Mia Minnes
- Computer Science Applications top 5%
- Education
- Developmental and Educational Psychology
- Gender Studies
- Media Technology top 10%
- Co-authors
- Christine AlvaradoLeo PorterBakhadyr KhoussainovJoe Gibbs PolitzKristen VaccaroJiamou LiuNathan DelsonMarco Carmosino
- Topics
- Online Learning and Analytics (9 papers)Teaching and Learning Programming (9 papers)Innovative Teaching and Learning Methods (5 papers)
- Journals
- Theoretical Computer ScienceJournal of Symbolic LogicACM Transactions on Computing Education
- Partner nations
- United StatesNew ZealandCanada
In The Last Decade
Mia Minnes
26 papers receiving 167 citations
Peers
Comparison fields: 5 of 39
- Computer Science Applications 100
- Education 55
- Developmental and Educational Psychology 37
- Gender Studies 34
- Media Technology 30
Countries citing papers authored by Mia Minnes
This map shows the geographic impact of Mia Minnes'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 Mia Minnes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mia Minnes more than expected).
Fields of papers citing papers by Mia Minnes
This network shows the impact of papers produced by Mia Minnes. 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 Mia Minnes. The network helps show where Mia Minnes may publish in the future.
Co-authorship network of co-authors of Mia Minnes
This figure shows the co-authorship network connecting the top 25 collaborators of Mia Minnes. A scholar is included among the top collaborators of Mia Minnes 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 Mia Minnes. Mia Minnes is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 3 | |
| 3 | 2 | |
| 4 | 0 | |
| 5 | 2 | |
| 6 | 1 | |
| 7 | 8 | |
| 8 | 10 | |
| 9 | 2 | |
| 10 | 4 | |
| 11 | 3 | |
| 12 | 3 | |
| 13 | 0 | |
| 14 | 6 | |
| 15 | 4 | |
| 16 | 2 | |
| 17 | Practice Makes Deeper? Regular Reflective Writing during Engineering Internships | 7 |
| 18 | What is Decidable about Strings | 1 |
| 19 | 3 | |
| 20 | 3 |
About Mia Minnes
Mia Minnes is a scholar working on Computer Science Applications, Developmental and Educational Psychology and Safety Research, having authored 28 papers that have together received 174 indexed citations. Recurring topics across this work include Online Learning and Analytics (9 papers), Teaching and Learning Programming (9 papers) and Innovative Teaching and Learning Methods (5 papers). The work is most often cited by research in Computer Science Applications (100 citations), Gender Studies (34 citations) and Media Technology (30 citations). Mia Minnes has collaborated with scholars based in United States, New Zealand and Canada. Frequent co-authors include Christine Alvarado, Leo Porter, Bakhadyr Khoussainov, Joe Gibbs Politz, Kristen Vaccaro, Jiamou Liu, Nathan Delson, Marco Carmosino, Jace Hargıs and Maziar Ghazinejad. Their work appears in journals such as Theoretical Computer Science, Journal of Symbolic Logic and ACM Transactions on Computing Education.
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.