A Segmentation Analysis Utilizing Natural Language Processing Model with Interactive Data Analytics Dashboard for Research Management Platform
DOI:
https://doi.org/10.11594/ijmaber.06.02.31Keywords:
Natural Language Processing, Data Analytics Dashboard, Research Management PlatformAbstract
Research is a vital component of a university and, currently, unstruc-tured big data is a significant issue in various ICT industries and insti-tutions. To solve this modern problem, the researchers developed a system to streamline the manual operations and traditional research management system of the university through Natural Language Pro-cessing (NPL). This quantitative research utilizing descriptive-developmental design is about designing and evaluating A Segmenta-tion Analysis Utilizing Natural Language Processing Model with In-teractive Data Analytics Dashboard for Bulacan State University Re-search Management Platform utilizes the framework of progressive prototyping in the development process. Consultative meetings, in-terviews and the use of survey questionnaires were held to obtain data from ten (10) RDO/CDRU and staff, twenty (20) IT experts and twenty (20) academicians were chosen using random sampling. Re-sults show that personalize learning management system is excellent in terms of functional suitability (M=4.66), performance efficiency (M=4.68), compatibility (M=4.67), usability (M=4.74), reliability (M=4.51), security (M=4.44), maintainability (M=4.72), and portabil-ity (M=4.65). Subsequently, the developed system recorded a grand mean of 4.63 interpreted as Excellent among all ISO/IEC 25010 crite-ria. This indicates that the system complies with end-user needs as well as software quality standards. It is therefore prepared for adop-tion. Along with its implementation, it is recommended to gather feedback regularly and conduct an impact analysis of the effective-ness of using the segmentation analysis utilizing natural language processing model with interactive data analytics dashboard for re-search management platform.
Downloads
References
Boston University. (n.d.). Research Information Management System (RIMS). Retrieved from https://www.bu.edu/tech/services/admin/research-systems/rims/
Bowdre, P. R. (2020). The use of predictive analytics to shift the culture of academic advising toward a focus on student suc-cess. Journal of Educational and Social Research, 7(3), 45–55. Retrieved from https://jespnet.com/journals/Vol_7_No_3_September_2020/3.pdf
Budi, S., Gata, W., Noor, M., Pangabean, S., & Rahayu, C. S. (2022). News portal website measurement analysis using ISO/IEC 25010 and McCall methods. Journal of Applied Engineering and Technological Science (JAETS), 4(1), 273–285.
Demmelmaier, G., & Westerberg, C. (2021). Data segmentation using NLP: Gender and age. Retrieved from https://www.diva-por-tal.org/smash/get/diva2:1527530/FULLTEXT01.pdf
Dhingra, P., Gayathri, N., Rakesh Kumar, S., Singanamalla, V., Ramesh, C., & Bal-amurugan, B. (2020). Internet of Things–based pharmaceutics data analysis. Emergence of Pharmaceutical Industry Growth with Industrial IoT Approach, 85–131. https://doi.org/10.1016/b978-0-12-819593-2.00004-2
Elia, G., Polimeno, G., Solazzo, G., & Passiante, G. (2020). A multi-dimension framework for value creation through big data. In-dustrial Marketing Management, 90, 617–632.
Elsevier. (n.d.). Why you need a Research In-formation Management System (RIMS). Retrieved from https://beta.elsevier.com/academic-and-government/why-you-need-cris?trial=true
Fire, M., & Guestrin, C. (2019). Over-optimization of academic publishing met-rics: Observing Goodhart's Law in action. GigaScience, 8. https://doi.org/10.1093/gigascience/giz053
Gao, H., Xu, Y., Yin, Y., Zhang, W., Li, R., & Wang, X. (2019). Context-aware QoS pre-diction with neural collaborative filtering for Internet-of-Things services. IEEE In-ternet of Things Journal, 1–1. https://doi.org/10.1109/jiot.2019.2956827
Johnson, R., Watkinson, A., & Mabe, M. (2018). The STM Report, 5th edition: An overview of scientific and scholarly publishing. STM. Retrieved February 11, 2025, from https://coilink.org/20.500.12592/7qcc41
Joshi, A., & Unger, K. (2017). Introduction to data visualization and visualizing your data effectively. Retrieved from https://www2.cs.uh.edu/~ceick/UDM/COSC3337-DV1.pdf
Kelly, S. (2018). The continuing evolution of publishing in the biological sciences. Bi-ology Open, 7(8). https://doi.org/10.1242/bio.037325
Li, J., Chiu, B., Shang, S., & Shao, L. (2020). Neural text segmentation and its applica-tion to sentiment analysis. IEEE Transac-tions on Knowledge and Data Engineer-ing, 34(2), 828–842.
Li, J., Sun, A., & Joty, S. R. (2018, July). SegBot: A generic neural text segmentation model with pointer network. Proceedings of the 27th International Joint Conference on Artificial Intelligence, 4166–4172. https://doi.org/10.5555/3304222.3304349
Love, DeMonner, & Teasley. (2021). Show stu-dents their data: Using dashboards to support self-regulated learning. Retrieved from https://er.educause.edu/articles/2021/7/show-students-their-data-using-dashboards-to-support-self-regulated-learning
Lukasik, M., Dadachev, B., Simoes, G., & Pap-ineni, K. (2020). Text segmentation by cross-segment attention. arXiv preprint arXiv:2004.14535.
Nistor, N., & Hernández-García, Á. (2018). What types of data are used in learning analytics? An overview of six cases. Com-puters in Human Behavior, 89, 335–338.
Olivetti, E. A., Cole, J. M., Kim, E., Kononova, O., Ceder, G., Han, T. Y. J., & Hiszpanski, A. M. (2020). Data-driven materials research enabled by natural language processing and information extraction. Applied Phys-ics Reviews, 7(4), 041317.
Shtekh, G., Kazakova, P., Nikitinsky, N., & Skachkov, N. (2018, October). Applying topic segmentation to document-level in-formation retrieval. Proceedings of the 14th Central and Eastern European Soft-ware Engineering Conference Russia, 1–6.
Tyagi, A. (2021). A review study of natural language processing techniques for text mining. International Journal of Engineer-ing Research & Technology, 10(9). https://doi.org/10.17577/IJERTV10IS090156
University of Waterloo. (n.d.). Data visualiza-tion. Retrieved from https://uwaterloo.ca/centre-for-teaching-excellence/catalogs/tip-sheets/data-visualization
Wang, D., Su, J., & Yu, H. (2020). Feature ex-traction and analysis of natural language processing for deep learning English lan-guage. IEEE Access, 8, 46335–46345.
Whitehead, D. (2019). Re: What is the stand-ard number of publications per year (es-pecially journal papers) for a researcher? Retrieved from https://www.researchgate.net/post/What-is-the-standard-number-of-publications-per-year-specially-journal-papers-for-a-research-er/5c42b0bcc7d8ab077805162a/citation/download
Wright, A., & Wiklicky, H. (2019). Comparison of syntactic and semantic representations of programs in neural embeddings. Re-trieved from https://arxiv.org/pdf/2001.09201.pdf
Downloads
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See the Effect of Open Access).
