EMPLOYABILITY AND VISUAL SELF-PRESENTATION: A STUDY OF SKILLS, EXPERIENCE, AND DIGITAL PORTFOLIOS
DOI:
https://doi.org/10.29121/shodhkosh.v6.i5s.2025.6964Keywords:
Employability, Visual Self-Presentation, Digital Portfolios, Skills Assessment, Online Recruitment, Professional IdentityAbstract [English]
The digitalized recruitment space is becoming more focused on visual self-presentation to transform the perception and assessment of employability. This paper analyzes the connection that exists between skills and experience in the workplace and digital portfolios with respect to the effect of visual storytelling on the hiring opinion. The discussed issue is the disconnect between real and visual capabilities and visually mediated visuals that take the upper hand in online recruitment services. The aim is to investigate the ways through which graduates and early-career professionals create digital portfolios and how employers perceive visual elements together with the reported skills and experience. The analysis approach integrates the contents of the portfolios, and extraction of features through machine learning and recruiter assessment study. The layout structure, imagery, typography, and storytelling of the project are visual elements that are reviewed against confirmed skill sets, internship histories, and showing a performance indicator. Quantitative models are used to evaluate correlations between the quality of visual presentation and shortlisting outcomes, and qualitative interviews are used to evaluate the employer perception of credibility, professionalism, and cultural fit. The results indicate that the perceived value of skills and experience in relation to coherent visual storytelling is stronger and provides a high shortlisting probability even in comparison with technical qualifications of equal weight. Nonetheless, having too much aesthetic focus, which is not supported by evidence, has a detrimental impact on the measurement of trust and employability. The analysis shows the differences in access to visual literacy and design tools, creating a problem regarding the equity of digitally mediated recruitment.
References
Albina, A., and Sumagaysay, L. (2020). Employability Tracer Study of Information Technology Education Graduates from a State University in the Philippines. Social Sciences and Humanities Open, 100055, 1–6. https://doi.org/10.1016/j.ssaho.2020.100055 DOI: https://doi.org/10.1016/j.ssaho.2020.100055
Assegie, T. A., Salau, A. O., Chhabra, G., Kaushik, K., and Braide, S. L. (2024). Evaluation of Random Forest and Support Vector Machine Models in Educational Data Mining. 2024 2nd International Conference on Advancement in Computation and Computer Technologies (InCACCT), 131–135. https://doi.org/10.1109/InCACCT61598.2024.10551110 DOI: https://doi.org/10.1109/InCACCT61598.2024.10551110
Aviso, K., Janairo, J., Lucas, R., Promentilla, M., Yu, D., and Tan, R. (2020). Predicting Higher Education Outcomes with Hyperbox Machine Learning: What Factors Influence Graduate Employability? Chemical Engineering Transactions, 81, 679–684.
Celine, S., Dominic, M. M., and Devi, M. S. (2020). Logistic Regression for Employability Prediction. International Journal of Innovative Technology and Exploring Engineering, 9(3), 2471–2478. https://doi.org/10.35940/ijitee.C8170.019320 DOI: https://doi.org/10.35940/ijitee.C8170.019320
Chopra, A., and Saini, M. L. (2023). Comparison Study of Different Neural Network Models for Assessing Employability Skills of IT Graduates. 2023 International Conference on Sustainable Communication Networks and Application (ICSCNA), Theni, India, 189–194. https://doi.org/10.1109/ICSCNA58489.2023.10368605 DOI: https://doi.org/10.1109/ICSCNA58489.2023.10368605
Maaliw, R. R., Quing, K. A. C., Lagman, A. C., Ugalde, B. H., Ballera, M. A., and Ligayo, M. A. D. (2022). Employability Prediction of Engineering Graduates Using Ensemble Classification Modeling. 2022 Ieee 12th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 288–294. https://doi.org/10.1109/CCWC54503.2022.9720783 DOI: https://doi.org/10.1109/CCWC54503.2022.9720783
Monteiro, S., Almeida, L., Gomes, C., and Sinval, J. (2020). Employability Profiles of Higher Education Graduates: A Person-Oriented Approach. Studies in Higher Education, 1–14. https://doi.org/10.1080/03075079.2020.1761785 DOI: https://doi.org/10.1080/03075079.2020.1761785
Nordin, N. I., Sobri, N. M., Ismail, N. A., Mahmud, M., and Alias, N. A. (2022). Modelling Graduate Unemployment from Students’ Perspectives. Journal of Media and Communication Studies, 8(2), 68–78. https://doi.org/10.24191/jmcs.v8i2.6986 DOI: https://doi.org/10.24191/jmcs.v8i2.6986
Oliver, N., Pérez-Cruz, F., Kramer, S., Read, J., and Lozano, J. (2021). Machine Learning and Knowledge Discovery in Databases. In Research Track 12975. Springer Nature. https://doi.org/10.1007/978-3-030-86523-8 DOI: https://doi.org/10.1007/978-3-030-86520-7
Philippine Statistic Authority. (2021). Unemployment Rate in September 2021 is Estimated at 8.9 Percent.
Shahriyar, J., Ahmad, J. B., Zakaria, N. H., and Su, G. E. (2022). Enhancing Prediction of Employability of Students: Automated Machine Learning Approach. 2022 2nd International Conference on Intelligent Cybernetics Technology and Applications (ICICyTA), Bandung, Indonesia, 87–92. https://doi.org/10.1109/ICICyTA57421.2022.10038231 DOI: https://doi.org/10.1109/ICICyTA57421.2022.10038231
Shuker, F. M., and Sadik, H. H. (2024). A Critical Review on Rural Youth Unemployment in Ethiopia. International Journal of Adolescence and Youth, 29(1), 1–17. https://doi.org/10.1080/02673843.2024.2322564 DOI: https://doi.org/10.1080/02673843.2024.2322564
Tamene, E. H., Salau, A. O., Vats, S., Kaushik, K., Molla, T. L., and Tin, T. T. (2024). Predictive Analysis of Graduate Students' Employability Using Machine Learning Techniques. 2024 International Conference on Artificial Intelligence and Emerging Technology (Global AI Summit), Greater Noida, India, 557–562. https://doi.org/10.1109/GlobalAISummit62156.2024.10947923 DOI: https://doi.org/10.1109/GlobalAISummit62156.2024.10947923
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Dr. Vaishali Rahate, Dr. Swati Sachin Jadhav, Roopa David, Abhijeet Deshpande, Bhairavi Kumbhare, Mohd Asif

This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.























