Analyzing the Impact of Online Learning on Higher Education: A Text Analytics Approach

Authors

  • Gulam Ruti Asplangyi Independent University

DOI:

https://doi.org/10.58776/ijitcsa.v2i2.149

Keywords:

Text Analytics, Higher Education, Data Analytics, Descriptive Analytics

Abstract

Amidst the relentless upheaval caused by the ongoing Covid-19 pandemic, the higher education landscape finds itself compelled to pivot towards internet-mediated learning modalities. This shift, while necessary for continuity, has engendered profound repercussions for students, educators, and administrative staff alike. Foremost among the concerns is the discernible impact on student learning outcomes and academic performance. Studies, such as those conducted by Brookings and The University of Chicago, underscore the alarming projections of learning loss and escalating failure rates within this context. Bloom, a prominent higher education institution grappling with the tumult of the pandemic, has witnessed a palpable decline in average grades since its onset. Recognizing the imperative to stem this tide and foster informed decision-making, Bloom endeavors to harness the power of text analytics. Through the systematic analysis of unstructured textual data sourced from diverse channels—ranging from social media platforms to educational websites—Bloom endeavors to unveil underlying patterns, discern actionable insights, and drive strategic interventions. This article presents a comprehensive framework delineating Bloom's foray into text analytics, elucidating the attendant challenges, proposed solutions, and anticipated implementation strategies. By delving into the nuances of managing unstructured textual data and navigating the complexities thereof, this endeavor seeks to empower Bloom with the tools and insights requisite for optimizing academic performance and mitigating the deleterious effects of the pandemic.

References

. R. R. Ribot, “A prediction model for student attrition using J48 classification,” International Journal for Research in Applied Science and Engineering Technology, vol. 8, no. 5, pp. 329–337, May 2020. doi:10.22214/ijraset.2020.5055.

. B. L. Shilpa and B. R. Shambhavi, “Structuring of unstructured data from heterogeneous sources,” Indian Journal Of Science And Technology, vol. 15, no. 41, pp. 2188–2193, Nov. 2022. doi:10.17485/ijst/v15i41.1566.

. M. Anandarajan, C. Hill, and T. Nolan, “Planning for text analytics,” Practical Text Analytics, pp. 27–41, Oct. 2018. doi:10.1007/978-3-319-95663-3_3.

. F. S. Esen, “Measuring web site performance with web analytics,” Social Media Analytics in Predicting Consumer Behavior, pp. 130–150, Mar. 2023. doi:10.1201/9781003200154-7.

. P. Keenan, “Geographic information systems and location analytics for business and Management,” Oxford Research Encyclopedia of Business and Management, Feb. 2020. doi:10.1093/acrefore/9780190224851.013.200.

. D. Ifenthaler, Learning analytics for school and system management, Oct. 2021. doi:10.1787/d535b828-en.

. J. S. Aguilar-Ruiz, A. Bifet, and J. Gama, “Data Stream Analytics,” Analytics, vol. 2, no. 2, pp. 346–349, Apr. 2023. doi:10.3390/analytics2020019.

. A. B. Hernández-Lara, A. Perera-Lluna, and E. Serradell-López, “Applying learning analytics to students’ interaction in business simulation games. the usefulness of learning analytics to know what students really learn,” Computers in Human Behavior, vol. 92, pp. 600–612, Mar. 2019. doi:10.1016/j.chb.2018.03.001.

. K. K. Lwin, R. C. Estoque, and Y. Murayama, “Data collection, processing, and applications for geospatial analysis,” Progress in Geospatial Analysis, pp. 29–48, 2012. doi:10.1007/978-4-431-54000-7_3.

. C. Irti, “Personal data, non-personal data, anonymised data, pseudonymised data, de-identified data,” Services and Business Process Reengineering, pp. 49–57, Aug. 2021. doi:10.1007/978-981-16-3049-1_5.

. O. A. Makhova, “Informing the users of medical devices about New Safety Data,” Medical Technologies. Assessment and Choice, no. 3, p. 41, 2023. doi:10.17116/medtech20234503141.

. S. Gupta and V. Giri, “Managing Data Lake Operations,” Practical Enterprise Data Lake Insights, pp. 297–315, 2018. doi:10.1007/978-1-4842-3522-5_8.

. A. Greasley, Simulating business processes for descriptive, predictive, and prescriptive analytics, Oct. 2019. doi:10.1515/9781547400690.

. X. Chen, H. Xie, and X. Tao, “Vision, status, and research topics of Natural Language Processing,” Natural Language Processing Journal, vol. 1, p. 100001, 2022. doi:10.1016/j.nlp.2022.100001.

. Y. Betancourt and S. Ilarri, “Use of text mining techniques for Recommender Systems,” Proceedings of the 22nd International Conference on Enterprise Information Systems, 2020. doi:10.5220/0009576507800787.

. E. Fersini, “Sentiment Analysis in social networks,” Sentiment Analysis in Social Networks, pp. 91–111, 2017. doi:10.1016/b978-0-12-804412-4.00006-

Downloads

Published

10-05-2024

How to Cite

Asplangyi, G. R. (2024). Analyzing the Impact of Online Learning on Higher Education: A Text Analytics Approach. International Journal of Information Technology and Computer Science Applications, 2(2), 107–114. https://doi.org/10.58776/ijitcsa.v2i2.149

Issue

Section

New Submission