Daniel C. Moos

3.2k citations
36 papers · 2.2k indexed · h-index 22

Daniel C. Moos

36 papers receiving 2.0k citations

Peers

Daniel C. Moos
Comparison fields: 5 of 82
  • Developmental and Educational Psychology 1.6k
  • Computer Science Applications 564
  • Education 1.2k
  • Experimental and Cognitive Psychology 271
  • Communication 120
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Countries citing papers authored by Daniel C. Moos

Since Specialization
Citations

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

Fields of papers citing papers by Daniel C. Moos

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel C. Moos. 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 Daniel C. Moos. The network helps show where Daniel C. Moos may publish in the future.

Co-authorship network

The 9 scholars most cited alongside Daniel C. Moos, 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 Daniel C. Moos Line = papers co-authored together Daniel C. Moos links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 202012
2 201579
3 20143
4
Examining hypermedia learning: The role of cognitive load and self-regulated learning
201317
5 201127
6 201154
7 20103
8
Self-Regulated Learning with Hypermedia: Too Much of a Good Thing?.
20108
9 2010210
10 20107
11
Issues in the measurement of cognitive and metacognitive regulatory processes used during hypermedia learning
20095
12 200939
13 200770
14 200791
15 200760
16 2007165
17
The Role of Goal Structure in Undergraduates’ Use of Self-Regulatory Variables in Two Hypermedia Learning Tasks
200645
18
Is externally-regulated learning by a human tutor effective in facilitating learning with hypermedia?
20061
19 200675
20
Can students collaboratively use hypermedia to learn about science?: the dynamics of self- and other-regulatory processes in the classroom
20041

About Daniel C. Moos

Daniel C. Moos is a scholar working on Developmental and Educational Psychology, Computer Science Applications, Education, Artificial Intelligence and Communication, having authored 36 papers that have together received 2.2k indexed citations. Recurring topics across this work include Innovative Teaching and Learning Methods (32 papers), Online and Blended Learning (18 papers), Educational Strategies and Epistemologies (12 papers), Intelligent Tutoring Systems and Adaptive Learning (10 papers), Online Learning and Analytics (9 papers), Visual and Cognitive Learning Processes (3 papers), Impact of Technology on Adolescents (2 papers) and Learning Styles and Cognitive Differences (2 papers). The work is most often cited by research in Developmental and Educational Psychology (1.6k citations), Computer Science Applications (564 citations), Education (1.2k citations), Experimental and Cognitive Psychology (271 citations) and Communication (120 citations). Daniel C. Moos has collaborated with scholars based in United States, Canada and Germany. Frequent co-authors include Roger Azevedo, Jeffrey A. Greene, Fielding I. Winters, Jennifer G. Cromley, Amber Chauncey, Amy M. Johnson, D. Bauer, Amy Witherspoon and Amanda L. Miller. Their work appears in journals such as Computers & Education, Computers in Human Behavior, Journal of Educational Computing Research, Instructional Science and Metacognition and Learning.

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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