H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 39 Citations 15,057 102 World Ranking 4805 National Ranking 57

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Data mining

His main research concerns Educational data mining, Data science, Artificial intelligence, Association rule learning and Machine learning. His studies deal with areas such as Anomaly detection and Data mining as well as Educational data mining. His Data science research is multidisciplinary, incorporating perspectives in Field, Concept mining, Web mining and Visualization.

His Association rule learning research includes themes of Learning Management, Collaborative filtering and Cluster analysis. Cristóbal Romero works mostly in the field of Machine learning, limiting it down to topics relating to Usage data and, in certain cases, Classifier, E-learning and Recommender system. His Genetic programming study combines topics from a wide range of disciplines, such as Evolutionary algorithm, High dimensional and Knowledge extraction.

His most cited work include:

  • Educational Data Mining: A Review of the State of the Art (1189 citations)
  • Educational data mining: A survey from 1995 to 2005 (935 citations)
  • KEEL: a software tool to assess evolutionary algorithms for data mining problems (907 citations)

What are the main themes of his work throughout his whole career to date?

Cristóbal Romero mainly focuses on Artificial intelligence, Machine learning, Educational data mining, Data science and Data mining. In Artificial intelligence, Cristóbal Romero works on issues like Usage data, which are connected to Classifier and Data mining algorithm. The study incorporates disciplines such as Preprocessor and Knowledge extraction in addition to Machine learning.

His biological study spans a wide range of topics, including Learning Management, Learning analytics, Web mining and Library science. His work is dedicated to discovering how Data science, Collaborative learning are connected with Educational technology and other disciplines. He has researched Association rule learning in several fields, including Class, Instructional design and Virtual learning environment, World Wide Web.

He most often published in these fields:

  • Artificial intelligence (39.34%)
  • Machine learning (31.15%)
  • Educational data mining (31.97%)

What were the highlights of his more recent work (between 2016-2021)?

  • Artificial intelligence (39.34%)
  • Educational data mining (31.97%)
  • Machine learning (31.15%)

In recent papers he was focusing on the following fields of study:

The scientist’s investigation covers issues in Artificial intelligence, Educational data mining, Machine learning, Blended learning and Data science. His research on Artificial intelligence frequently links to adjacent areas such as Natural language processing. His research integrates issues of Learning analytics and World Wide Web in his study of Educational data mining.

His work on Sensor fusion, Statistical classification, Predictive modelling and Genetic algorithm as part of general Machine learning study is frequently connected to Multi criteria, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. His Blended learning research is multidisciplinary, relying on both Educational assessment, Game based learning, Distance education and Library science. In his works, he conducts interdisciplinary research on Data science and Process mining.

Between 2016 and 2021, his most popular works were:

  • A survey on educational process mining (57 citations)
  • Educational data science in massive open online courses (43 citations)
  • Educational data mining and learning analytics: An updated survey (35 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Programming language

His primary areas of study are Data science, Process, World Wide Web, Learning analytics and Educational data mining. His Data science study incorporates themes from Text mining and Event. His Process research spans across into fields like Data mining and Management system.

His work in the fields of Thesaurus and Massive open online course overlaps with other areas such as Skepticism and Application areas. Cristóbal Romero has included themes like Set, Cluster analysis, Interface, Knowledge extraction and Analytics in his Learning analytics study. His Educational data mining study integrates concerns from other disciplines, such as Domain, Field, Graph and Representation.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Educational Data Mining: A Review of the State of the Art

Cristóbal Romero;Sebastián Ventura.
systems man and cybernetics (2010)

2195 Citations

Educational data mining: A survey from 1995 to 2005

C. Romero;S. Ventura.
Expert Systems With Applications (2007)

2017 Citations

Data mining in course management systems: Moodle case study and tutorial

Cristóbal Romero;Sebastián Ventura;Enrique García.
Computer Education (2008)

1449 Citations

KEEL: a software tool to assess evolutionary algorithms for data mining problems

J. Alcalá-Fdez;L. Sánchez;S. García;M. J. del Jesus.
soft computing (2008)

1209 Citations

Data mining in education

Cristobal Romero;Sebastian Ventura.
Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery (2013)

868 Citations

Predicting students' final performance from participation in on-line discussion forums

Cristóbal Romero;Manuel-Ignacio López;Jose-María Luna;Sebastián Ventura.
Computer Education (2013)

541 Citations

Handbook of Educational Data Mining

Cristobal Romero;Sebastian Ventura;Mykola Pechenizkiy;Ryan S.J.d. Baker.
Chapman and Hall/CRC data mining and knowledge discovery series (2010)

501 Citations

Data Mining Algorithms to Classify Students

Cristóbal Romero;Sebastián Ventura;Pedro G. Espejo;César Hervás.
educational data mining (2008)

498 Citations

Web usage mining for predicting final marks of students that use Moodle courses

Cristobal Romero;Cristobal Romero;Pedro G. Espejo;Amelia Zafra;Amelia Zafra;Jose Raul Romero.
Computer Applications in Engineering Education (2013)

324 Citations

Knowledge Discovery with Genetic Programming for Providing Feedback to Courseware Authors

Cristóbal Romero;Sebastián Ventura;Paul De Bra.
User Modeling and User-adapted Interaction (2005)

240 Citations

If you think any of the details on this page are incorrect, let us know.

Contact us

Best Scientists Citing Cristóbal Romero

Francisco Herrera

Francisco Herrera

University of Granada

Publications: 137

Sebastián Ventura

Sebastián Ventura

University of Córdoba

Publications: 85

Salvador García

Salvador García

University of Granada

Publications: 45

Alberto Fernández

Alberto Fernández

University of Granada

Publications: 41

Ryan S. Baker

Ryan S. Baker

University of Pennsylvania

Publications: 36

Humberto Bustince

Humberto Bustince

Universidad Publica De Navarra

Publications: 28

Dragan Gasevic

Dragan Gasevic

Monash University

Publications: 28

Kenneth R. Koedinger

Kenneth R. Koedinger

Carnegie Mellon University

Publications: 21

Ali Asghar Heidari

Ali Asghar Heidari

National University of Singapore

Publications: 19

José Manuel Benítez

José Manuel Benítez

University of Granada

Publications: 17

Shane Dawson

Shane Dawson

University of South Australia

Publications: 16

Abelardo Pardo

Abelardo Pardo

University of South Australia

Publications: 15

Francisco José García-Peñalvo

Francisco José García-Peñalvo

University of Salamanca

Publications: 15

Huiling Chen

Huiling Chen

Wenzhou University

Publications: 14

Hisao Ishibuchi

Hisao Ishibuchi

Southern University of Science and Technology

Publications: 14

Yusuke Nojima

Yusuke Nojima

Osaka Prefecture University

Publications: 14

Something went wrong. Please try again later.