Eva Millán

2.0k citations
24 papers · 576 · h-index 13

Impact in

Papers in

Eva Millán

23 papers receiving 525 citations

Peers

Eva Millán
Comparison fields: 5 of 81
  • Computer Science Applications 281
  • Developmental and Educational Psychology 186
  • Artificial Intelligence 316
  • Information Systems 132
  • Architecture 8
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Noboru Matsuda United States
Katerina Mangaroska Norway
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Juan Pablo de Castro Fernández Spain
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Luisa M. Regueras Spain
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Citations per field
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Citations per year

Countries citing papers authored by Eva Millán

Since Specialization
Citations

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

Fields of papers citing papers by Eva Millán

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 20 scholars most cited alongside Eva Millán, 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 Eva Millán Line = papers co-authored together Eva Millán links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 24 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2004116
2 201079
3 201774
4 200262
5 200841
6 201235
7
Discovering Student Preferences in E-Learning
200733
8 202018
9 202014
10 201414
11 200113
12 202212
13 202112
14 200111
15
Introducing Prerequisite Relations in a Multi-layered Bayesian Student Model
200510
16 20148
17 20198
18 20225
19 20163
20 20063

About Eva Millán

Eva Millán is a scholar working on Artificial Intelligence, Computer Science Applications, Mechanical Engineering, Developmental and Educational Psychology and Information Systems, having authored 24 papers that have together received 576 indexed citations. Recurring topics across this work include Intelligent Tutoring Systems and Adaptive Learning (12 papers), Online Learning and Analytics (7 papers), Design Education and Practice (5 papers), Architecture and Computational Design (4 papers), Bayesian Modeling and Causal Inference (3 papers), AI-based Problem Solving and Planning (3 papers), Educational Technology and Assessment (3 papers) and Learning Styles and Cognitive Differences (3 papers). The work is most often cited by research in Computer Science Applications (281 citations), Developmental and Educational Psychology (186 citations), Artificial Intelligence (316 citations), Information Systems (132 citations) and Architecture (8 citations). Eva Millán has collaborated with scholars based in Spain, United Kingdom and Brazil. Frequent co-authors include José-Luís Pérez-de-la-Cruz, Gladys Castillo, Rose Luckin, Mutlu Cukurova, Cristina Carmona-Duarte, Ricardo Conejo, Mónica Trella, Eduardo Guzmán, Manolis Mavrikis and Kasia Müldner. Their work appears in journals such as Computers & Education, International Journal of Artificial Intelligence in Education, IEEE Transactions on Learning Technologies, Journal of Learning Analytics and Journal of Building Engineering.

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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