Abstract
Historically, the notion of transfer has been very controversial, conceptually as well as empirically. Therefore, there is an obvious need for further inquiry aimed at a better understanding of the processes underlying transfer. Taking into account the recent literature, this article defines transfer as the broad, productive, and supported use of acquired knowledge, skills, and motivations in new contexts and learning tasks. As an illustration, an intervention study is briefly discussed. This study shows the possibility of designing a powerful learning environment that yields transfer effects in accordance with this reconceptualized perspective on transfer.
Throughout history, educators have attempted to equip students with cognitive tools that they can apply beyond the initial learning context. At present, transfer of learned knowledge and skills is still considered a fundamental goal of education. It is, for instance, expected that the teaching of reading comprehension will facilitate students' reading and understanding of texts in other subject-matter domains and outside the classroom. In addition, the field of industrial and corporate training is strongly interested in the transfer of learning. For example, an employer may attempt to teach clerks to use a text-processing program in such a way that afterward they can easily and quickly acquire mastery of a new program.
The scientific study of transfer dates back only to the beginning of the previous century. From the start, the concept has been very controversial, conceptually as well as empirically. The empirical literature contains many failures to achieve transfer, but also many successful demonstrations of transfer. At the conceptual level, researchers argue for divergent conceptions of transfer that reflect different schools in psychology. For instance, from a behavioristic perspective, for transfer to occur the transfer task must share specific identical elements with the original learning task. But from a cognitive psychology standpoint, the transfer of general skills, such as problem-solving strategies, can occur (De Corte, 1999).
In this article, I attempt to explain and overcome the empirical and conceptual discords by reconceptualizing transfer in terms of the productive use of cognitive tools and motivations. I discuss an intervention study illustrating the possibility of designing learning environments that yield transfer effects in accordance with the revised perspective on transfer. The article ends by touching on some major issues for further inquiry.
RECONCEPTUALIZING TRANSFER
Proposals to reconceptualize the transfer construct are making an important contribution toward advancing theory and research. An analysis of the literature shows that traditionally transfer was very narrowly conceived as the independent and immediate application of knowledge and skills acquired in one situation to another. Accordingly, narrow criteria of successful transfer were adopted. Bransford and Schwartz (1999) called this narrow definition the direct-application theory of transfer. In this framework, the key question is, can people apply something they learned directly and independently to a new setting? A typical characteristic of this approach to transfer is that the final transfer task (i.e., the experimental task that is used to test whether transfer has taken place) takes the form of sequestered problem solving. That is, while solving the transfer task, subjects do not get opportunities to invoke support from other resources, such as texts or colleagues, or to try things out, receive feedback, or revise their work.
As an alternative to this view, Bransford and Schwartz proposed a broader perspective emphasizing preparation for future learning (PFL) as the major aspect of transfer. Under this framework, the focus in assessing transfer is on subjects' abilities to learn in novel, resource-rich contexts. This view is much more in line with the now-prevailing notion of learning as an active and constructive process, but emphasizes in addition the active nature of transfer itself. Indeed, in this approach a novel context is not conceived as just “given”; using one's prior knowledge and the available resources, one can modify the situation and its perception. For instance, confronted with a fellow learner's perspective about a problem situation, one can revise one's own perception of the problem. In this respect, Bransford and Schwartz also emphasized the important role of metacognitive (or self-regulatory) skills. Such active control of the transfer situation is lacking in the direct-application model. Another benefit of the PFL model of transfer is that it suggests affective and motivational qualities, in addition to cognitive skills, are candidates for transfer.
The PFL approach is convergent with a redefinition of transfer by Hatano and Greeno (1999), who criticized traditional models of transfer for both treating knowledge as a static property of an individual and adopting inappropriately narrow criteria of successful transfer. They considered the conceptualization of transfer as the direct application of acquired elements from one situation to another as incompatible with current perspectives on the contextualized or situated nature of knowledge. That is, the direct-application theory is static, in the sense that it neglects how aspects of thinking that arise from interactions among people, and between people and other material and informational systems, might affect performance in the transfer situation. Hatano and Greeno proposed replacing the term transfer with the term productivity, to refer to the generality of learning (i.e., the degree to which learning in some situation has effects on task-related activities in a variety of other situations). The latter situations can—in accordance with the PFL perspective—involve hints or other kinds of support that facilitate the recall of relevant prior knowledge. Hatano and Greeno rightly claimed that in everyday learning environments, people rarely need to use previously acquired knowledge and skills without also having access to external support.
There is thus a strong tendency toward reconceptualizing transfer, emphasizing the broad, productive, and supported use of acquired knowledge, skills, and motivations, as opposed to the direct and sequestered application of skills from one situation to another. From an educational perspective, it is important to focus on the implications of this reconceptualization of transfer. This more expanded definition should enable researchers and instructors to design powerful learning environments that enhance students' preparation for future learning and allow them to make productive use of their acquired knowledge, skills, and motivations. This reconceptualization is in line with a constructivist and situated perspective on learning: Learners are not passive recipients of information, but they actively construct their knowledge and skills in narrow interaction with the physical and social context in which learning occurs. Given this, and because of the important role of metacognitive skills for successful transfer (Mayer & Wittrock, 1996; see also National Research Council, 2000), principles for designing powerful learning environments in general (thus when the focus is not on transfer; see, e.g., De Corte, in press) are also appropriate for designing learning environments that focus on transfer, that is, on fostering students' PFL and their competence in broadly applying their cognitive and motivational potential.
There is now a rather broad consensus about the following principles for the design of powerful teaching-learning environments.
First, environments that foster the productive use of knowledge, skills, and motivations should support constructive learning processes in all students, including the passive ones. However, emphasizing the active nature of learning does not imply that teachers should stand back and let students do whatever they want. The claim that productive learning is facilitated by good teaching still holds true.
Second, powerful learning environments should enhance students' cognitive and motivational self-regulation. There is evidence that learners who have a high degree of self-regulation also tend to be highly motivated and competent in using their knowledge productively (see, e.g., National Research Council, 2000). Thus, external regulation of learning should be gradually removed so that students become agents of their own learning and transfer.
Third, as mentioned earlier, students' cognitive tools and motivational qualities are acquired in narrow interaction with the social context of learning. Therefore, sociocultural supports for learning, such as interaction and collaboration, can promote the broad application of these learned elements (Hatano & Greeno, 1999; Volet, 1999).
Fourth, the situated character of learning also means that preparation for future learning can be fostered by confronting students with challenging problems that have personal meaning for them, and are representative of tasks they will encounter in the future.
Fifth, powerful learning environments should create a classroom culture that induces students to articulate and to reflect on their cognitive and motivational processes during learning and problem solving. Indeed, to become productive and self-regulated users of their cognitive and motivational potentials, students should be aware of them, and believe that they are worthwhile and useful.
A LEARNING ENVIRONMENT THAT FACILITATES PRODUCTIVE USE OF STUDENTS' POTENTIAL
Starting from a constructivist perspective on learning, Masui and I carried out an intervention study that illustrates the possibility of designing a powerful learning environment that yields effects in accordance with the revised perspective on transfer (Masui & De Corte, 1999). More specifically, we examined whether students trained in orienting and self-judging could subsequently use these abilities productively, and what the effects on academic performance might be.
Orienting is a cognitive self-regulation activity, and involves preparing oneself to learn and solve problems by examining givens and characteristics of the task, by thinking of possible and desirable goals and cognitive activities, and by taking account of prior knowledge, interest, capacities, and contextual factors. Self-judging is a motivational self-regulation activity related to orienting; indeed, orienting activities relating to a given task provide opportunities to assess one's personal qualities and competencies (e.g., prior knowledge and attitudes) as a learner and problem solver. Self-judging is motivational in the sense that it helps students to make an accurate appraisal of the effort needed to accomplish a task successfully.
Over the course of 7 months, in 10 sessions of 90 min each, both orienting and self-judging skills were taught to 47 university freshmen studying business economics. Focusing on the course in microeconomics, the students were asked to complete a number of exercises and homework assignments aimed at practicing and transferring knowledge and skills. We designed the learning environment using the following guidelines, which are in accordance with the principles presented earlier and, thus, with major implications of the revised perspective on transfer as the productive use of cognitive tools and motivational qualities:
Embed the acquisition of knowledge and skills in the real study context;
Link activities of orienting and self-judging to students' personal goals;
Sequence learning tasks and teaching methods in such a way that learning becomes progressively more self-regulated;
Use a variety of forms of class organization and social interaction, namely, modeling, individual assignments, working in pairs, small-group work, and whole-class discussion;
Stimulate articulation of and reflection upon learning and problem-solving processes;
Create ample opportunities for practice and for the productive use of acquired knowledge and skills in new tasks and problem situations.
To foster skill in self-judging, we gave students self-judging assignments concerning their examination experiences and their exam results after the first and second semesters of the academic year.
We assessed the effects of this learning environment by comparing the performance of this group of students after the intervention with the performance of two equivalent control groups. More specifically, we assessed whether students were able to use the trained skills of orienting and self-judging productively for studying a course that was not involved in the intervention, namely, statistics. Thus, we adopted a much broader criterion of successful transfer than is embodied in the traditional sequestered problem-solving task. The students' preparedness for studying statistics was assessed indirectly by asking them to provide specific orienting information relating to the statistics course (e.g., “How much time do you think you will have to invest weekly in the theoretical and practical parts of the statistics course, including the lessons?”), and to complete a self-judging assignment (“Do you think that the statistics course will be easy or difficult for you? Explain your answer.”).
The results showed that after the initial intervention, students in the experimental group, compared with those in the control groups, had more elaborate and relevant metacognitive knowledge of orienting in a study task as well as of self-judging. Moreover, and of great importance in light of the previous discussion, the students in the experimental group were significantly better able than those in the control groups to use both acquired skills effectively in the statistics course. They could better orient themselves toward studying statistics, and they gave more evidence of self-judging with regard to their competence to study statistics. For instance, they formulated more personal statements to support their self-judgment. Furthermore, a positive correlation was found between orienting and self-judging behavior and academic performance in the course. A more detailed analysis of the data showed that whereas students' characteristics assessed before the intervention (e.g., intelligence, prior academic knowledge) accounted for a notable amount of the variability in academic performance, adding orienting and self-judging behavior to the analysis substantially increased the amount of variability accounted for. In sum, students in the experimental group were better prepared for a new learning task. They were able to productively use the acquired cognitive and motivational skills in a novel context (i.e., the new course).
Evidence in line with the broader approach to transfer has also been reported by other scholars. An excellent example is Volet's (1999) study of the transfer of learning-related cognitions, motivations, and behaviors acquired by Asian students in their home country. When these students were in the less familiar Western-oriented cultural-educational context of an Australian university, the learning results that appeared to transfer well across the two different cultural and educational contexts were those that related to the students' fundamental beliefs about learning (e.g., their high achievement motivation, deep approach to learning, belief in the importance of effort for learning, and recognition of the benefits of interactive and collaborative forms of learning). Interesting results with primary-school pupils have also been found, for instance, in the Fostering Communities of Learning (Campione, Shapiro, & Brown, 1995), and Jasper projects (Cognition and Technology Group at Vanderbilt, 1997). For example, in the Fostering Communities of Learning project, sixth-grade students who had acquired reading and argumentation strategies in the context of biology and environmental science were later able to use these strategies flexibly for learning in other content areas.
CONCLUSION
The traditional approach to transfer has recently been criticized, especially because it takes a narrow view of the criterion for evidence of transfer and neglects the active, constructive, interactive, and contextualized nature of learning. This criticism has led to the emergence of a reconceptualized view of transfer in terms of two related constructs, namely, preparation for future learning and productivity of learning results. As shown by the example involving orienting and self-judging training, an instructional intervention based on the current understanding of principles for designing powerful learning environments can yield successful transfer, conceptualized in this way.
However, considering the complexity of the transfer phenomenon, important questions must be answered before a well-elaborated explanatory theory of transfer can be developed. Continued research is needed to improve understanding of the processes underlying transfer; to unravel the components of learning environments that facilitate the productive use of acquired knowledge, skills, and motivations; and to design alternative forms for assessing transfer.
The complexity of transfer derives from the involvement of several interacting categories of variables, namely, learner characteristics, learning and transfer tasks, and instructional and transfer contexts. Future research is needed to disentangle the processes whereby these different categories of variables either facilitate or inhibit transfer. For instance, although there is empirical evidence that metacognitive skills, a learner variable, foster transfer, the mechanisms underlying these facilitating effects have yet to be determined.
With respect to task variables, transfer distance has been a major issue of debate. Traditionally, the term refers to the degree of difference between the original learning task and the transfer task. It has been shown, however, that transfer distance cannot simply be reduced to a matter of degree of difference. Rather, qualitative task features and their mutual relations influence transfer. Continued inquiry is needed to determine what the important qualitative features are, and how they affect the extent of transfer that is achieved. Furthermore, substantial attention should be paid to clarifying more analytically the instructional conditions and processes that boost transfer. Indeed, because intervention studies such as the one I described involve complex learning environments, it is impossible to establish the relative importance of the different components that produce observed transfer effects.
Finally, a major challenge for research is to develop alternative assessment forms aligned with the revised transfer concept. Traditional static, standardized tests of achievement should be replaced by dynamic assessments that allow students to demonstrate how their past learning activities and experiences have equipped them to approach new learning tasks.