Michael Mayo
Impact in
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- Online Learning and Analytics
- Teaching and Learning Programming
- Artificial Intelligence top 5%
- Intelligent Tutoring Systems and Adaptive Learning
- AI-based Problem Solving and Planning
- Bayesian Modeling and Causal Inference
Papers in
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- Intelligent Tutoring Systems and Adaptive Learning 4
- AI-based Problem Solving and Planning 4
- Bayesian Modeling and Causal Inference 3
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- Advanced Image and Video Retrieval Techniques 2
- Image Retrieval and Classification Techniques 2
- Handwritten Text Recognition Techniques 1
- Journals
- User Modeling and User-Adapted Interaction (1 paper)Research Commons (University of Waikato) (4 papers)University of Canterbury Research Repository (University of Canterbury) (1 paper)
- Partner nations
- New ZealandAustralia
In The Last Decade
Michael Mayo
7 papers receiving 223 citations
Peers
Comparison fields: 5 of 35
- Computer Science Applications 83
- Artificial Intelligence 221
- Developmental and Educational Psychology 60
- Information Systems 48
- Software 8
Countries citing papers authored by Michael Mayo
This map shows the geographic impact of Michael Mayo'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 Michael Mayo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Mayo more than expected).
Fields of papers citing papers by Michael Mayo
This network shows the impact of papers produced by Michael Mayo. 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 Michael Mayo. The network helps show where Michael Mayo may publish in the future.
Co-authors
The 2 scholars most cited alongside Michael Mayo, 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 | Optimising ITS Behaviour with Bayesian Networks and Decision Theory | 2001 | 116 |
| 2 | 2002 | 74 | |
| 3 | 2008 | 33 | |
| 4 | 2002 | 19 | |
| 5 | Symbol grounding and its implications for artificial intelligence | 2003 | 14 |
| 6 | 2001 | 11 | |
| 7 | 2008 | 2 |
About Michael Mayo
Michael Mayo is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Theory and Mathematics, Cultural Studies and Developmental and Educational Psychology, having authored 7 papers that have together received 269 indexed citations. Recurring topics across this work include Intelligent Tutoring Systems and Adaptive Learning (4 papers), AI-based Problem Solving and Planning (4 papers), Bayesian Modeling and Causal Inference (3 papers), Advanced Image and Video Retrieval Techniques (2 papers), Image Retrieval and Classification Techniques (2 papers), Handwritten Text Recognition Techniques (1 paper), Innovative Teaching and Learning Methods (1 paper) and Language and cultural evolution (1 paper). The work is most often cited by research in Computer Science Applications (83 citations), Artificial Intelligence (221 citations), Developmental and Educational Psychology (60 citations), Information Systems (48 citations) and Software (8 citations). Michael Mayo has collaborated with scholars based in New Zealand and Australia. Frequent co-authors include Antonija Mitrović and Brent Martin. Their work appears in journals such as User Modeling and User-Adapted Interaction, Research Commons (University of Waikato) and University of Canterbury Research Repository (University of Canterbury).
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.