Brian M. Slator

1.3k citations
58 papers · 677 indexed · h-index 14

Impact in

Papers in

Brian M. Slator

52 papers receiving 571 citations

Peers

Brian M. Slator
Comparison fields: 5 of 99
  • Computer Science Applications 87
  • Developmental and Educational Psychology 139
  • Artificial Intelligence 322
  • Language and Linguistics 79
  • Human-Computer Interaction 41
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Citations per field
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Citations per year

Countries citing papers authored by Brian M. Slator

Since Specialization
Citations

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

Fields of papers citing papers by Brian M. Slator

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Brian M. Slator. 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 Brian M. Slator. The network helps show where Brian M. Slator may publish in the future.

Co-authors

The 25 scholars most cited alongside Brian M. Slator, 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 Brian M. Slator Line = papers co-authored together Brian M. Slator links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 201512
2
INTELLIGENT TUTORS IN IMMERSIVE VIRTUAL ENVIRONMENTS
20132
3 201310
4
Measuring the effectiveness of programmingland
20071
5
An online computer science instructional resource
20062
6 2005111
7 20053
8
Rushing Headlong into the Past: The Blackwood Simulation.
20011
9
Virtual Worlds in Large Enrollment Science Classes Significantly Improve Authentic Learning
200126
10
Mixing Media For Distance Learning: Using Ivn And Moo In Comp372
19993
11
Designing, building, and assessing a virtual world for science education.
19996
12 199922
13
Electric words dictionaries, computers, and meanings
199679
14 199626
15 19940
16 19925
17 19901
18
A tractable machine dictionary as a resource for computational semantics
198942
19 198817
20 198612

About Brian M. Slator

Brian M. Slator is a scholar working on Computer Science Applications, Developmental and Educational Psychology, Language and Linguistics, Media Technology and Space and Planetary Science, having authored 58 papers that have together received 677 indexed citations. Recurring topics across this work include Teaching and Learning Programming (13 papers), Innovative Teaching and Learning Methods (10 papers), Open Education and E-Learning (10 papers), Natural Language Processing Techniques (10 papers), Lexicography and Language Studies (8 papers), Experimental Learning in Engineering (7 papers), Educational Games and Gamification (5 papers) and Educational Tools and Methods (5 papers). The work is most often cited by research in Computer Science Applications (87 citations), Developmental and Educational Psychology (139 citations), Artificial Intelligence (322 citations), Language and Linguistics (79 citations) and Human-Computer Interaction (41 citations). Brian M. Slator has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Yorick Wilks, Louise Guthrie, Phillip E. McClean, Donald P. Schwert, Bernhardt Saini‐Eidukat, Alan White, Lisa Daniels, Dan Fass, Tony Plate and James E. McDonald. Their work appears in journals such as Communications of the ACM, Journal of Network and Computer Applications, Computers & Geosciences, Computer and PLoS Biology.

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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