Tanja Käser

46 papers receiving 572 citations

Tanja Käser's Hit Papers

The critical role of trust in adopting AI-powered educational technology for learning: An instrument for measuring student perceptions 2025 · 31 citations
310Years since publication102030

Peers

Tanja Käser
Comparison fields: 5 of 64
  • Computer Science Applications 236
  • Developmental and Educational Psychology 182
  • Statistics and Probability 119
  • Health Informatics 17
  • Artificial Intelligence 279
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Countries citing papers authored by Tanja Käser

Since Specialization
Citations

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

Fields of papers citing papers by Tanja Käser

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Tanja Käser, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Tanja Käser Line = papers co-authored together Tanja Käser links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 53 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2017113
2 201371
3
The critical role of trust in adopting AI-powered educational technology for learning: An instrument for measuring student perceptions
Hit paper breakdown →
202531
4 201729
5 202421
6 202320
7 202018
8 202017
9 201316
10 202214
11 201314
12 201614
13 201213
14 202213
15
Different parameters - same prediction. An analysis of learning curves
201413
16 202212
17 202212
18 202311
19
Temporally Coherent Clustering of Student Data.
201611
20 201611

About Tanja Käser

Tanja Käser is a scholar working on Artificial Intelligence, Computer Science Applications, Developmental and Educational Psychology, Education and Statistics and Probability, having authored 53 papers that have together received 608 indexed citations. Recurring topics across this work include Online Learning and Analytics (19 papers), Intelligent Tutoring Systems and Adaptive Learning (13 papers), Innovative Teaching and Learning Methods (11 papers), Cognitive and developmental aspects of mathematical skills (10 papers), Mathematics Education and Teaching Techniques (7 papers), Topic Modeling (5 papers), Reading and Literacy Development (5 papers) and Educational Games and Gamification (4 papers). The work is most often cited by research in Computer Science Applications (236 citations), Developmental and Educational Psychology (182 citations), Statistics and Probability (119 citations), Health Informatics (17 citations) and Artificial Intelligence (279 citations). Tanja Käser has collaborated with scholars based in Switzerland, Germany and United States. Frequent co-authors include Markus Groß, Severin Klingler, Alexander G. Schwing, Karin Kucian, Michael von Aster, Juliane Kohn, Daniel L. Schwartz, Thiemo Wambsganß, Gian-Marco Baschera and Mirko Marras. Their work appears in journals such as International Journal of Artificial Intelligence in Education, Frontiers in Psychology, Proceedings of the ACM on Human-Computer Interaction, IEEE Transactions on Learning Technologies and British Journal of Educational Technology.

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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