DOI: https://doi.org/10.2307/41166664

Huan Lv. (2024). Innovation of Teaching Methods of Civic and Political Education in Colleges and Universities with the Assistance of Artificial Intelligence. Applied Mathematics and Nonlinear Sciences, 9(1). DOI: https://doi.org/10.2478/amns-2024-2882

Iyengar, S., Sood, G., & Lelkes, Y. (2012). Affect, Not Ideology: A Social Identity Perspective on Polarization. Public Opinion Quarterly, 76(3), 405–431. DOI: https://doi.org/10.1093/poq/nfs059

Ji, R., Yang, J., Wu, Y., Li, Y., Li, R., Chen, J., & Yang, J. (2024). Correction to: Construction and analysis of students’ physical health portrait based on principal component analysis improved Canopy-K-means algorithm. The Journal of Supercomputing, 80(14). DOI: https://doi.org/10.1007/s11227-024-06157-y

Kolb, A. Y., & Kolb, D. A. (2005). Learning styles and learning spaces: Enhancing experiential learning in higher education. Academy of Management Learning & Education, 4(2), 193–212. DOI: https://doi.org/10.5465/amle.2005.17268566

Larsson, R., & Finkelstein, S. (1999). Integrating Strategic, Organizational, and Human Resource Perspectives on Mergers and Acquisitions: A Case Survey of Synergy Realization. Organization Science, 10(1), 1–26. DOI: https://doi.org/10.1287/orsc.10.1.1

Li, J. (2024). 2017-2024 China Learner Portrait Research Status and Hot Spot Analysis-- Citespace visualization study based on CNKI. World Education Forum, 2(4). DOI: https://doi.org/10.18686/wef.v2i4.4597

Lin, M. (2025). Research on Precision Teaching Strategies of Civic and Political Education for College Students Driven by Big Data. Applied Mathematics and Nonlinear Sciences, 10(1). DOI: https://doi.org/10.2478/amns-2025-0796

Ma, L. (2024). Precise Implementation of Mental Health Thinking Measures in Universities Based on Student Portrait Analysis. International Journal of E-Collaboration, 20(1), 1–1. DOI: https://doi.org/10.4018/ijec.348578

Muhlroth, C., & Grottke, M. (2020). Artificial Intelligence in Innovation: How to Spot Emerging Trends and Technologies. IEEE Transactions on Engineering Management, 69(2), 1–18. DOI: https://doi.org/10.1109/tem.2020.2989214

Ouzzani, M., Hammady, H., Fedorowicz, Z., & Elmagarmid, A. (2016). Rayyan—a Web and Mobile App for Systematic Reviews. Systematic Reviews, 5(1). DOI: https://doi.org/10.1186/s13643-016-0384-4

Pei, H. (2024). Functional Analysis of Physical Education Program Construction in Colleges and Universities on Promoting Students’ Mental Health. Applied Mathematics and Nonlinear Sciences, 9(1). DOI: https://doi.org/10.2478/amns-2024-3306

Qian, H. (2024). Research on Innovation and Entrepreneurship and Talent Cultivation Mode of College Students under the Background of Artificial Intelligence Technology. Applied Mathematics and Nonlinear Sciences, 9(1). DOI: https://doi.org/10.2478/amns-2024-2120

Shulman, L. (1987). Knowledge and Teaching: Foundations of the New Reform. Harvard Educational Review, 57(1), 1–23. DOI: https://doi.org/10.17763/haer.57.1.j463w79r56455411

Strijbos, J., & Engels, N. (2022). A Multifaceted Portrait of Students’ Connectedness to Urban Schools. Urban Education, 004208592210817. DOI: https://doi.org/10.1177/00420859221081769

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information technology: toward a Unified View. MIS Quarterly, 27(3), 425–478. DOI: https://doi.org/10.2307/30036540

von Schomberg, R. (2013). A Vision of Responsible Research and Innovation. Responsible Innovation, 51–74. DOI: https://doi.org/10.6084/m9.figshare.995971

Wang, Y., Wen, J., Zhou, W., Wu, Q., Wei, Y., Li, H., & Tao, B. (2022). Research on Abnormal Behavior Prediction by Integrating Multiple Indexes of Student Behavior and Text Information in Big Data Environment. Wireless Communications and Mobile Computing, 2022, 1–17. DOI: https://doi.org/10.1155/2022/1902155

Wu, X., Lu, Y., Zeng, Y., Han, H., Sun, X., Zhang, J., Wei, N., & Ye, Z. (2024). Personality portraits, resilience, and professional identity among nursing students: a cross-sectional study. BMC Nursing, 23(1). DOI: https://doi.org/10.1186/s12912-024-02007-7

Zhai, Y., & Gan, Y. (2025). Extraction and analysis of individual features of students’ ideological and political education based on big data clustering algorithm. Applied Mathematics and Nonlinear Sciences, 10(1). DOI: https://doi.org/10.2478/amns-2025-0531