Earl J. Schweppe
- Computer Science Applications top 1%
- Information Systems top 10%
- Artificial Intelligence
- Computational Theory and Mathematics top 10%
- Computer Networks and Communications top 10%
- Co-authors
- Samuel D. ConteJohn W. HamblenThomas A. KeenanDavid M. YoungWilliam F. AtchisonE.J. McCluskeyWerner C. RheinboldtSilvio O. Navarro
- Topics
- Teaching and Learning Programming (8 papers)Experimental Learning in Engineering (5 papers)Software Engineering Research (2 papers)
- Journals
- Communications of the ACMACM SIGCSE BulletinMacmillan eBooks
- Partner nations
- United StatesCanada
In The Last Decade
Earl J. Schweppe
11 papers receiving 290 citations
Peers
Comparison fields: 5 of 45
- Computer Science Applications 223
- Information Systems 101
- Artificial Intelligence 82
- Computational Theory and Mathematics 72
- Computer Networks and Communications 66
Countries citing papers authored by Earl J. Schweppe
This map shows the geographic impact of Earl J. Schweppe'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 Earl J. Schweppe with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Earl J. Schweppe more than expected).
Fields of papers citing papers by Earl J. Schweppe
This network shows the impact of papers produced by Earl J. Schweppe. 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 Earl J. Schweppe. The network helps show where Earl J. Schweppe may publish in the future.
Co-authorship network of co-authors of Earl J. Schweppe
This figure shows the co-authorship network connecting the top 25 collaborators of Earl J. Schweppe. A scholar is included among the top collaborators of Earl J. Schweppe 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 Earl J. Schweppe. Earl J. Schweppe 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 | 1 | |
| 3 | ACM Curricula Recommendations for Computer Science | 3 |
| 4 | ACM Recommended Curricula for Computer Science and Information Processing Programs in Colleges and Universities, 1968-1981 | 4 |
| 5 | 8 | |
| 6 | 5 | |
| 7 | 1 | |
| 8 | 0 | |
| 9 | 308 | |
| 10 | An introduction to algorithmic methods using the MAD language | 4 |
| 11 | 8 | |
| 12 | 1 |
About Earl J. Schweppe
Earl J. Schweppe is a scholar working on Computer Science Applications, Media Technology and Hardware and Architecture, having authored 12 papers that have together received 344 indexed citations. Recurring topics across this work include Teaching and Learning Programming (8 papers), Experimental Learning in Engineering (5 papers) and Software Engineering Research (2 papers). The work is most often cited by research in Computer Science Applications (223 citations), Software (57 citations) and Hardware and Architecture (40 citations). Earl J. Schweppe has collaborated with scholars based in United States and Canada. Frequent co-authors include Samuel D. Conte, John W. Hamblen, Thomas A. Keenan, David M. Young, William F. Atchison, E.J. McCluskey, Werner C. Rheinboldt, Silvio O. Navarro and William G. Bulgren. Their work appears in journals such as Communications of the ACM, ACM SIGCSE Bulletin and Macmillan eBooks.
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.