EDUCATIONAL PSYCHOLOGIST, 43(4), 214–216, 2008Copyright C© Taylor & Francis Group, LLCISSN: 0046-1520 print / 1532-6985 onlineDOI: 10.1080/00461520802392208

Instructional Implications of David C. Geary’sEvolutionary Educational Psychology

John SwellerSchool of Education

University of New South Wales, Australia

David C. Geary’s thesis has the potential to alter our understanding of those aspects of humancognition relevant to instruction. His distinction between biologically primary knowledge thatwe have evolved to acquire and biologically secondary knowledge that is culturally important,taught in educational institutions and which we have not evolved to acquire in modular form,is critical to instructional design. In this article, I outline some of the instructional implicationsthat I believe flow from Geary’s theory.

David C. Geary’s distinction between biologically primaryand biologically secondary information constitutes an ad-vance that is rare in our discipline. For researchers in instruc-tional psychology, the distinction adds a major piece of thejigsaw puzzle on which we are all working. In the process,Geary has provided a theoretical framework that has the po-tential to resolve important issues with profound instructionalimplications.

For several decades, the dominant theoretical frameworkof instructional psychologists has been various versionsof a discovery learning/constructivist teaching paradigm(Kirschner, Sweller, & Clark, 2006). Although that frame-work can probably be sourced back to philosophers such asDewey or even Rousseau, in more recent times, Bruner’s(1961) advocacy of discovery learning can be considered asthe origin of the current movement. In its modern versions,it was suggested that because learners must construct theirknowledge of the world, we should not present them with in-formation transmitted from a teacher to a student but ratherlearners should as far as possible and within limits be per-mitted to construct that knowledge without unnecessary im-pediments associated with receiving knowledge from others.

For several decades and for good reasons, this argumenthas been dominant. Although there are many causes for thatdominance, one obvious cause stood out: Outside of edu-cational/instructional contexts, we do acquire huge amountsof information without explicit instruction or, indeed, anydiscernible instruction whatsoever. The manner in which welearn to speak provides one of the most startling examples

Correspondence should be addressed to John Sweller, School of Educa-tion, University of New South Wales, Sydney, NSW 2052, Australia. E-mail:j.sweller@unsw.edu.au

of our ability to discover large amounts of complex knowl-edge without explicit instruction. We learn how to simulta-neously arrange our lips, tongue, breath, and voice simply byimmersion in a listening/speaking society. That learning isunconscious, effortless, and rapid. Not only is the knowledgeacquired at a very young age without teaching, but most ofus would have little idea how to teach someone to speak theirnative language.

It was both easy and sensible to observe the ease withwhich humans acquired large amounts of knowledge out-side of educational contexts and the difficulty many peoplehad in acquiring considerably less knowledge within edu-cational contexts. The logical conclusion seemed to be thatthis distinction was caused by the artificial and inappropriateprocedures used to teach. If the same procedures were usedwithin education as were used in the external world, it wasquite reasonable to assume that learning could be equally aseffortless and effective. This rational argument seems to haveimplicitly driven instructional procedures in all curriculumareas. Although there are signs of a current backlash, it isprobably accurate to say that this paradigm is still dominant.

Why has there been a backlash? Although the rationalemade sense as a hypothesis, despite decades of effort no largebody of empirical evidence based on randomized, controlledexperiments supporting constructivist teaching procedureshas emerged with no credible body of evidence supportingthe procedure. If anything, the evidence points in quite thereverse direction. When dealing with novices in a domain,there are an overwhelming number of studies demonstratingthat learners provided with worked examples to study learnmore and perform better on tests than learners asked to solvethe equivalent problems (see Renkl, 2005). This effect, calledthe worked example effect, has been demonstrated on innu-merable occasions around the world. It directly contradicts

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the suggestion that students will learn more if they are askedto discover something for themselves, in this case a prob-lem solution, rather than being presented with the relevantinformation by an instructor.

At this point, those of us associated with theories such ascognitive load theory (e.g., Clark, Nguyen, & Sweller, 2006)that were constructed around the empirical evidence asso-ciated with effects such as the worked example effect werefaced with a conundrum. On one hand, there was overwhelm-ing evidence that learners could acquire immense amounts ofinformation outside of educational institutions, largely with-out explicit instruction. On the other hand, when we ran in-structional experiments using commonly taught educationalmaterials, the results strongly indicated that far more waslearned using explicit instruction. For this researcher, work-ing within a cognitive load theory framework, there was noobvious resolution to the contradiction.

Geary’s thesis has provided the required resolution. In theprocess, theories such as cognitive load theory, designed togenerate novel instructional procedures, have been consid-erably strengthened. By introducing the distinction betweenbiologically primary and biologically secondary information,Geary has explained why learners can acquire some informa-tion easily and unconsciously, indeed, are strongly motivatedto acquire such information, whereas other information canbe acquired only with considerable conscious effort, often re-quiring external motivation. Instructional procedures that as-sume learners can acquire biologically secondary knowledgeby “immersion” (Kirschner et al., 2006) in the same way asthey can acquire biologically primary knowledge flow, at leastin part, from our previous failure to distinguish between thesetwo categories of knowledge. When dealing with biologi-cally secondary knowledge we have neither the motivationalimpetus nor the genetically inspired ability to assimilate in-formation automatically. We require explicit instruction andmotivational encouragement. Neither is required when deal-ing with biologically primary knowledge.

Since Geary’s formulation, it has become clear that theo-ries like cognitive load theory apply solely to the biologicallysecondary knowledge for which schools and other educa-tional institutions were invented. The cognitive architectureon which the theory is based (Sweller, 2003, 2004; Sweller& Sweller, 2006) applies to the wide variety of knowledgedealt with in educational contexts rather than the specificknowledge addressed by modular, biologically primary sys-tems. That architecture assumes that most human cognitiveactivity is driven by a large store of information held inlong-term memory; that most of that information is obtainedfrom other people by imitating, listening, and reading; thatproblem solving in novel domains is heavily influenced by arandom generate and test procedure; that novel informationis processed by a limited capacity, limited duration workingmemory; and that there are no working memory limitationswhen working memory deals with familiar information fromlong-term memory. In various forms, these principles have

constituted a cognitive architecture that has been used in thegeneration of a variety of instructional procedures (Sweller,2003, 2004).

From Geary’s work it has become clear that some of theseprinciples either do not apply or only distantly apply to bi-ologically primary knowledge. For example, a large storeof information in long-term memory is equally importantfor either primary or secondary knowledge. In contrast, al-though working memory limitations when acquiring novelinformation are critical to secondary knowledge, those work-ing memory limitations seem to be less important when ac-quiring novel primary knowledge. The modular systems foracquiring primary knowledge seem to permit huge amountsof information to be rapidly assimilated with a working mem-ory load that is considerably reduced compared to the cog-nitive load imposed by biologically secondary knowledge.Children learn the prodigiously complex motor movementsassociated with speaking their native language with relativelylittle apparent strain on their working memory.

Similarly, the modular primary knowledge systems are or-ganized to acquire knowledge without the random generateand test that is characteristic of human problem solving. Theterm “problem solving” seems to apply only to secondaryknowledge. We do not puzzle over how to proceed when ac-quiring our native language or learning to recognize facesin the typical manner associated with solving, for example,problems in a biologically secondary knowledge domain suchas mathematics. The random generate and test process that iscentral to problem solving when dealing with novel, biologi-cally secondary knowledge is irrelevant to novel, biologicallyprimary knowledge because we have evolved to respond ap-propriately when dealing with primary knowledge. We do nothave to test which responses might be appropriate in a givenenvironment because we are genetically programmed to con-sider only a very narrow range of possible responses. Thatgenetic programming is unavailable when dealing with thebiologically secondary knowledge characteristically relevantin education-based environments.

It should be noted that the aforementioned principlesused to describe human cognitive architecture when dealingwith the secondary knowledge relevant to education, applyequally to the constructs of evolution by natural selection(Sweller & Sweller, 2006) and, in that sense, constitute a nat-ural information-processing system. Until relatively recently,evolution has been largely ignored in the field of psychologyand has been quite irrelevant to educational psychologists. Itis easy to assume that evolutionary principles have no signifi-cance when dealing with instructional design. In fact, there isa clear link between instruction and evolution via two steps.The first step involves a link between instructional proceduresand cognitive architecture, whereas the second step involves alink between cognitive architecture and biological evolution.

With respect to the first step, instructional proceduresthat fail to take human cognitive architecture into accountare likely to be random in their effectiveness. Too many

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instructional design recommendations seem to proceed asthough human cognitive architecture does not exist. Thenear universal recommendation that we should teach forunderstanding rather than rote learning provides a casein point. Most of us concerned with instructional issuessubscribe to this recommendation. Nevertheless, we are fartoo happy to recommend that instruction should facilitate“understanding” rather than “rote learning” without everindicating which aspects of human cognitive architecture areinvolved in understanding or rote learning. Frequently, thereis an assumption that although rote learning clearly requireslong-term memory, understanding is somehow unrelatedto memory. In fact, both are critically dependent on theexistence of a long-term memory store.

As an example, if we rote learn that 3 × 4 = 12, we canstore that rote-learned fact in long-term memory. If, in addi-tion, we not only learn that 3 × 4 = 12 but also understandthat multiplication means repeated addition so that (3 × 4) =(3 + 3 + 3 + 3) = 12, then we have gone a small way towardunderstanding the meaning of multiplication. Nevertheless,this additional information also must be stored in long-termmemory if understanding is to occur. Further understandingof the concepts associated with multiplication requires ad-ditional storage of information in long-term memory. Thedifference between learning by rote and learning by under-standing is not that one requires information to be storedin long-term memory whereas the other does not. Both areequally reliant on the storage of information in long-termmemory. It is the nature of the information stored that dis-tinguishes between the two forms of learning. Learning withunderstanding requires large amounts of linked information(called high element interactivity information by cognitiveload theorists) to be stored in long-term memory. Process-ing novel, linked information in working memory imposesa heavy cognitive load, and so many learners will rote learninstead. It does not follow that learning with understandingsomehow reduces the role of long-term memory or, for thatmatter, working memory. The only difference is that learningwith understanding results in more information being storedin long-term memory and that information has character-istics that make it difficult to process in working memory.When dealing with biologically secondary knowledge, hu-man cognitive architecture is central to all forms of learningand should not be ignored by instructional designers.

Relations between instructional procedures and cogni-tive architecture provide a first step in linking instructionto evolution. Relations between cognitive architecture andbiological evolution provide the second step. Human cogni-tive architecture presumably evolved in the same way as allother biological structures and functions evolved. If, to con-tinue the aforementioned example, we describe “understand-ing” in terms of long-term memory storage and processingin working memory, we can provide potential evolutionarymechanisms by which human “understanding” could evolve.Why did we evolve with a vast long-term memory associated

with a limited working memory when dealing with novelinformation? Why did we evolve so that the limitations ofworking memory disappear when it deals with familiar in-formation from long-term memory? If we wish to place anemphasis on understanding rather than rote learning, whatare the instructional implications of these cognitive charac-teristics?

Any proposed mechanisms that relate instructional designto cognitive architecture on one hand and cognitive architec-ture to biological evolution on the other hand may or maynot be valid but they are potentially describable. In contrast,if a frequently used term such as “understanding” is left un-defined as it is in many instructional recommendations, itmerely becomes mystical. We are all in favor of learningwith understanding rather than rote learning, but if we donot define what we mean by learning with understanding in amanner that is intelligible from an evolutionary perspective,we run the risk of providing a framework that encourages theuse of almost any instructional procedure including ones thatare most unlikely to be effective. Our ability to “understand”has evolved in the same way as any other human function,and there is an onus on instructional designers to explainwhat they mean by understanding and how their concept ofunderstanding can be incorporated into a human cognitive ar-chitecture that evolved according to the precepts of evolutionby natural selection.

Geary’s evolutionary educational psychology with its em-phasis on modular primary knowledge that we have evolvedto acquire, and more general secondary knowledge that hasbecome culturally important but for many people frequentlydifficult to understand, provides us with a firm foundation forinstructional design. For those of us involved in instructionalissues, I believe the advantages of his approach should beimmediately apparent.

REFERENCES

Bruner, J. (1961). The art of discovery. Harvard Educational Review, 31,21–32.

Clark, R. C., Nguyen, F., & Sweller, J. (2006). Efficiency in learning:Evidence-based guidelines to manage cognitive load. San Francisco:Pfeiffer.

Kirschner, P., Sweller, J., & Clark, R. (2006). Why minimal guidance duringinstruction does not work: An analysis of the failure of constructivist,discovery, problem-based, experiential and inquiry-based teaching. Edu-cational Psychologist, 41, 75–86.

Renkl, A. (2005). The worked out example principle in multimedia learning.In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning(pp. 229–245). New York: Cambridge University Press.

Sweller, J. (2003). Evolution of human cognitive architecture. In B. Ross(Ed.), The psychology of learning and motivation (Vol. 43, pp. 215–266).San Diego: Academic Press.

Sweller, J. (2004). Instructional design consequences of an analogy betweenevolution by natural selection and human cognitive architecture. Instruc-tional Science, 32, 9–31.

Sweller, J., & Sweller, S. (2006). Natural information processing systems.Evolutionary Psychology, 4, 434–458.

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