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.
This map shows the geographic impact of Daniel Seaton'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 Daniel Seaton with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Seaton more than expected).
This network shows the impact of papers produced by Daniel Seaton. 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 Daniel Seaton. The network helps show where Daniel Seaton may publish in the future.
Co-authorship network of co-authors of Daniel Seaton
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Seaton.
A scholar is included among the top collaborators of Daniel Seaton 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 Daniel Seaton. Daniel Seaton is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Davis, Dan, Daniel Seaton, Claudia Hauff, & Geert‐Jan Houben. (2018). Toward large-scale learning design. Research Repository (Delft University of Technology). 1–10.12 indexed citations
Lopez, Glenn, et al.. (2017). Google BigQuery for Education. DSpace@MIT (Massachusetts Institute of Technology). 181–184.18 indexed citations
6.
Coleman, Cody, Daniel Seaton, & Isaac L. Chuang. (2015). Probabilistic Use Cases. 141–148.36 indexed citations
7.
Seaton, Daniel, Yoav Bergner, Isaac L. Chuang, Piotr Mitros, & David E. Pritchard. (2014). Who does what in a massive open online course. DSpace@MIT (Massachusetts Institute of Technology).3 indexed citations
8.
Seaton, Daniel, Sergiy Nesterko, Tommy Mullaney, et al.. (2014). Characterizing Video Use in the Catalogue of MITx MOOCs. 4.27 indexed citations
Seaton, Daniel, Yoav Bergner, & David E. Pritchard. (2013). Exploring the relationship between course structure and etext usage in blended and open online courses.. Educational Data Mining. 350–351.4 indexed citations
13.
Pardos, Zachary A., Yoav Bergner, Daniel Seaton, & David E. Pritchard. (2013). Adapting Bayesian Knowledge Tracing to a Massive Open Online Course in edX. Educational Data Mining. 137–144.51 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.