Philip H. Winne

20.1k citations
166 papers · 11.3k indexed · 2 hit papers · h-index 56

Philip H. Winne

161 papers receiving 9.9k citations

Hit Papers

Feedback and Self-Regulated Learning: A Theoretical Synth...2.1k199520262005201550010001.5k2.0k

Peers

Philip H. Winne
Comparison fields: 5 of 151
  • Developmental and Educational Psychology 7.0k
  • Computer Science Applications 2.4k
  • Education 6.5k
  • Experimental and Cognitive Psychology 1.6k
  • Communication 511
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Roger Azevedo United States
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Marlene Scardamalia Canada
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Citations per field
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Citations per year

Countries citing papers authored by Philip H. Winne

Since Specialization
Citations

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

Fields of papers citing papers by Philip H. Winne

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Philip H. Winne. 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 Philip H. Winne. The network helps show where Philip H. Winne may publish in the future.

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20260
2 202211
3 202113
4 20211
5 202014
6 20173
7 20166
8 20149
9
Generating Natural Language Questions to Support Learning On-Line
201367
10
Adaptive Learning Environments: Foundations and Frontiers
20123
11
Assessing the psychometric properties of the achievement goals questionnaire across task contexts
20128
12
Bootstrapping learner's self-regulated learning.
201032
13 200948
14 200687
15
Pedagogy: teaching for learning
200515
16 200549
17
The Role of Feedback on Studying, Achievement and Calibration.
20003
18
Feedback and Self-Regulated Learning: A Theoretical Synthesisbreakdown →
19952133
19
Exploring Individual Differences in Studying Strategies Using Graph Theoretic Statistics.
199430
20 198020

About Philip H. Winne

Philip H. Winne is a scholar working on Developmental and Educational Psychology, Computer Science Applications and Education, having authored 166 papers that have together received 11.3k indexed citations. Recurring topics across this work include Innovative Teaching and Learning Methods (86 papers), Educational Strategies and Epistemologies (39 papers), Online and Blended Learning (32 papers), Online Learning and Analytics (30 papers), Intelligent Tutoring Systems and Adaptive Learning (19 papers), Education, Achievement, and Giftedness (17 papers), Education and Critical Thinking Development (15 papers) and Motivation and Self-Concept in Sports (13 papers). The work is most often cited by research in Developmental and Educational Psychology (7.0k citations), Computer Science Applications (2.4k citations) and Education (6.5k citations). Philip H. Winne has collaborated with scholars based in Canada, United States and Australia. Frequent co-authors include Deborah L. Butler, Allyson F. Hadwin, Dianne Jamieson‐Noel, John C. Nesbit, Ronald W. Marx, Carmen L.Z. Gress, Nancy E. Perry, Krista R. Muis, James Hartley and Ken R. Lodewyk. Their work appears in journals such as Child Development, Annual Review of Psychology and Journal of Educational Psychology.

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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