Can humans benefit from their prior experience when attempting to solve novel problems? Transfer research suggests the complexity within this question. This entry describes the various complexities in studying transfer. First competing definitions of transfer are described, and then the discussion turns to the dimensions along which transfer may occur and the types of evidence that different researchers argue is needed to demonstrate the existence of transfer. Obstacles that prevent some from transferring their experience to new situations are described next, and the interventions that have been undertaken to surmount them. Finally, dominant approaches to studying and conceptualizing transfer are discussed, such as the preparation-for-future-learning view, information processing, and situated cognition approaches.
One definition of transfer is: “the carrying over of an act or way of acting from one performance to another” (Woodworth & Schlosberg, 1954, p. 734). Another definition is: “the ability to extend what has been learned in one context to new contexts” (Bransford, Brown, & Cocking, 1999, p. 39). A third definition is: “a situation where information learned at one point in time influences performance on information encountered at a later point in time” (Royer, Mestre, and Dufresne, 2005). The broadest of these conceptions suggests that transfer should be a widespread phenomenon: past learning must affect future performance frequently, and, indeed, formal schooling is predicated on this assumption. However, others disagree. Detterman points out: “Transfer has been one of the most actively studied phenomena in psychology … Reviewers are in almost total agreement that little transfer occurs” (1993, p. 8). Detterman goes on to suggest, therefore, that the educational implications of the assumption of transfer are misplaced: “Cognitive psychologists, and other people who should know better, continue to advocate a philosophy of education that is totally lacking in empirical support” (1993, p. 16). One wonders who is correct.
The disparity lies, in part, in what qualifies as successful transfer, both in terms of the extent and nature of the initial learning experience and in terms of the novelty and difficulty of the transfer test. The history of this debate goes back over a century to the debate between two famous early psychologists, Edward L. Thorndike (1874–1949) and Charles Judd (1873–1946), about the implications of their findings. Beginning around 1900, Thorndike and his colleagues reported a series of experiments finding poor or uneven transfer across tasks, despite dependence upon similar operations. For example, after training subjects to estimate the area of certain geometric shapes (e.g., 100 sq. cm rectangles), they did not transfer their learning to solve other problems concerning estimating geometric area, such as estimating the area of other rectangles and triangles (see Thorndike & Woodworth, 1901). By contrast, in Judd's 1908 experiment, boys throwing darts at a submerged target, adapted better to a change in target depth if they were given an explanation of the principle of optical refraction that made the underwater target appear to be at a different depth.
This disparity in experimental evidence regarding transfer success has continued into the 2000s. For example, Gick and Holyoak (1980) employed an analogical transfer experiment using the classic Dunker tumor radiation problem (how to destroy cancer cells within the body without damaging surrounding flesh by converging on the diseased cells from many directions so as to spread out and dilute the potentially damaging effects of the rays) and an analogous military situation (troops spread over many roads to avoid detonating weight-sensitive mines). They found that if subjects were told to think about the training problem, when tackling the transfer test, transfer was superior. By contrast, Reed, Ernst, and Banerji (1974) failed to demonstrate transfer on most performance measures, using the missionary-cannibal problem (how to get safely across a river in a limited-capacity boat without having the cannibals in the group ever outnumber the missionaries) and an analogous problem substituting wives and jealous husbands. Judd's underwater target findings suggest that a theoretical understanding of principles may be important for successful transfer, a claim supported by Brown's extensive body of work on transfer which also points to the need to foster theoretical understanding, though not necessarily by explicit instruction (e.g., Brown & Kane, 1988; Brown, 1989). (The above study by Gick and Holyoak also points to the need to foster a connection between the training and transfer materials.)
Given that the world outside of psychology experiments does not generally tell problem solvers which aspects of their experience are likely to be most relevant to current problem-solving situations, it could be argued that spontaneity is a requirement for true transfer and, therefore, that this work shows that such transfer is difficult to obtain. Further, much of the evidence for transfer described above is dismissed by some (e.g., Detterman, 1993) as demonstrating only near transfer, in which the transfer test is so close in some way to the training situation as to be trivial. This viewpoint argues that such near transfer does not count as true transfer because the situations in which society values transfer, such as from school learning to work performance, require far transfer, which is rarely obtained. However, others dispute this gloomy conclusion, claiming that training can enhance general thinking skills, which would constitute far transfer of learning (see, e.g., Perkins & Grotzer, 1997; Halpern, 1998). In sum, as of 2008, “There is little agreement in the scholarly community about the nature of transfer, the extent to which it occurs, and the nature of its underlying mechanisms” (Barnett & Ceci, 2002, p. 612).
The conflicting evidence concerning whether transfer occurs can be better understood if the disputed claims are dissected and the findings categorized according to the contextual and content dimensions along which transfer of learning has been assessed. Barnett and Ceci (2002) detailed a taxonomy of 6 context and 3 content dimensions along which transfer might be assessed and along which studies have suggested transfer success may differ, with near transfer (transfer to more similar contexts) generally resulting in easier transfer than far transfer (to more dissimilar contexts). The contextual dimensions include knowledge domain (e.g., physics vs. math), physical context (e.g., school vs. home), temporal context (e.g., in 5 minutes vs. in a month), functional context (e.g., as an academic exercise vs. a task for earning money), social context (e.g., individual vs. group) and modality (e.g., written vs. verbal).
The content dimensions along which transfer success may vary involve the nature of the skill to be transferred (e.g., rote procedure vs. abstract principle), the performance change measured for this skill (e.g., percentage of correct answers vs. speed of response), and the memory demands of the transfer task used to measure it (e.g., spontaneous recall vs. prompted recognition). For example, as suggested by Gick and Holyoak's 1980 work, discussed earlier, transfer assessed after prompting may be easier to achieve than transfer assessed by spontaneous recall. Transfer may also be more likely when “learning contexts are framed as part of a larger ongoing intellectual conversation in which students are actively involved” (Engle, 2006, p. 451). Engle conducted a detailed case study of fifth graders in a Community of Learners classroom and suggested that future research should take into account not just the content to be transferred but also the framing of the wider learning context. The use of the terms successful transfer and far transfer without specifying and discriminating between these various dimensions has been the source of much confusion.
Successful transfer requires encoding and subsequently identifying and retrieving relevant knowledge, followed by application of the knowledge to the transfer problem. Problems can occur at any or all of these stages. For example, knowledge may be adequately encoded but a student may fail to recognize its relevance. Successful near transfer combined with unsuccessful far transfer could be due to either a failure to recognize the relevance of the information, a failure to adapt the knowledge to the new situation, or encoding of such a specific interpretation of the initial learning situation that it is not applicable to the new situation. For example, Schliemann and Nunes (1990) found that school math class learning did not transfer to a fishing problem to which the same mathematical concepts could be applied (e.g., calculating proportions). This failure could be because the math class taught the material in a superficial manner, which did not lead to an understanding of the principles underlying the mathematical routines involved but merely showed the students how to mechanistically reproduce a very specific arithmetic procedure. Alternatively, it could be that the relevant knowledge was encoded, but the subjects failed to recognize the relevance of that knowledge to the fishing questions, perhaps because that knowledge was, for them, embedded in their knowledge of the school situation. This kind of situation-specificity was explored by Lave (1988) in her classic work with supermarket shoppers, which found a lack of evidence for transfer from school math to shopping; despite knowing how to carry out elementary arithmetical operations on a math test, the same procedures were not used to determine unit prices at the market.
Some efforts to resolve these issues have focused on hypothesizing about the precise components of knowledge encoded in the initial training and mapping them onto the components of the transfer problem. Singley and Anderson's 1989 production rule approach to modeling computer programming learning is of this type. However, applying such approaches to modeling complex and less well structured learning situations, or those in which similarities are abstract and less apparent, may be difficult.
Transfer processes may also be affected by motivation, which could influence initial learning, initiation of transfer attempts, spontaneity, and persistence in transfer tasks. Studies have shown that having mastery goals consistently predicts cognitive engagement and, in particular, results in activities such as deep processing and metacognitive strategies or insights into the workings of one's own cognitive systems (Pugh & Bergin, 2006). However, engagement does not always predict achievement, though this may be because of the kinds of achievement measures used, which may not reflect the deep processing associated with transfer (Pugh & Bergin, 2006). Performance goals may have damaging effects on engagement and transfer because a focus on avoiding a display of incompetence hampers deep processing and a focus on trying to do well shows mixed results. For example, a study by Bereby-Meyer and Kaplan (2005) found that, for a group of grade-school children, the performance-approach goal condition did not differ from the control condition when the goal was induced before encoding and performed worse than the control when the goal was induced after encoding but prior to the transfer test.
Experimental studies have generally found that learning experiences that promote deep, theoretical understanding are most conducive to transfer to different domains (see Barnett & Ceci, 2002, for a review). Deep understanding can be facilitated by encouraging learners to compare and contrast a variety of examples and by requiring them to explain and justify their decisions. A number of researchers have reached similar conclusions and have investigated ways in which training can promote such understanding. Generally, these approaches focus on getting the learners to engage with the training materials at a deep, structural level. Catrambone and Holyoak (1989) used comparison questions with multiple examples, which improved transfer, for example. Similarly, Cummins (1992) found that inter-problem processing (focus on comparison questions) promoted more transfer than intra-problem processing (focus on specific wording or details). Needham and Begg (1991) encouraged their subjects to fruitfully engage with learning materials by using problem-oriented training (e.g., trying to explain) in contrast to memory-oriented training. However, it is not obvious what deep theoretical understanding really means. Wagner (2006) has suggested that it may not necessarily be more abstract but instead may be “increasingly complex sensitivity to the contextual differences … encountered” (p. 4).
Halpern, Hansen, and Riefer (1990) suggested that hard work pays off in their study that enhanced subjects' ability to draw inferences from a studied passage by including far analogies in their training materials, presumably encouraging a focus on deep, structural processing. Their subjects did not derive the same benefit from a near analogy, which the authors suggest may be because they did not have to work as hard to make sense of it. Similarly, Reed and Saavedra (1986) showed that a task involving more concrete and effortful processing, termed the discovery method (running a computer simulation with feedback), improved performance more than a passive task (observing a computer-generated graph).
The merits of the discovery method of learning have been the subject of much debate. Chen and Klahr (1999) studied children's learning of the scientific method, specifically the “control of variables strategy” (the idea that one can figure out whether something is causal by changing it while holding all else the same). They compared training using probing questions with or without direct instruction and found that direct instruction was necessary for learning and transfer. Later, Klahr and Nigam (2004) compared the effectiveness of direct instruction and discovery learning on learning and transfer. They concluded that more children learned from direct instruction, challenging the “widely accepted claim in the science- and mathematics-education community … that discovery learning, as opposed to direct instruction, is the best way to get deep and lasting understanding” (p. 661). However, Kuhn (2005, 2006) disputes this conclusion, suggesting that “direct instruction appears to be neither a necessary nor sufficient condition for robust acquisition or for maintenance over time” (2006, p. 384) based on her research. Klahr (2005) disagrees with her interpretation of her findings, instead stating that “they show how difficult it is to achieve long-term transfer with anything less than extremely detailed and direct instruction” (p. 871). Further research is needed to resolve this debate.
The constructivist view of learning argues that the challenge is for learners to construct new knowledge for themselves and that one factor which determines their ability to do so is their level of prior knowledge (Bransford et al., 2000). For example, Schwartz and Bransford (1998) demonstrated that preparatory work, such as generating distinctions between contrasting cases in psychological experiments, enhanced future learning from a lecture, as measured by transfer one week later. They proposed a distinction between this preparation-for-future-learning view and a sequestered approach which does not provide access to outside sources of information, suggesting that much classical transfer research is from the latter paradigm and advocating for the former. Sequestered problem solving is inadequate as an assessment of learning because it artificially restricts the problem solver's access to other sources of information which are an integral part of problem solving. Their preparation-for-future-learning view advocates exploration of how past learning prepares the learner for future learning in contrast to the sequestered problem-solving approach. However, learning and transfer could theoretically be assessed in an information-poor or an information-rich environment (sequestered or not), independent of the transfer measure used (problem-solving performance or enhanced future learning).
The broader concept of transfer suggested by the prep-aration-for-future-learning view fits within the tradition of studying more generalized outcomes such as the development of practical intelligence (see Sternberg & Kalmar, 1998) and metacognitive and critical thinking skills. As stated by the renowned transfer researcher, the late Ann Brown, “Effective learners operate best when they have insight into their own strengths and weaknesses and access to their own repertoires of strategies for learning. For the past 20 years or so, this type of knowledge and control over thinking has been termed metacognition” (Brown, 1997, p. 411). Halpern's 1998 critical thinking program is an attempt to boost such general reasoning skills: “to promote the learning of transcontextual thinking skills and the awareness of and the ability to direct one's own thinking and learning” (p. 451). Halpern's program of instruction builds skills such as verbal reasoning, argument analysis, hypothesis testing, probability, and decision-making. Training also aims to promote transfer by focusing on awareness of which skills to use and providing practice with a broad range of examples, supported by feedback and probing questions. One of the goals is to develop the rich, interconnected knowledge structures—the deep understanding that Brown (1989) suggested is important for transfer. In line with this view, the National Research Council's Committee on the Development of the Science of Learning (Bransford, Brown, & Cocking, 1999) concluded that transfer can be facilitated by training students in metacognitive awareness through activities that encourage introspective awareness and self-monitoring.
Some cognitive scientists have studied much more tightly specified learning and transfer processes. Holland and colleagues (1986) described the mechanism of analogical transfer, in terms of four steps: “encoding of the target, selection of a source analog, mapping of the source and target, and transfer of knowledge to the target by generation of new rules” (p. 307). Each step can then be broken down further, for example, encoding involves representing the problem in terms of the initial state, goal state, relevant operators, and path constraints. Singley and Anderson (1989; Anderson 1993) took this information processing approach further and proposed that cognitive skills can be understood in terms of production rules which represent knowledge, which can then be instantiated in, for example, computerized tutors for teaching programming skills.
Such decontextualised approaches have been criticized (see, e.g., Lave, 1988) for neglecting the embeddedness of learning and knowledge in context. Lave has advocated a strong view of this “situated cognition” hypothesis and suggests that learning is inextricably entwined with the context in which it was acquired: “Cognition … is distributed—… not divided among—mind, body, activity and culturally organized settings (which include other actors)” (p. 1). More nuanced interpretations suggest that aspects of context may moderate transferability (Barnett & Ceci, 2002).
Lobato (2003) distinguished between what she called the classical transfer approach and actor-oriented transfer and suggested that the apparent failure to transfer in many traditional experiments was, in fact, a failure on the part of the experimenters to adopt the perspective of the learner. On this view, success or failure on the particular outcome measures the researcher had in mind is immaterial; what matters is how the learners make connections between the new and old situations. This approach often uses case studies as an investigative method, either alone (see, e.g., Lobato & Siebert, 2002) or in conjunction with group data (see, e.g., Dufresne et al., 2005). Lobato (2006) contrasts the classical transfer approach, of using highly controlled experiments and statistical analysis to assess whether particular learning opportunities result in predetermined differences on carefully designed transfer assessments, with her actor-oriented approach, using ethnographic methods to search for effects of prior learning on novel situations. This method increases the likelihood of capturing subtle effects on future performance that may not have been the intended lesson to be learned, but that may nevertheless provide insight into the mechanisms underlying the transfer process and generate hypotheses for further investigations. She emphasizes that generalization involves the construction of relationships rather than simply the reproduction of existing relations.
However, the implication that traditional transfer researchers have ignored the wider effects of prior learning and focused exclusively on a narrow, experimenter-imposed measure of transfer is somewhat misleading. Traditional researchers have often devised multiple assessments of learning outcomes and explored the consequences of many experimental manipulations to capture the richness of the learning and transfer processes, as well as used microgenetic methods to understand the step-by-step processes of learning (see, e.g., Brown & Kane, 1988; Chen & Klahr, 1999).
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