Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Predicting students' final performance from participation in on-line discussion forums
2013400 citationsCristóbal Romero, José María Luna et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by José María Luna
Since
Specialization
Citations
This map shows the geographic impact of José María Luna'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 José María Luna with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites José María Luna more than expected).
This network shows the impact of papers produced by José María Luna. 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 José María Luna. The network helps show where José María Luna may publish in the future.
Co-authorship network of co-authors of José María Luna
This figure shows the co-authorship network connecting the top 25 collaborators of José María Luna.
A scholar is included among the top collaborators of José María Luna based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with José María Luna. José María Luna is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Cano, Alberto, José María Luna, Eva Gibaja, & Sebastián Ventura. (2015). LAIM discretization for multi-label data. Information Sciences. 330. 370–384.34 indexed citations
13.
Cano, Alberto, José María Luna, Amelia Zafra, & Sebastián Ventura. (2015). A classification module for genetic programming algorithms in JCLEC. Journal of Machine Learning Research. 16(1). 491–494.16 indexed citations
14.
Bravo, Javier, et al.. (2015). Exploring the Influence of ICT in online Education Through Data Mining Tools.. Educational Data Mining. 540–543.1 indexed citations
15.
Gabor, Manuela Rozalia, et al.. (2014). THE SKILL MATCH CHALLENGE. EVIDENCES FROM THE SMART PROJECT. 1182–1189.1 indexed citations
16.
Romero, Cristóbal, et al.. (2012). Classification via clustering for predicting final marks starting from the student participation in Forums.. Educational Data Mining. 148–151.19 indexed citations
17.
Luna, José María, et al.. (2012). Classification via Clustering for Predicting Final Marks Based on Student Participation in Forums.. Educational Data Mining.78 indexed citations
18.
Romero, Cristóbal, et al.. (2012). Meta-learning Approach for Automatic Parameter Tuning: A case of study with educational datasets.. Educational Data Mining. 180–183.13 indexed citations
19.
Romero, Cristóbal, José Raúl Romero, José María Luna, & Sebastián Ventura. (2010). Mining Rare Association Rules from e-Learning Data.. Educational Data Mining. 171–180.53 indexed citations
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.