Hans Spada

107 papers receiving 3.4k citations

Peers

Hans Spada
Comparison fields: 5 of 133
  • Developmental and Educational Psychology 1.9k
  • Computer Science Applications 511
  • Communication 464
  • Education 1.3k
  • Experimental and Cognitive Psychology 531
Replace Jon‐Chao Hong with:
Jon‐Chao Hong Taiwan
Ulrike Creß Germany
Herre van Oostendorp Netherlands
Katie Salen United States
Heinz Mandl Germany
Wim Jochems Netherlands
Rob Martens Netherlands
Gerald Knezek United States
Ann Jones United Kingdom
Alberto J. Cañas United States
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Citations per field
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Citations per year

Countries citing papers authored by Hans Spada

Since Specialization
Citations

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

Fields of papers citing papers by Hans Spada

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 113 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2005366
2
Learning in Humans and Machines : Towards an Interdisciplinary Learning Science
1995351
3 2007281
4 2014220
5 2014212
6 2004211
7
Learning in Humans and Machines
1995134
8
Developmental models of thinking
1980112
9 200899
10
Acquiring knowledge in science and mathematics : the use of multiple representations in technology based learning environments
199899
11 200196
12 200184
13 201074
14 201174
15 201463
16 201859
17 199955
18 201453
19 201545
20 198541

About Hans Spada

Hans Spada is a scholar working on Developmental and Educational Psychology, Sociology and Political Science, Social Psychology, Artificial Intelligence and Education, having authored 113 papers that have together received 3.7k indexed citations. Recurring topics across this work include Innovative Teaching and Learning Methods (49 papers), Team Dynamics and Performance (13 papers), Intelligent Tutoring Systems and Adaptive Learning (11 papers), Online and Blended Learning (10 papers), Visual and Cognitive Learning Processes (10 papers), Knowledge Management and Sharing (9 papers), Environmental Education and Sustainability (9 papers) and AI-based Problem Solving and Planning (8 papers). The work is most often cited by research in Developmental and Educational Psychology (1.9k citations), Computer Science Applications (511 citations), Communication (464 citations), Education (1.3k citations) and Experimental and Cognitive Psychology (531 citations). Hans Spada has collaborated with scholars based in Germany, United States and Switzerland. Frequent co-authors include Nikol Rummel, Peter Reimann, Ulf J.J. Hahnel, Anne Meier, Reimann, Josef Nerb, Rolf Ploetzner, Michael Wiedmann, Daniel Bodemer and Rainer H. Kluwe. Their work appears in journals such as International Journal of Computer-Supported Collaborative Learning, Frontiers in Psychology, Learning and Instruction, European Psychologist and Cognition & Emotion.

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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