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.
The State of Educational Data Mining in 2009: A Review and Future Visions
Better to be frustrated than bored: The incidence, persistence, and impact of learners’ cognitive–affective states during interactions with three different computer-based learning environments
This map shows the geographic impact of Ryan S. Baker'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 Ryan S. Baker with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ryan S. Baker more than expected).
This network shows the impact of papers produced by Ryan S. Baker. 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 Ryan S. Baker. The network helps show where Ryan S. Baker may publish in the future.
Co-authorship network of co-authors of Ryan S. Baker
This figure shows the co-authorship network connecting the top 25 collaborators of Ryan S. Baker.
A scholar is included among the top collaborators of Ryan S. Baker 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 Ryan S. Baker. Ryan S. Baker is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Agarwal, Deepak, et al.. (2020). Dynamic knowledge tracing through data driven recency weights.. Educational Data Mining.2 indexed citations
3.
Baker, Ryan S., et al.. (2019). A Better Cold-Start for Early Prediction of Student At-Risk Status in New School Districts.. Educational Data Mining.6 indexed citations
4.
Molenaar, Inge, et al.. (2019). Designing dashboards to support learners' self-regulated learning. Data Archiving and Networked Services (DANS). 764–775.9 indexed citations
5.
Agarwal, Deepak, et al.. (2018). Contextual Derivation of Stable BKT Parameters for Analysing Content Efficacy.. Educational Data Mining.1 indexed citations
6.
Baker, Ryan S., et al.. (2016). Classifying behavior to elucidate elegant problem solving in an educational game.. Educational Data Mining. 448–453.10 indexed citations
7.
Slater, Stefan, Ryan S. Baker, Jaclyn Ocumpaugh, et al.. (2016). Semantic Features of Math Problems: Relationships to Student Learning and Engagement.. Educational Data Mining. 223–230.4 indexed citations
8.
Rowe, Elizabeth, Ryan S. Baker, & Jodi Asbell‐Clarke. (2015). Strategic Game Moves Mediate Implicit Science Learning.. Educational Data Mining. 432–435.3 indexed citations
9.
Brown, Rebecca, Collin Lynch, Michael Eagle, et al.. (2015). Good Communities and Bad Communities: Does Membership Affect Performance?. Educational Data Mining. 612–613.5 indexed citations
10.
Baker, Ryan S., et al.. (2015). Making the Most. 31(2).2 indexed citations
11.
Baker, Ryan S., et al.. (2014). Comparing Expert and Metric-Based Assessments of Association Rule Interestingness. Educational Data Mining. 44–51.10 indexed citations
12.
Paquette, Luc, et al.. (2014). Towards Understanding Expert Coding of Student Disengagement in Online Learning.. Cognitive Science.12 indexed citations
13.
Pedro, Michael A. Sao, Janice D. Gobert, & Ryan S. Baker. (2014). The Impacts of Automatic Scaffolding on Students' Acquisition of Data Collection Inquiry Skills. Grantee Submission.3 indexed citations
14.
Jiang, Yang, et al.. (2014). Identifying Transfer of Inquiry Skills across Physical Science Simulations using Educational Data Mining. International Conference of Learning Sciences. 1. 222–229.8 indexed citations
15.
Baker, Ryan S., Sujith M. Gowda, Michael Wixon, et al.. (2012). Sensor-free automated detection of affect in a Cognitive Tutor for Algebra.. Educational Data Mining. 126–133.23 indexed citations
Baker, Ryan S., Agathe Merceron, & Philip I. Pavlik. (2010). [Proceedings of the] International Conference on Educational Data Mining (EDM) (3rd, Pittsburgh, PA, July 11-13, 2010).. Educational Data Mining.1 indexed citations
18.
Baker, Ryan S.. (2009). Differences between Intelligent Tutor Lessons, and the Choice to Go Off-Task.. Educational Data Mining. 11–20.6 indexed citations
19.
Fogarty, James, Ryan S. Baker, & Scott E. Hudson. (2005). Case studies in the use of ROC curve analysis for sensor-based estimates in human computer interaction. Graphics Interface. 129–136.88 indexed citations
20.
Tamassia, Roberto, et al.. (2001). JDSL: The data structures library in java. 26(4). 21–31.3 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.