Key ideas
Core concepts
- Discovery-based approaches have students learn through exploration, inquiry, and problem-solving rather than direct instruction, beginning with complex scenarios rather than building foundational skills systematically.
- Evidence shows explicit teaching is more effective, especially for novice learners.
Non-explicit teaching includes inquiry-based learning, project-based learning, and discovery methods that minimise direct instruction in favour of student-led exploration.
Discovery-based learning has intuitive appeal: scientists discover new knowledge through experimentation and inquiry, so students should learn science through experimentation and inquiry. This reasoning drove the 1960s New Math reforms following Sputnik’s launch, as educators hoped to develop scientific and engineering talent by mimicking how professionals in those fields work (Phillips, 2015). However, how experts generate new knowledge in a domain differs from how novices acquire foundational understanding of that domain (Kirschner, Sweller, & Clark, 2006). Discovery-based approaches overwhelm novice learners’ working memory (Sweller, 1988), ignore expert-novice differences in thinking (Chi, Feltovich, & Glaser, 1981), and produce inferior learning outcomes compared to explicit teaching (Alfieri, Brooks, Aldrich, & Tenenbaum, 2011; Stockard et al., 2018). This applies to the biologically secondary knowledge that comprises most academic content (Geary, 2007).
Connected to
Explicit Teaching | Maths Wars | Just-in-Time | Education as Natural Development | What Research Can You Trust | Cognitive Load Theory | Experts and Novices Think Differently | Biologically Primary & Secondary Knowledge | Part-Whole Approach | Struggle | Motivation | Situated Cognition | Implementation Fidelity
Underlying assumptions
Non-explicit teaching approaches have students learn complex academic concepts through discovery and exploration, with minimal direct instruction from teachers (Kirschner et al., 2006). These methods begin with complex, real-world scenarios and expect students to extract underlying principles through guided exploration.
Inquiry-based learning begins with a particular context, pretends students are already experts in this context, and asks them to behave as such. Teachers drop hints to keep students on track without providing direct information. As Heathcote and Herbert (1985) describe the foundational premise: “A teacher cannot presume to give direct information to experts but must set up ways in which the experts will discover what they know while at the same time protecting them from the awareness that they do not as yet have this expertise.”
STEM and project-based learning aim to show students real-world applications and interconnections between subjects. However, real-world contexts present multiple elements simultaneously, which often creates cognitive overload for novice learners (Hmelo-Silver, Duncan, & Chinn, 2007).
What goes wrong in practice
Complex problems present too many variables for students to consider, creating cognitive overload, and students disengage from frustration. The methods expect novices to think like scientists without prerequisite knowledge, but students lack the foundation to act as “experts”. Real-world contexts can constrain mathematical thinking: STEM projects sometimes use only basic measurement or simple graphs rather than developing mathematical sophistication.
Non-explicit approaches ignore the limited capacity of working memory for novel information (approximately four elements; Cowan, 2001). Students must simultaneously hold multiple problem elements, generate solution strategies, monitor progress toward goals, and connect new information to prior knowledge, causing cognitive overload. These approaches expect students to think like experts, yet experts and novices think differently (Chi et al., 1981). Experts can handle complex scenarios because they have automated foundational knowledge through extensive practice (Ericsson & Kintsch, 1995).
Teaching writing through repeated whole compositions with feedback resembles a volleyball coach who eliminates all drills and exercises, has players only play complete games, provides feedback at the end of each game, and expects improvement without practising component skills. Complex biologically secondary knowledge requires a bottom-up approach with explicit teaching from the outset (Geary, 2007, 2008).
Evidence
Klahr and Nigam (2004) randomly assigned students to explicit teaching or discovery learning for the scientific variable control principle. The explicit teaching group learnt the principle better (77% vs 23% success rate). Transfer tasks (judging science fair posters) showed no difference between groups. The discovery approach showed no advantage despite claims of “deeper” learning. The study found no evidence that discovery leads to superior learning outcomes or better transfer.
New Math
In the early 1960s, the US was shocked by the Russians launching Sputnik 1 marking the start of the space race. The idea was: we need more scientists and engineers and we need them fast. Since scientists and engineers gain new knowledge through experimentation, inquiry, and discovery, we should educate our children through experimentation, inquiry, and discovery. This didn’t work for many reasons, including cognitive overload and expert-novice differences. While New Math was quickly dismantled due to its inefficacy, this has led to decades of inquiry and discovery learning dominating the pedagogy used in schools and negatively affecting student learning.
Where discovery does fit
Play-based learning teaches biologically primary knowledge such as motor skills, pattern recognition, and social skills. These abilities develop naturally through environmental interaction (Geary, 2007). Biologically secondary knowledge such as mathematical concepts, reading, and scientific principles requires explicit teaching (Geary, 2007, 2008). “Doing” a subject, like science, differs from learning its foundational concepts, so inquiry and discovery work better as extension activities after foundational knowledge is secure.
High-performing education systems balance teacher-directed and inquiry methods (OECD, 2016). This balance considers the type of instruction for each approach, the sequence in which instruction occurs, and appropriate ratios of different methods.
Inquiry has value in an effective curriculum. Students need opportunities to work on complex problems showing connections between concepts, though teachers should watch for cognitive overload when using real-world contexts with novice learners. Basing the entire curriculum on inquiry rather than explicit teaching contradicts evidence on effective learning due to working memory limitations (Kirschner et al., 2006; Sweller et al., 2019). Explicit instruction should come first for biologically secondary knowledge, before complex applications.
References
Alfieri, L., Brooks, P. J., Aldrich, N. J., & Tenenbaum, H. R. (2011). Does discovery-based instruction enhance learning? Journal of Educational Psychology, 103(1), 1-18. https://doi.org/10.1037/a0021017
Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5(2), 121-152. https://doi.org/10.1207/s15516709cog0502_2
Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24(1), 87-114. https://doi.org/10.1017/S0140525X01003922
Ericsson, K. A., & Kintsch, W. (1995). Long-term working memory. Psychological Review, 102(2), 211-245. https://doi.org/10.1037/0033-295X.102.2.211
Geary, D. C. (2007). Educating the evolved mind: Conceptual foundations for an evolutionary educational psychology. In J. S. Carlson & J. R. Levin (Eds.), Psychological perspectives on contemporary educational issues (pp. 1-99). Information Age Publishing.
Geary, D. C. (2008). An evolutionarily informed education science. Educational Psychologist, 43(4), 179-195. https://doi.org/10.1080/00461520802392133
Heathcote, D., & Herbert, P. (1985). A drama of learning: Mantle of the expert. Theory Into Practice, 24(3), 173-180. https://doi.org/10.1080/00405848509543176
Hmelo-Silver, C. E., Duncan, R. G., & Chinn, C. A. (2007). Scaffolding and achievement in problem-based and inquiry learning: A response to Kirschner, Sweller, and Clark (2006). Educational Psychologist, 42(2), 99-107. https://doi.org/10.1080/00461520701263368
Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75-86. https://doi.org/10.1207/s15326985ep4102_1
Klahr, D., & Nigam, M. (2004). The equivalence of learning paths in early science instruction: Effects of direct instruction and discovery learning. Psychological Science, 15(10), 661-667. https://doi.org/10.1111/j.0956-7976.2004.00737.x
OECD. (2016). PISA 2015 results (Volume II): Policies and practices for successful schools. OECD Publishing. https://doi.org/10.1787/9789264267510-en
Phillips, C. J. (2015). The new math: A political history. University of Chicago Press.
Stockard, J., Wood, T. W., Coughlin, C., & Rasplica Khoury, C. (2018). The effectiveness of direct instruction curricula: A meta-analysis of a half century of research. Review of Educational Research, 88(4), 479-507. https://doi.org/10.3102/0034654317751919
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285. https://doi.org/10.1207/s15516709cog1202_4
Sweller, J., van Merriënboer, J. J. G., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 31(2), 261-292. https://doi.org/10.1007/s10648-019-09465-5