Kathrin Seßler

5.9k total citations · 2 hit papers
4 papers, 489 citations indexed

About

Kathrin Seßler is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Experimental and Cognitive Psychology. According to data from OpenAlex, Kathrin Seßler has authored 4 papers receiving a total of 489 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Artificial Intelligence, 1 paper in Computer Vision and Pattern Recognition and 1 paper in Experimental and Cognitive Psychology. Recurrent topics in Kathrin Seßler's work include Visual and Cognitive Learning Processes (1 paper), Innovative Teaching and Learning Methods (1 paper) and Online Learning and Analytics (1 paper). Kathrin Seßler is often cited by papers focused on Visual and Cognitive Learning Processes (1 paper), Innovative Teaching and Learning Methods (1 paper) and Online Learning and Analytics (1 paper). Kathrin Seßler collaborates with scholars based in Germany. Kathrin Seßler's co-authors include Gjergji Kasneci, Martin Pawelczyk, Vadim Borisov, Tobias Leemann, Johannes Haug, Enkelejda Kasneci, Arne Bewersdorff, Claudia Nerdel, Xiaoming Zhaı and Maria Bannert and has published in prestigious journals such as IEEE Transactions on Neural Networks and Learning Systems, Learning and Individual Differences and Computers and Education Artificial Intelligence.

In The Last Decade

Kathrin Seßler

4 papers receiving 476 citations

Hit Papers

Deep Neural Networks and Tabular Data: A Survey 2022 2026 2023 2024 2022 2025 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Deep Neural Networks and Tabular Data: A Survey
    2022 428 citations Vadim Borisov, Tobias Leemann et al. IEEE Transactions on Neural Networks and Learning Systems profile →
  2. Taking the next step with generative artificial intelligence: The transformative role of multimodal large language models in science education
    2025 25 citations Arne Bewersdorff, Christian Hartmann et al. Learning and Individual Differences profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kathrin Seßler Germany 4 234 46 39 34 28 4 489
Tobias Leemann Germany 3 262 1.1× 54 1.2× 32 0.8× 33 1.0× 26 0.9× 4 513
Martin Pawelczyk Germany 2 206 0.9× 45 1.0× 29 0.7× 33 1.0× 26 0.9× 3 432
Vadim Borisov Russia 9 223 1.0× 55 1.2× 34 0.9× 35 1.0× 42 1.5× 48 652
Mahmoud B. Alhasanat Jordan 7 156 0.7× 60 1.3× 52 1.3× 19 0.6× 30 1.1× 16 531
Awny Sayed Egypt 8 229 1.0× 55 1.2× 65 1.7× 29 0.9× 14 0.5× 21 443
Shen Li-ming China 5 231 1.0× 78 1.7× 36 0.9× 49 1.4× 22 0.8× 8 493
Mohaimenul Azam Khan Raiaan Bangladesh 10 231 1.0× 93 2.0× 44 1.1× 31 0.9× 17 0.6× 26 589
Harshvardhan GM India 11 162 0.7× 96 2.1× 39 1.0× 49 1.4× 33 1.2× 20 488
Eduardo Soares Brazil 8 354 1.5× 73 1.6× 44 1.1× 24 0.7× 22 0.8× 30 734
Jeffrey Li United States 4 234 1.0× 30 0.7× 24 0.6× 14 0.4× 12 0.4× 5 524

Countries citing papers authored by Kathrin Seßler

Since Specialization
Citations

This map shows the geographic impact of Kathrin Seßler'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 Kathrin Seßler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kathrin Seßler more than expected).

Fields of papers citing papers by Kathrin Seßler

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kathrin Seßler. 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 Kathrin Seßler. The network helps show where Kathrin Seßler may publish in the future.

Co-authorship network of co-authors of Kathrin Seßler

This figure shows the co-authorship network connecting the top 25 collaborators of Kathrin Seßler. A scholar is included among the top collaborators of Kathrin Seßler 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 Kathrin Seßler. Kathrin Seßler is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

4 of 4 papers shown
1.
Seßler, Kathrin, et al.. (2025). Can AI grade your essays? A comparative analysis of large language models and teacher ratings in multidimensional essay scoring. Open access LMU (Ludwid Maxmilian's Universitat Munchen). 462–472. 6 indexed citations
2.
Bewersdorff, Arne, Christian Hartmann, Kathrin Seßler, et al.. (2025). Taking the next step with generative artificial intelligence: The transformative role of multimodal large language models in science education. Learning and Individual Differences. 118. 102601–102601. 25 indexed citations breakdown →
3.
Bewersdorff, Arne, et al.. (2023). Assessing student errors in experimentation using artificial intelligence and large language models: A comparative study with human raters. Computers and Education Artificial Intelligence. 5. 100177–100177. 30 indexed citations
4.
Borisov, Vadim, Tobias Leemann, Kathrin Seßler, et al.. (2022). Deep Neural Networks and Tabular Data: A Survey. IEEE Transactions on Neural Networks and Learning Systems. 35(6). 7499–7519. 428 indexed citations breakdown →

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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