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.
Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework
2013359 citationsPratim Sengupta, John S. Kinnebrew 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 Pratim Sengupta
Since
Specialization
Citations
This map shows the geographic impact of Pratim Sengupta'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 Pratim Sengupta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pratim Sengupta more than expected).
This network shows the impact of papers produced by Pratim Sengupta. 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 Pratim Sengupta. The network helps show where Pratim Sengupta may publish in the future.
Co-authorship network of co-authors of Pratim Sengupta
This figure shows the co-authorship network connecting the top 25 collaborators of Pratim Sengupta.
A scholar is included among the top collaborators of Pratim Sengupta 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 Pratim Sengupta. Pratim Sengupta is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Shanahan, Marie‐Claire, et al.. (2020). Centering and Decentering Participation in Public Computing Through Co-operative Action.. ICLS.3 indexed citations
7.
Shanahan, Marie‐Claire, et al.. (2020). Improvisational Infrastructuring by Facilitators in Public Computing.. ICLS.
8.
Farris, Amy Voss, Amanda Dickes, & Pratim Sengupta. (2020). Grounding computational abstractions in scientific experience. International Conference of Learning Sciences. 1333–1340.4 indexed citations
Sengupta, Pratim & Marie‐Claire Shanahan. (2017). Boundary play and pivots in public computation: new directions in STEM education. International journal of engineering education. 33(3). 1124–1134.6 indexed citations
Sengupta, Pratim & Uri Wilensky. (2016). Understanding Electric Current Using Agent-based Models: Connecting the Micro-level with Flow Rate.. 216–227.1 indexed citations
14.
Farris, Amy Voss, Amanda Dickes, & Pratim Sengupta. (2016). Development of Disciplined Interpretation Using Computational Modeling in the Elementary Science Classroom. arXiv (Cornell University). 282–289.3 indexed citations
15.
Sengupta, Pratim, et al.. (2016). Playing Modeling Games in the Science Classroom: The Case for Disciplinary Integration.. Educational Technology archive. 56(3). 16–22.9 indexed citations
16.
Farris, Amy Voss & Pratim Sengupta. (2014). Perspectival computational thinking for learning physics: A case study of collaborative agent-based modeling. International Conference of Learning Sciences. 2. 1102–1106.11 indexed citations
17.
Basu, Satabdi, et al.. (2014). Investigating Student Generated Computational Models of Science.. ICLS.8 indexed citations
18.
Clark, Douglas B., Stephen S. Killingsworth, Mario Martinez-Garza, et al.. (2013). Digital Games and Science Learning: Design Principles and Processes to Augment Commercial Game Design Conventions..1 indexed citations
19.
Wilkerson, Michelle Hoda, Pratim Sengupta, & Uri Wilensky. (2008). Perceptual supports for sensemaking: a case study using multi agent based computational learning environments. International Conference of Learning Sciences. 151–152.1 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.