John Self

1.7k citations
30 papers · 582 · h-index 12

Impact in

Papers in

John Self

29 papers receiving 496 citations

Peers

John Self
Comparison fields: 5 of 76
  • Computer Science Applications 185
  • Developmental and Educational Psychology 228
  • Artificial Intelligence 294
  • Health Informatics 7
  • Human Factors and Ergonomics 11
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Citations per field
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Citations per year

Countries citing papers authored by John Self

Since Specialization
Citations

This map shows the geographic impact of John Self'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 John Self with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Self more than expected).

Fields of papers citing papers by John Self

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John Self. 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 John Self. The network helps show where John Self may publish in the future.

Co-authors

The 7 scholars most cited alongside John Self, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with John Self Line = papers co-authored together John Self links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Learning and Teaching with Computers
1983103
2 197496
3
Theoretical foundations for intelligent tutoring systems
199040
4
Learning and teaching with computers : artificial intelligence in education
198340
5
System Intelligence in Constructivist Learning
200040
6 199239
7 200238
8
Artificial Intelligence and Human Learning: Intelligent Computer-Aided Instruction
198828
9 198523
10 198620
11
Intelligent knowledge-based systems : an introduction
198714
12 199013
13
Learning and Teaching with Computers: The Artificial Intelligence Revolution
198611
14
On the dynamics of learner models
19949
15 19799
16
A learner model reason maintenance system
19948
17
Involving the Learner in Diagnosis - Potentials and Problems
20007
18 19827
19 19977
20
Cognitive diagnosis for tutoring systems
19926

About John Self

John Self is a scholar working on Artificial Intelligence, Developmental and Educational Psychology, Computer Science Applications, Cognitive Neuroscience and Computational Theory and Mathematics, having authored 30 papers that have together received 582 indexed citations. Recurring topics across this work include Intelligent Tutoring Systems and Adaptive Learning (12 papers), Innovative Teaching and Learning Methods (5 papers), AI-based Problem Solving and Planning (4 papers), Cognitive Science and Education Research (4 papers), Online Learning and Analytics (4 papers), Semantic Web and Ontologies (3 papers), AI in Service Interactions (3 papers) and Logic, Reasoning, and Knowledge (2 papers). The work is most often cited by research in Computer Science Applications (185 citations), Developmental and Educational Psychology (228 citations), Artificial Intelligence (294 citations), Health Informatics (7 citations) and Human Factors and Ergonomics (11 citations). John Self has collaborated with scholars based in United Kingdom, Ghana and Brazil. Frequent co-authors include Tim O’Shea, Pierre Dillenbourg, Geoff Cumming, Ana Paiva, Roger T. Hartley, Vania Dimitrova and Paul Brna. Their work appears in journals such as Instructional Science, The Computer Journal, Robotica, Communications of the ACM and Educational Review.

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.

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