The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Inductive Teaching and Learning Methods: Definitions, Comparisons, and Research Bases
Abstract
Traditional engineering instruction is deductive, beginning with theories and progressing to the applications of those theories. Alternative teaching approaches are more inductive. Topics are introduced by presenting specific observations, case studies or problems, and theories are taught or the students are helped to discover them only after the need to know them has been established. This study reviews several of the most commonly used inductive teaching methods, including inquiry learning, problem-based learning, project-based learning, case-based teaching, discovery learning, and just-in-time teaching. The paper defines each method, highlights commonalities and specific differences, and reviews research on the effectiveness of the methods. While the strength of the evidence varies from one method to another, inductive methods are consistently found to be at least equal to, and in general more effective than, traditional deductive methods for achieving a broad range of learning outcomes.
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Citations
Cites
Does Active Learning Work? A Review of the Research
Prince, M. (2004) · Journal of Engineering Education · doi:10.1002/j.2168-9830.2004.tb00809.x · Cited by 7,334 · cited 5×
“AUTHORS’ BIOGRAPHIES Michael Prince is a professor in the Department of Chemical Engineering at Bucknell University, where he has been since receiving his Ph.D. from the University of California at Berkeley in 1989.”
BackgroundInfluentialSelf-citationHake, R. R. (1998) · American Journal of Physics · doi:10.1119/1.18809 · Cited by 6,072
Enhancing teaching through constructive alignment
Biggs, J. (1996) · Higher Education · doi:10.1007/bf00138871 · Cited by 4,084
Springer, L., Stanne, M. E., & Donovan, S. S. (1999) · Review of Educational Research · doi:10.3102/00346543069001021 · Cited by 1,943
Effects of Problem-Based Learning: A Meta-Analysis From the Angle of Assessment
Gijbels, D., Dochy, F., Van den Bossche, P., & Segers, M. (2005) · Review of Educational Research · doi:10.3102/00346543075001027 · Cited by 917
Designing and teaching courses to satisfy the ABET engineering criteria
Felder, R. M., & Brent, R. (2003) · Journal of Engineering Education · doi:10.1002/j.2168-9830.2003.tb00734.x · Cited by 866
Self-citationNovak, G. M., Patterson, E. T., Gavrin, A. D., Christian, W., & Forinash, K. (1999) · American Journal of Physics · doi:10.1119/1.19159 · Cited by 384
Cited by
Active learning increases student performance in science, engineering, and mathematics
Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1319030111 · Cited by 9,787
Experiential Learning Theory as a Guide for Experiential Educators in Higher Education
Kolb, A. Y., & Kolb, D. A. (2022) · Experiential Learning and Teaching in Higher Education · doi:10.46787/elthe.v1i1.3362 · Cited by 712
“As experiential, learner-centered education has gained widespread acceptance in the twenty-first century (Prince & Felder 2006; Slavich & Zimbardo 2012), more and more educators are experimenting with experiential learning practices such as service learning (Bielefeldt et al. 2011; Brower 2011),…”
BackgroundBorrego, M., & Henderson, C. (2014) · Journal of Engineering Education · doi:10.1002/jee.20040 · Cited by 463
How Can Recommender Systems Benefit from Large Language Models: A Survey
Lin, J., Dai, X., Xi, Y., Liu, W., Chen, B., Zhang, H., Liu, Y., Wu, C., Li, X., Zhu, C., et al. (2025) · ACM Transactions on Information Systems · doi:10.1145/3678004 · Cited by 242
Project-based learning as a contributing factor to graduates’ work readiness
Jollands, M., Jolly, L., & Molyneaux, T. (2012) · European Journal of Engineering Education · doi:10.1080/03043797.2012.665848 · Cited by 162
Metadata
- Authors
- Prince, M. J., & Felder, R. M.
- Venue
- Journal of Engineering Education
- Publisher
- Wiley
- Published
- 2006-04
- Volume
- 95
- Issue
- 2
- Pages
- 123-138
- ISSN
- 1069-4730 (ISSN-L)
- OpenAlex
- W2115669451
- Cited by
- 2,446
- References
- 7
- FWCI
- 145.54
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
