Kathrin Seßler
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence top 5%
- Machine Learning and Data Classification
- Machine Learning in Healthcare
- Topic Modeling
- Anomaly Detection Techniques and Applications
- Explainable Artificial Intelligence (XAI)
- Imbalanced Data Classification Techniques
Papers in
-
- Text Readability and Simplification 1
- Intelligent Tutoring Systems and Adaptive Learning 1
- Natural Language Processing Techniques 1
- Topic Modeling 1
- Co-authors
- Gjergji Kasneci (2 shared papers)Vadim Borisov (1 shared paper)Martin Pawelczyk (1 shared paper)Tobias Leemann (1 shared paper)Johannes Haug (1 shared paper)Enkelejda Kasneci (3 shared papers)Arne Bewersdorff (2 shared papers)Claudia Nerdel (2 shared papers)
- Journals
- Computers and Education Artificial Intelligence (1 paper)Learning and Individual Differences (1 paper)IEEE Transactions on Neural Networks and Learning Systems (1 paper)Open access LMU (Ludwid Maxmilian's Universitat Munchen) (1 paper)
- Partner nations
- GermanyUnited States
In The Last Decade
Kathrin Seßler
4 papers receiving 557 citations
Kathrin Seßler's Hit Papers
Peers
Comparison fields: 5 of 130
- Health Informatics 21
- Artificial Intelligence 243
- Health Information Management 29
- Computer Science Applications 18
- Computer Vision and Pattern Recognition 53
Countries citing papers authored by Kathrin Seßler
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
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-authors
The 12 scholars most cited alongside Kathrin Seßler, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Deep Neural Networks and Tabular Data: A Survey Hit paper breakdown → | 2022 | 485 |
| 2 | Taking the next step with generative artificial intelligence: The transformative role of multimodal large language models in science education Hit paper breakdown → | 2025 | 37 |
| 3 | 2023 | 35 | |
| 4 | 2025 | 11 |
About Kathrin Seßler
Kathrin Seßler is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Education and Computer Science Applications, having authored 4 papers that have together received 568 indexed citations. Recurring topics across this work include Visual and Cognitive Learning Processes (1 paper), Text Readability and Simplification (1 paper), Intelligent Tutoring Systems and Adaptive Learning (1 paper), Natural Language Processing Techniques (1 paper), Innovative Teaching and Learning Methods (1 paper), Educational Assessment and Pedagogy (1 paper), Educational Games and Gamification (1 paper) and Topic Modeling (1 paper). The work is most often cited by research in Health Informatics (21 citations), Artificial Intelligence (243 citations), Health Information Management (29 citations), Computer Science Applications (18 citations) and Computer Vision and Pattern Recognition (53 citations). Kathrin Seßler has collaborated with scholars based in Germany and United States. Frequent co-authors include Gjergji Kasneci, Vadim Borisov, Martin Pawelczyk, Tobias Leemann, Johannes Haug, Enkelejda Kasneci, Arne Bewersdorff, Claudia Nerdel, Christian Hartmann and Marie Hornberger. Their work appears in journals such as Computers and Education Artificial Intelligence, Learning and Individual Differences, IEEE Transactions on Neural Networks and Learning Systems and Open access LMU (Ludwid Maxmilian's Universitat Munchen).
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.