James D. Basham

43 papers receiving 1.1k citations

James D. Basham's Hit Papers

Understanding the implementation of personalized learning: A research synthesis 2020 · 140 citations
1400+2+4Years since publication4080120

Peers

James D. Basham
Comparison fields: 5 of 89
  • Computer Science Applications 229
  • Safety Research 265
  • Developmental and Educational Psychology 317
  • Human Factors and Ergonomics 59
  • Education 661
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Kavita Rao United States
Sean J. Smith United States
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Dorit Maor Australia
Marta Montenegro Rueda Spain
Inmaculada García‐Martínez Spain
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Countries citing papers authored by James D. Basham

Since Specialization
Citations

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

Fields of papers citing papers by James D. Basham

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by James D. Basham. 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 James D. Basham. The network helps show where James D. Basham may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 45 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2013142
2
Understanding the implementation of personalized learning: A research synthesis
Hit paper breakdown →
2020140
3 2016106
4 201372
5 200567
6 201261
7 201053
8 202351
9 201050
10
Opportunity in crisis: The role of universal design for learning in educational redesign
202039
11 201238
12 201434
13 202031
14 200929
15 201429
16 201725
17 201621
18 202121
19 201921
20 201020

About James D. Basham

James D. Basham is a scholar working on Education, Developmental and Educational Psychology, Computer Science Applications, Safety Research and Information Systems, having authored 45 papers that have together received 1.2k indexed citations. Recurring topics across this work include Online Learning and Analytics (12 papers), Online and Blended Learning (12 papers), Innovative Teaching and Learning Methods (11 papers), Disability Education and Employment (9 papers), Child Development and Digital Technology (5 papers), Mobile Learning in Education (5 papers), Autism Spectrum Disorder Research (4 papers) and Education and Technology Integration (4 papers). The work is most often cited by research in Computer Science Applications (229 citations), Safety Research (265 citations), Developmental and Educational Psychology (317 citations), Human Factors and Ergonomics (59 citations) and Education (661 citations). James D. Basham has collaborated with scholars based in United States, Australia and Taiwan. Frequent co-authors include Matthew T. Marino, Maya Israel, Sohyun Yang, Zhang Ling, Sean J. Smith, William M. Stahl, Tracey E. Hall, José Blackorby, Eleazar Vasquez and Eunice Eunhee Jang. Their work appears in journals such as Learning Disability Quarterly, Journal of Computer Assisted Learning, Journal of Research on Technology in Education, Journal of Science Education and Technology and International Journal of Mobile Learning and Organisation.

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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