Doug Markant
Impact in
- General Decision Sciences top 10%
- Decision-Making and Behavioral Economics
Papers in
-
- Machine Learning and Algorithms 2
- Data Stream Mining Techniques 1
- Artificial Intelligence in Games 1
- Evolutionary Algorithms and Applications 1
- Intelligent Tutoring Systems and Adaptive Learning 1
- Co-authors
- Todd M. Gureckis (3 shared papers)David Halpern (1 shared paper)John V. McDonnell (1 shared paper)Jay B. Martin (1 shared paper)Anna Coenen (1 shared paper)Alexander Rich (1 shared paper)Patricia P. Chan (1 shared paper)Jessica B. Hamrick (1 shared paper)
- Journals
- Cognitive Science (2 papers)Behavior Research Methods (1 paper)NeuroImage (1 paper)eScholarship (California Digital Library) (2 papers)
- Partner nations
- United StatesCanadaGermany
In The Last Decade
Doug Markant
5 papers receiving 198 citations
Peers
Comparison fields: 5 of 63
- General Decision Sciences 20
- Computational Mathematics 4
- Cognitive Neuroscience 92
- Developmental and Educational Psychology 40
- Computer Science Applications 13
Countries citing papers authored by Doug Markant
This map shows the geographic impact of Doug Markant'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 Doug Markant with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Doug Markant more than expected).
Fields of papers citing papers by Doug Markant
This network shows the impact of papers produced by Doug Markant. 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 Doug Markant. The network helps show where Doug Markant may publish in the future.
Co-authors
The 17 scholars most cited alongside Doug Markant, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2015 | 136 | |
| 2 | 2008 | 41 | |
| 3 | Active learning strategies in a spatial concept learning game | 2009 | 26 |
| 4 | Category Learning Through Active Sampling | 2010 | 13 |
| 5 | The impact of biased hypothesis generation on self-directed learning. | 2016 | 1 |
| 6 | Navigating the "chain of command": Enhanced integrative encoding through active control of study. | 2019 | 0 |
About Doug Markant
Doug Markant is a scholar working on Artificial Intelligence, Education, Developmental and Educational Psychology, Statistics and Probability and Pediatrics, Perinatology and Child Health, having authored 6 papers that have together received 217 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (2 papers), Data Stream Mining Techniques (1 paper), Advanced Neuroimaging Techniques and Applications (1 paper), Artificial Intelligence in Games (1 paper), Evolutionary Algorithms and Applications (1 paper), Intelligent Tutoring Systems and Adaptive Learning (1 paper), Tensor decomposition and applications (1 paper) and Fetal and Pediatric Neurological Disorders (1 paper). The work is most often cited by research in General Decision Sciences (20 citations), Computational Mathematics (4 citations), Cognitive Neuroscience (92 citations), Developmental and Educational Psychology (40 citations) and Computer Science Applications (13 citations). Doug Markant has collaborated with scholars based in United States, Canada and Germany. Frequent co-authors include Todd M. Gureckis, David Halpern, John V. McDonnell, Jay B. Martin, Anna Coenen, Alexander Rich, Patricia P. Chan, Jessica B. Hamrick, Marc Niethammer and Martha E. Shenton. Their work appears in journals such as Cognitive Science, Behavior Research Methods, NeuroImage and eScholarship (California Digital Library).
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.