Eric Melz
Impact in
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- Child and Animal Learning Development
Papers in
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- Topic Modeling 2
- Natural Language Processing Techniques 1
- Intelligent Tutoring Systems and Adaptive Learning 1
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- Multimodal Machine Learning Applications 1
- Co-authors
- Keith J. Holyoak (3 shared papers)Laura R. Novick (1 shared paper)Yolanda Gil (1 shared paper)Thomas D. Wickens (1 shared paper)Trent E. Lange (1 shared paper)Charles M. Wharton (1 shared paper)Paul E. Downing (1 shared paper)Patricia W. Cheng (1 shared paper)
- Journals
- Cognitive Psychology (1 paper)Journal of Experimental Psychology Learning Memory and Cognition (1 paper)National Conference on Artificial Intelligence (1 paper)Text REtrieval Conference (1 paper)
- Partner nations
- United States
In The Last Decade
Eric Melz
5 papers receiving 193 citations
Peers
Comparison fields: 5 of 53
- Developmental and Educational Psychology 101
- General Decision Sciences 10
- Artificial Intelligence 135
- Experimental and Cognitive Psychology 39
- History and Philosophy of Science 9
Countries citing papers authored by Eric Melz
This map shows the geographic impact of Eric Melz'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 Eric Melz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eric Melz more than expected).
Fields of papers citing papers by Eric Melz
This network shows the impact of papers produced by Eric Melz. 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 Eric Melz. The network helps show where Eric Melz may publish in the future.
Co-authors
The 14 scholars most cited alongside Eric Melz, 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 | 1994 | 70 | |
| 2 | Component processes in analogical transfer: Mapping, pattern completion, and adaptation. | 1994 | 67 |
| 3 | Explicit representations of problem-solving strategies to support knowledge acquisition | 1996 | 42 |
| 4 | 1993 | 34 | |
| 5 | Multiple-Engine Question Answering in TextMap. | 2003 | 28 |
About Eric Melz
Eric Melz is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems and Management, Cognitive Neuroscience and Experimental and Cognitive Psychology, having authored 5 papers that have together received 241 indexed citations. Recurring topics across this work include Topic Modeling (2 papers), Memory Processes and Influences (1 paper), Natural Language Processing Techniques (1 paper), Mental Health Research Topics (1 paper), Personal Information Management and User Behavior (1 paper), Multimodal Machine Learning Applications (1 paper), Spreadsheets and End-User Computing (1 paper) and Intelligent Tutoring Systems and Adaptive Learning (1 paper). The work is most often cited by research in Developmental and Educational Psychology (101 citations), General Decision Sciences (10 citations), Artificial Intelligence (135 citations), Experimental and Cognitive Psychology (39 citations) and History and Philosophy of Science (9 citations). Eric Melz has collaborated with scholars based in United States. Frequent co-authors include Keith J. Holyoak, Laura R. Novick, Yolanda Gil, Thomas D. Wickens, Trent E. Lange, Charles M. Wharton, Paul E. Downing, Patricia W. Cheng, Michael R. Waldmann and Abdessamad Echihabi. Their work appears in journals such as Cognitive Psychology, Journal of Experimental Psychology Learning Memory and Cognition, National Conference on Artificial Intelligence and Text REtrieval Conference.
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.