Evaluating Web Frameworks for Personal Learning Decision-Making: A Comparative Analysis

Authors

  • Lindita Nebiu Hyseni Kadri Zeka Public University, Gjilan, Kosovo
  • Artan Dermaku 2Kadri Zeka Public University, Gjilan, Kosovo
  • Zamir Dika South East European University, Tetovo, North Macedonia

DOI:

https://doi.org/10.22399/ijcesen.1845

Keywords:

Web frameworks, Job postings, Google Core Web Vitals, Learning curve, Community support

Abstract

Nowadays with rapid evolution of World Wide Web, the web frameworks of different programing languages are crucial for development standardization, but the huge type of web frameworks makes challenges for chosen the appropriate one for personal learning therefore, this study evaluates and compare the PhP and Python popular web frameworks in terms of their job market trends, web performances, learning resources and community support.Findings of job market trends shows that PhP web frameworks such as Laravel and Slim are being widespread in Europe region, while the python web frameworks such as Django and Flask are being more widespread in the United States. Whereas the findings of performance testing shows that Laravel outperform better in loading speed comparing to the other web frameworks treated in this study, while Django perform better in visual stability and Flask is better than Slim in responsiveness. As well, the survey findings shows that Online tutorials and ChatGPT are most used learning resources comparing to traditional community forums. These findings offer valuable understanding for developers and students in choosing the appropriate web framework for their personal learning based on job market demands and project requirements and can be used by educators and policymakers in curriculum adaption and teaching strategy to meet the industry needs.Moreover, this study contributes to a deeper understanding of Laravel, Django, Slim and Flask framework adoption in professional and academic settings considering the gap in the literature regarding the comparison of frameworks especially for performances, learning resources and job market trends.

References

[1] Bray, M., Adamson, B., & Mason, M. (Eds.). (2014). Comparative education research: Approaches and methods(Vol. 19). Springer. DOI: https://doi.org/10.1007/978-3-319-05594-7

[2] Rabianski, J. S. (2003). Primary and secondary data: Concepts, concerns, errors, and issues. The Appraisal Journal, 71(1), 43.

[3] Govinda, K., Reddy, M., & Haldar, A. (2024). Job recommendation system using LinkedIn user profiles. In 2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE) (pp. 1-6). IEEE.

[4] Johnson, M. A., & Leo, C. (2020). The inefficacy of LinkedIn? A latent change model and experimental test of using LinkedIn for job search. Journal of Applied Psychology, 105(11), 1262. DOI: https://doi.org/10.1037/apl0000491

[5] da Motta Veiga, S. P., Clark, B. B., & Moake, T. R. (2020). Influence of job-dedicated social media on employer reputation. Corporate Reputation Review, 23, 241-253. DOI: https://doi.org/10.1057/s41299-019-00083-z

[6] Orgad, S. (2024). Posting vulnerability on LinkedIn. New Media & Society. https://doi.org/10.1177/14614448241243094 DOI: https://doi.org/10.1177/14614448241243094

[7] Govinda, K., Reddy, M., & Haldar, A. (2024). Job recommendation system using LinkedIn user profiles. In 2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE) (pp. 1-6). IEEE. DOI: https://doi.org/10.1109/ic-ETITE58242.2024.10493325

[8] Dobbala, M. K., & Lingolu, M. S. S. (2022). Web performance tooling and the importance of web vitals. Journal of Technological Innovations, 3(3).

[9] Vepsäläinen, J., Hellas, A., & Vuorimaa, P. (2024). Overview of web application performance optimization techniques. arXiv preprint arXiv:2412.07892.

[10] Edgar, M. (2024). Cumulative layout shift. In Speed Metrics Guide: Choosing the Right Metrics to Use When Evaluating Websites (pp. 157-180). Berkeley, CA: Apress. DOI: https://doi.org/10.1007/979-8-8688-0155-6_9

[11] Wehner, N., Amir, M., Seufert, M., Schatz, R., & Hoßfeld, T. (2022, September). A vital improvement? Relating Google's core web vitals to actual web QoE. In 2022 14th International Conference on Quality of Multimedia Experience (QoMEX) (pp. 1-6). IEEE. DOI: https://doi.org/10.1109/QoMEX55416.2022.9900881

[12]del Pilar Salas-Zárate, M., Alor-Hernández, G., Valencia-García, R., Rodríguez-Mazahua, L., Rodríguez-González, A., & López Cuadrado, J. L. (2015). Analyzing best practices on web development frameworks: The lift approach. Science of Computer Programming, 102, 1-19. DOI: https://doi.org/10.1016/j.scico.2014.12.004

[13] Hasnain, M., & Ullah, S. (2023). Learning and programming challenges of React.js open-source framework. Library Hi Tech News. DOI: https://doi.org/10.1108/LHTN-05-2023-0088

[14] Vlachopoulos, D., & Makri, A. (2019). Online communication and interaction in distance higher education: A framework study of good practice. International Review of Education, 65(4), 605-632. DOI: https://doi.org/10.1007/s11159-019-09792-3

[15] Reynolds, E., Treahy, D., Chao, C. C., & Barab, S. (2021). The Internet learning forum: Developing a community prototype for teachers of the 21st century. In The Web in Higher Education (pp. 107-125). CRC Press. DOI: https://doi.org/10.1201/9781003063803-7

[16] Cameron, R. (2009). A sequential mixed model research design: Design, analytical and display issues. International Journal of Multiple Research Approaches, 3(2), 140-152. DOI: https://doi.org/10.5172/mra.3.2.140

[17] Peng, G. C., Nunes, J. M. B., & Annansingh, F. (2011). Investigating information systems with mixed-methods research. In Proceedings of the IADIS International Workshop on Information Systems Research Trends, Approaches and Methodologies (pp. 1-7). Sheffield.

[18] Hyseni, L. N., & Dika, Z. (2017, August). An integrated framework of conceptual modeling for performance improvement of the information systems. In 2017 Seventh International Conference on Innovative Computing Technology (INTECH) (pp. 174-180). IEEE DOI: https://doi.org/10.1109/INTECH.2017.8102438

Downloads

Published

2025-05-15

How to Cite

Nebiu Hyseni, L., Dermaku, A., & Dika, Z. (2025). Evaluating Web Frameworks for Personal Learning Decision-Making: A Comparative Analysis. International Journal of Computational and Experimental Science and Engineering, 11(2). https://doi.org/10.22399/ijcesen.1845

Issue

Section

Research Article