Josh Gardner
Impact in
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- Online Learning and Analytics
- E-Learning and Knowledge Management
Papers in
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- Online Learning and Analytics 7
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- Machine Learning and Data Classification 4
- Explainable Artificial Intelligence (XAI) 2
- Imbalanced Data Classification Techniques 1
- Co-authors
- Christopher Brooks (8 shared papers)Ryan S. Baker (3 shared papers)Yuming Yang (1 shared paper)Arya Farahi (1 shared paper)Danai Koutra (1 shared paper)Ludwig Schmidt (1 shared paper)Adrienne Decker (1 shared paper)Robert Culbertson (1 shared paper)
- Journals
- arXiv (Cornell University) (1 paper)Educational Data Mining (2 papers)
- Partner nations
- United States
In The Last Decade
Josh Gardner
12 papers receiving 147 citations
Peers
Comparison fields: 5 of 54
- Computer Science Applications 90
- Health Informatics 7
- Safety Research 27
- Artificial Intelligence 84
- Developmental and Educational Psychology 14
Countries citing papers authored by Josh Gardner
This map shows the geographic impact of Josh Gardner'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 Josh Gardner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Josh Gardner more than expected).
Fields of papers citing papers by Josh Gardner
This network shows the impact of papers produced by Josh Gardner. 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 Josh Gardner. The network helps show where Josh Gardner may publish in the future.
Co-authors
The 13 scholars most cited alongside Josh Gardner, 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 | 2019 | 97 | |
| 2 | 2018 | 16 | |
| 3 | 2018 | 14 | |
| 4 | Modeling and Experimental Design for MOOC Dropout Prediction: A Replication Perspective. | 2019 | 8 |
| 5 | 2017 | 6 | |
| 6 | 2020 | 5 | |
| 7 | Statistical Approaches to the Model Comparison Task in Learning Analytics. | 2017 | 3 |
| 8 | 2007 | 2 | |
| 9 | 2024 | 2 | |
| 10 | 2020 | 1 | |
| 11 | 2018 | 1 | |
| 12 | Statistical Testing for AUC Data [R package auctestr version 1.0.0] | 2017 | 1 |
| 13 | Toward Replicable Predictive Model Evaluation in MOOCs. | 2017 | 0 |
About Josh Gardner
Josh Gardner is a scholar working on Computer Science Applications, Artificial Intelligence, Computer Networks and Communications, Software and Control and Systems Engineering, having authored 13 papers that have together received 156 indexed citations. Recurring topics across this work include Online Learning and Analytics (7 papers), Machine Learning and Data Classification (4 papers), Software System Performance and Reliability (3 papers), Software Testing and Debugging Techniques (2 papers), Explainable Artificial Intelligence (XAI) (2 papers), Imbalanced Data Classification Techniques (1 paper), Model-Driven Software Engineering Techniques (1 paper) and Traffic Prediction and Management Techniques (1 paper). The work is most often cited by research in Computer Science Applications (90 citations), Health Informatics (7 citations), Safety Research (27 citations), Artificial Intelligence (84 citations) and Developmental and Educational Psychology (14 citations). Josh Gardner has collaborated with scholars based in United States. Frequent co-authors include Christopher Brooks, Ryan S. Baker, Yuming Yang, Arya Farahi, Danai Koutra, Ludwig Schmidt, Adrienne Decker, Robert Culbertson, Brian M. McSkimming and Michael Oehrtman. Their work appears in journals such as arXiv (Cornell University) and Educational Data Mining.
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.