The Cambridge Handbook of the Learning Sciences
Abstract
The interdisciplinary field of the learning sciences encompasses educational psychology, cognitive science, computer science, and anthropology, among other disciplines. The Cambridge Handbook of the Learning Sciences is the definitive introduction to this innovative approach to teaching, learning, and educational technology. This dramatically revised second edition incorporates the latest research in the field, includes twenty new chapters on emerging areas of interest, and features contributors who reflect the increasingly international nature of the learning sciences. The authors address the best ways to design educational software, prepare effective teachers, organize classrooms, and use the internet to enhance student learning. They illustrate the importance of creating productive learning environments both inside and outside school, including after-school clubs, libraries, museums, and online learning environments. Accessible and engaging, the Handbook has proven to be an essential resource for graduate students, researchers, teachers, administrators, consultants, educational technology designers, and policy makers on a global scale.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Cited by
Sorted by citations
Azevedo, R. (2015) · Educational Psychologist · doi:10.1080/00461520.2015.1004069 · Cited by 418 · see citing works
Argumentation for Learning: Well-Trodden Paths and Unexplored Territories
Asterhan, C. S. C., & Schwarz, B. B. (2016) · Educational Psychologist · doi:10.1080/00461520.2016.1155458 · Cited by 338 · see citing works
Sedrakyan, G., Malmberg, J., Verbert, K., Järvelä, S., & Kirschner, P. A. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2018.05.004 · Cited by 283 · see citing works
Conditions for the Effectiveness of Multiple Visual Representations in Enhancing STEM Learning
Rau, M. A. (2017) · Educational Psychology Review · doi:10.1007/s10648-016-9365-3 · Cited by 281 · see citing works
Big data for policymaking: Fad or fasttrack?
Giest, S. (2017) · Policy Sciences · doi:10.1007/s11077-017-9293-1 · Cited by 235 · see citing works
Effects of real-time analytics-based personalized scaffolds on students’ self-regulated learning
Lim, L., Bannert, M., van der Graaf, J., Singh, S., Fan, Y., Surendrannair, S., Rakovic, M., Molenaar, I., Moore, J., & Gašević, D. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107547 · Cited by 198 · see citing works
Kong, S.-C., Cheung, M.-Y. W., & Tsang, O. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100214 · Cited by 167 · see citing works
Multimodal data to design visual learning analytics for understanding regulation of learning
Noroozi, O., Alikhani, I., Järvelä, S., Kirschner, P. A., Juuso, I., & Seppänen, T. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2018.12.019 · Cited by 140 · see citing works
Sormunen, K., Juuti, K., & Lavonen, J. (2020) · International Journal of Science and Mathematics Education · doi:10.1007/s10763-019-09998-9 · Cited by 80 · see citing works
Examining the instructor-student collaborative partnership in an online learning community course
Ouyang, F., Chang, Y.-H., Scharber, C., Jiao, P., & Huang, T. (2020) · Instructional Science · doi:10.1007/s11251-020-09507-4 · Cited by 60 · see citing works
Silseth, K., Steier, R., & Arnseth, H. C. (2024) · International Journal of Computer-Supported Collaborative Learning · doi:10.1007/s11412-023-09413-0 · Cited by 20 · see citing works
Collaborative Online Learning across Cultures: the Role of Teaching and Social Presence
Grothaus, C. (2022) · Qualitative Research in Education · doi:10.17583/qre.10474 · Cited by 18 · see citing works
Teaching and learning cultures in higher education: A mismatch in conceptions
Sagy, O., Hod, Y., & Kali, Y. (2019) · Higher Education Research & Development · doi:10.1080/07294360.2019.1576594 · Cited by 16 · see citing works
Computing Education Research Today
Fincher, S. A., Tenenberg, J., Dorn, B., Hundhausen, C., McCartney, R., & Murphy, L. (2019) · The Cambridge Handbook of Computing Education Research · doi:10.1017/9781108654555.003 · Cited by 7 · see citing works
From avoidance to adoption: strategies for integrating AI in higher education contexts
Gerard, J., Hadi, M. U., Natsheh, B. R. R., Singh, M., & Reid, A. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1869723
- Authors
- Sawyer, R. K.
- Corresponding author
- R. Keith Sawyer
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2014-09-23
- OpenAlex
- W1544649311
- Cited by
- 907
- References
- 0
- Citations by year
- 2026: 43 · 2025: 67 · 2024: 90 · 2023: 89 · 2022: 98 · 2021: 94 · 2020: 73 · 2019: 96 · 2018: 72 · 2017: 63 · 2016: 45 · 2015: 35 · 2014: 15 · 2013: 5 · 2012: 5
- FWCI
- 31.05
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Cambridge University Press eBooks (publishedVersion)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neuroscience, Education and Cognitive Function
- Keywords
- learning sciences, educational psychology, cognitive science, educational technology, educational software design, teacher preparation, classroom organization, online learning, learning environment, after-school programs, virtual learning environments, interdisciplinary research
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
