Javier Bravo

674 citations
32 papers · 470 · h-index 11

Impact in

Papers in

Javier Bravo

28 papers receiving 441 citations

Peers

Javier Bravo
Comparison fields: 5 of 63
  • Computer Science Applications 247
  • Information Systems 241
  • Education 157
  • Health Information Management 20
  • Artificial Intelligence 101
Replace Teresa Sancho Vinuesa with:
Teresa Sancho Vinuesa Spain
Ricardo-Adán Salas-Rueda Mexico
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Julià Minguillón Spain
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Kairit Tammets Estonia
Nick Z. Zacharis Greece
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Citations per field
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Citations per year

Countries citing papers authored by Javier Bravo

Since Specialization
Citations

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

Fields of papers citing papers by Javier Bravo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 23 scholars most cited alongside Javier Bravo, 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 Javier Bravo Line = papers co-authored together Javier Bravo links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 32 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020130
2
Recommendation in Higher Education Using Data Mining Techniques.
200985
3 202065
4 201937
5 202028
6 200521
7
Detecting Symptoms of Low Performance Using Production Rules.
200915
8 201714
9 201813
10
Improving AEH Courses through Log Analysis.
200810
11 201710
12 20177
13 20165
14
Exploring the influence of facial verification software on student academic performance in online learning environments
20154
15
A Problem-Oriented Method for Supporting AEH Authors through Data Mining
20074
16 20174
17
A Methodology for Elaborating Activities for Higher Education in 3D Virtual Worlds
20153
18
Students' perception of the importance of facial authentication software in Moodle tools
20172
19 20232
20
Using Decision Trees for Discovering Problems on Adaptive Courses
20082

About Javier Bravo

Javier Bravo is a scholar working on Computer Science Applications, Information Systems, Education, Artificial Intelligence and Developmental and Educational Psychology, having authored 32 papers that have together received 470 indexed citations. Recurring topics across this work include Online Learning and Analytics (12 papers), E-Learning and Knowledge Management (7 papers), Educational Innovations and Technology (6 papers), Data Mining Algorithms and Applications (4 papers), Online and Blended Learning (3 papers), Innovative Teaching Methods (3 papers), Intelligent Tutoring Systems and Adaptive Learning (3 papers) and Software System Performance and Reliability (2 papers). The work is most often cited by research in Computer Science Applications (247 citations), Information Systems (241 citations), Education (157 citations), Health Information Management (20 citations) and Artificial Intelligence (101 citations). Javier Bravo has collaborated with scholars based in Spain, Peru and Chile. Frequent co-authors include Francisco D. Guillén‐Gámez, Álvaro Ortigosa, Sonia Janeth Romero Martínez, David Lizcano, Rosa M. Carro, Iván García‐Magariño, Jaime Lloret, Raquel Lacuesta, J.L. Díaz-Pérez and Julián Conejo‐Mir. Their work appears in journals such as International journal of engineering education, Technology Knowledge and Learning, IEEE Communications Magazine, Online Learning and IEEE Transactions on Learning Technologies.

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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