Shawn Betts

413 total citations
24 papers, 274 citations indexed

About

Shawn Betts is a scholar working on Artificial Intelligence, Developmental and Educational Psychology and Cognitive Neuroscience. According to data from OpenAlex, Shawn Betts has authored 24 papers receiving a total of 274 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 13 papers in Developmental and Educational Psychology and 9 papers in Cognitive Neuroscience. Recurrent topics in Shawn Betts's work include Intelligent Tutoring Systems and Adaptive Learning (10 papers), Visual and Cognitive Learning Processes (8 papers) and Innovative Teaching and Learning Methods (6 papers). Shawn Betts is often cited by papers focused on Intelligent Tutoring Systems and Adaptive Learning (10 papers), Visual and Cognitive Learning Processes (8 papers) and Innovative Teaching and Learning Methods (6 papers). Shawn Betts collaborates with scholars based in United States, South Korea and Switzerland. Shawn Betts's co-authors include John R. Anderson, Jon M. Fincham, Jennifer L. Ferris, Hee Seung Lee, Daniel Bothell, Christian Lebière, Aryn Pyke, Hee Seung Lee, Jian Yang and Dan Bothell and has published in prestigious journals such as Proceedings of the National Academy of Sciences, PLoS ONE and NeuroImage.

In The Last Decade

Shawn Betts

22 papers receiving 262 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Shawn Betts United States	10	125	103	102	78	75	24	274
Thomas G. Holzman United States	8	78 0.6×	79 0.8×	126 1.2×	69 0.9×	100 1.3×	16	298
Irina V. Kapler Canada	4	90 0.7×	81 0.8×	129 1.3×	9 0.1×	67 0.9×	5	247
Rebecca Boncoddo United States	7	60 0.5×	25 0.2×	116 1.1×	23 0.3×	62 0.8×	12	258
Aryn Pyke United States	9	61 0.5×	35 0.3×	101 1.0×	241 3.1×	52 0.7×	23	368
Moira Rose Dillon United States	9	70 0.6×	20 0.2×	95 0.9×	102 1.3×	29 0.4×	20	241
Yi Qian China	9	88 0.7×	67 0.7×	124 1.2×	60 0.8×	16 0.2×	24	252
Kay-Michael Würzner Germany	7	206 1.6×	111 1.1×	246 2.4×	16 0.2×	86 1.1×	12	416
Eun Hee Jeon United States	4	70 0.6×	88 0.9×	312 3.1×	25 0.3×	21 0.3×	4	376
David W. Braithwaite United States	13	34 0.3×	61 0.6×	175 1.7×	318 4.1×	95 1.3×	33	483
Scott C. Stoness United States	6	52 0.4×	135 1.3×	45 0.4×	8 0.1×	57 0.8×	9	231

Countries citing papers authored by Shawn Betts

Since Specialization

Citations

This map shows the geographic impact of Shawn Betts'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 Shawn Betts with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shawn Betts more than expected).

Fields of papers citing papers by Shawn Betts

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shawn Betts. 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 Shawn Betts. The network helps show where Shawn Betts may publish in the future.

Co-authorship network of co-authors of Shawn Betts

This figure shows the co-authorship network connecting the top 25 collaborators of Shawn Betts. A scholar is included among the top collaborators of Shawn Betts 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 Shawn Betts. Shawn Betts is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Anderson, John R., et al.. (2024). Tight resource-rational analysis. Cognitive Systems Research. 86. 101239–101239. 1 indexed citations

Betts, Shawn, et al.. (2024). Periodic tapping mechanisms of skill learning in a fast-paced video game.. Journal of Experimental Psychology Human Perception & Performance. 50(1). 39–63.

Anderson, John R., et al.. (2023). An Integrated Model of Collaborative Skill Acquisition: Anticipation, Control Tuning, and Role Adoption. Cognitive Science. 47(7). e13303–e13303. 1 indexed citations

Betts, Shawn, et al.. (2021). Cognitive & motor skill transfer across speeds: A video game study. PLoS ONE. 16(10). e0258242–e0258242. 4 indexed citations

Anderson, John R., Shawn Betts, Daniel Bothell, & Christian Lebière. (2021). Discovering skill. Cognitive Psychology. 129. 101410–101410. 3 indexed citations

Betts, Shawn, et al.. (2021). A decay-based account of learning and adaptation in complex skills.. Journal of Experimental Psychology Learning Memory and Cognition. 47(11). 1761–1791. 3 indexed citations

Anderson, John R., et al.. (2020). Reconstructing fine-grained cognition from brain activity. NeuroImage. 221. 116999–116999. 2 indexed citations

Anderson, John R., et al.. (2019). Learning rapid and precise skills.. Psychological Review. 126(5). 727–760. 19 indexed citations

Fincham, Jon M., John R. Anderson, Shawn Betts, & Jennifer L. Ferris. (2018). Using Neural Imaging and Cognitive Modeling to Infer Mental States while Using an Intelligent Tutoring System. Research Showcase @ Carnegie Mellon University (Carnegie Mellon University). 51–60. 2 indexed citations

10.

Anderson, John R., Jon M. Fincham, Jennifer L. Ferris, & Shawn Betts. (2018). Can Neural Imaging Investigate Learning in an Educational Task?. Research Showcase @ Carnegie Mellon University (Carnegie Mellon University).

11.

Lee, Hee Seung, Shawn Betts, & John R. Anderson. (2016). Embellishing Problem-Solving Examples with Deep Structure Information Facilitates Transfer. The Journal of Experimental Education. 85(2). 309–333. 13 indexed citations

12.

Fincham, Jon M., et al.. (2015). End effects and cross-dimensional interference in identification of time and length: Evidence for a common memory mechanism. Cognitive Affective & Behavioral Neuroscience. 15(3). 680–695. 10 indexed citations

13.

Lee, Hee Seung, Shawn Betts, & John R. Anderson. (2015). Not taking the easy road: When similarity hurts learning. Memory & Cognition. 43(6). 939–952. 10 indexed citations

14.

Pyke, Aryn, Shawn Betts, Jon M. Fincham, & John R. Anderson. (2014). Visuospatial referents facilitate the learning and transfer of mathematical operations: Extending the role of the angular gyrus. Cognitive Affective & Behavioral Neuroscience. 15(1). 229–250. 9 indexed citations

15.

Lee, Hee Seung, Jon M. Fincham, Shawn Betts, & John R. Anderson. (2014). An fMRI investigation of instructional guidance in mathematical problem solving. Trends in Neuroscience and Education. 3(2). 50–62. 9 indexed citations

16.

Betts, Shawn, et al.. (2012). Brain Networks Supporting Execution of Mathematical Skills versus Acquisition of New Mathematical Competence. PLoS ONE. 7(12). e50154–e50154. 12 indexed citations

17.

Lee, Hee Seung, et al.. (2011). When Does Provision of Instruction Promote Learning. Cognitive Science. 33(33). 4 indexed citations

18.

Anderson, John R., Shawn Betts, Jennifer L. Ferris, & Jon M. Fincham. (2011). Tracking children's mental states while solving algebra equations. Human Brain Mapping. 33(11). 2650–2665. 26 indexed citations

19.

Anderson, John R., Shawn Betts, Jennifer L. Ferris, & Jon M. Fincham. (2010). Cognitive and metacognitive activity in mathematical problem solving: prefrontal and parietal patterns. Cognitive Affective & Behavioral Neuroscience. 11(1). 52–67. 48 indexed citations

20.

Betts, Shawn, et al.. (2009). Practice enables successful learning under minimal guidance.. Journal of Educational Psychology. 101(4). 790–802. 35 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.

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