Gomathy Ramaswami

437 total citations
9 papers, 248 citations indexed

About

Gomathy Ramaswami is a scholar working on Computer Science Applications, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Gomathy Ramaswami has authored 9 papers receiving a total of 248 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computer Science Applications, 5 papers in Artificial Intelligence and 4 papers in Computer Networks and Communications. Recurrent topics in Gomathy Ramaswami's work include Online Learning and Analytics (9 papers), Software System Performance and Reliability (4 papers) and Explainable Artificial Intelligence (XAI) (4 papers). Gomathy Ramaswami is often cited by papers focused on Online Learning and Analytics (9 papers), Software System Performance and Reliability (4 papers) and Explainable Artificial Intelligence (XAI) (4 papers). Gomathy Ramaswami collaborates with scholars based in New Zealand and Pakistan. Gomathy Ramaswami's co-authors include Teo Sušnjak, Anuradha Mathrani, Pablo García, James B.P. Lim, Andre L. C. Barczak and Rahila Umer and has published in prestigious journals such as SHILAP Revista de lepidopterología, International Journal of Educational Technology in Higher Education and Technology Knowledge and Learning.

In The Last Decade

Gomathy Ramaswami

9 papers receiving 237 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Gomathy Ramaswami New Zealand 7 168 88 43 38 22 9 248
Sandra G. Nunn United States 3 188 1.1× 48 0.5× 86 2.0× 42 1.1× 28 1.3× 5 254
John T. Avella United States 3 188 1.1× 48 0.5× 88 2.0× 42 1.1× 28 1.3× 7 256
Therese Kanai United States 3 189 1.1× 46 0.5× 92 2.1× 43 1.1× 28 1.3× 4 250
Alfred Essa United States 7 195 1.2× 68 0.8× 62 1.4× 34 0.9× 31 1.4× 17 236
Vangel V. Ajanovski North Macedonia 4 254 1.5× 74 0.8× 53 1.2× 70 1.8× 52 2.4× 16 303
Donald Ipperciel Canada 6 147 0.9× 85 1.0× 47 1.1× 40 1.1× 12 0.5× 27 258
Geraldine Gray Ireland 8 176 1.0× 82 0.9× 51 1.2× 38 1.0× 29 1.3× 22 228
Joshua D. Baron United States 6 287 1.7× 123 1.4× 120 2.8× 27 0.7× 29 1.3× 8 344
Pratya Nuankaew Thailand 10 143 0.9× 52 0.6× 86 2.0× 61 1.6× 55 2.5× 54 299
Avinash Boroowa United Kingdom 9 319 1.9× 59 0.7× 167 3.9× 56 1.5× 38 1.7× 11 395

Countries citing papers authored by Gomathy Ramaswami

Since Specialization
Citations

This map shows the geographic impact of Gomathy Ramaswami's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Gomathy Ramaswami with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gomathy Ramaswami more than expected).

Fields of papers citing papers by Gomathy Ramaswami

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gomathy Ramaswami. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Gomathy Ramaswami. The network helps show where Gomathy Ramaswami may publish in the future.

Co-authorship network of co-authors of Gomathy Ramaswami

This figure shows the co-authorship network connecting the top 25 collaborators of Gomathy Ramaswami. A scholar is included among the top collaborators of Gomathy Ramaswami based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Gomathy Ramaswami. Gomathy Ramaswami is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

9 of 9 papers shown
1.
Sušnjak, Teo, Gomathy Ramaswami, & Anuradha Mathrani. (2022). Learning analytics dashboard: a tool for providing actionable insights to learners. International Journal of Educational Technology in Higher Education. 19(1). 12–12. 95 indexed citations
2.
Ramaswami, Gomathy, Teo Sušnjak, & Anuradha Mathrani. (2022). Supporting Students’ Academic Performance Using Explainable Machine Learning with Automated Prescriptive Analytics. Big Data and Cognitive Computing. 6(4). 105–105. 7 indexed citations
3.
Ramaswami, Gomathy, Teo Sušnjak, Anuradha Mathrani, & Rahila Umer. (2022). Use of Predictive Analytics within Learning Analytics Dashboards: A Review of Case Studies. Technology Knowledge and Learning. 28(3). 959–980. 25 indexed citations
4.
Ramaswami, Gomathy, Teo Sušnjak, & Anuradha Mathrani. (2022). On Developing Generic Models for Predicting Student Outcomes in Educational Data Mining. Big Data and Cognitive Computing. 6(1). 6–6. 24 indexed citations
5.
Mathrani, Anuradha, Teo Sušnjak, Gomathy Ramaswami, & Andre L. C. Barczak. (2021). Perspectives on the challenges of generalizability, transparency and ethics in predictive learning analytics. SHILAP Revista de lepidopterología. 2. 100060–100060. 37 indexed citations
6.
Ramaswami, Gomathy, Teo Sušnjak, Anuradha Mathrani, & Rahila Umer. (2020). Predicting Students Final Academic Performance using Feature Selection Approaches. 1–5. 6 indexed citations
7.
Ramaswami, Gomathy, Teo Sušnjak, & Anuradha Mathrani. (2019). Capitalizing on Learning Analytics Dashboard for Maximizing Student Outcomes. 1–6. 5 indexed citations
8.
Ramaswami, Gomathy, Teo Sušnjak, Anuradha Mathrani, James B.P. Lim, & Pablo García. (2019). Using educational data mining techniques to increase the prediction accuracy of student academic performance. Information and Learning Sciences. 120(7/8). 451–467. 48 indexed citations
9.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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2026