The Cognitive Basis of Knowledge Transfer

Gick, Mary L.; Holyoak, Keith J.

doi:10.1016/b978-0-12-188950-0.50008-4

Cited by 302 publications

(259 citation statements)

References 51 publications

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“…The issue here is that individuals have to make inferences as to the applicability of lessons learned in the context of past experiences to the task presently at hand. As task heterogeneity increases, inferences become more difficult to make and, when made, they are more likely to generate inappropriate generalizations and poorer performance (Cormier and Hagman, 1987;Gick and Holyoak, 1987;Holland et al 1986;Holyoak and Thagard, 1995). How does the degree of task heterogeneity affect the relative effectiveness of the capability-building mechanisms?…”

Section: H1mentioning

confidence: 99%

Deliberate Learning and the Evolution of Dynamic Capabilities

Zollo

Winter

2002

Organization Science

5,109

132

4,771

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This paper investigates the mechanisms through which organizations develop dynamic capabilities, defined as routinized activities directed to the development and adaptation of operating routines. It addresses the role of (1) experience accumulation, (2) knowledge articulation, and (3) knowledge codification processes in the evolution of dynamic, as well as operational, routines. The argument is made that dynamic capabilities are shaped by the coevolution of these learning mechanisms. At any point in time, firms adopt a mix of learning behaviors constituted by a semiautomatic accumulation of experience and by deliberate investments in knowledge articulation and codification activities. The relative effectiveness of these capability-building mechanisms is analyzed here as contingent upon selected features of the task to be learned, such as its frequency, homogeneity, and degree of causal ambiguity. Testable hypotheses about these effects are derived. Somewhat counterintuitive implications of the analysis include the relatively superior effectiveness of highly deliberate learning processes such as knowledge codification at lower levels of frequency and homogeneity of the organizational task, in contrast with common managerial practice. The authors would like to acknowledge the support received from INSEAD's R&D department and the Reginald H. Jones Center of the Wharton School. We are also grateful for the many suggestions, comments and critiques generously offered by Ron Adner, Max Boisot, Tim Folta, Anna Grandori, Bruce Kogut, Arie Lewin, Bart Nooteboom, Gabriel Szulanski, Christoph Zott and three anonymous reviewers Of course, we remain responsible for any remaining error and omissions. In its working paper version, this paper was titled "From Organizational Routines to Dynamic Capabilities." 2 Deliberate Learning and the Evolution of Dynamic Capabilities Abstract:This paper investigates the mechanisms through which organizations develop dynamic capabilities, defined as routinized activities directed to the development and adaptation of operating routines, and reflects upon the role of (1) experience accumulation, (2) knowledge articulation and (3) knowledge codification processes in the evolution of dynamic, as well as operational, routines. The argument is made that dynamic capabilities are shaped by the co-evolution of these learning mechanisms. At any point in time, firms adopt a mix of learning behaviors constituted by a semi-automatic accumulation of experience and by deliberate investments in knowledge articulation and codification activities. The relative effectiveness of these capability-building mechanisms is analyzed here as contingent upon selected features of the task to be learned, such as its frequency, homogeneity and degree of causal ambiguity, and testable hypotheses are derived. Somewhat counterintuitive implications of the analysis include the relatively superior effectiveness of highly deliberate learning processes, such as knowledge codification, at lower levels of frequency and homo...

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Section: H1mentioning

confidence: 99%

Deliberate Learning and the Evolution of Dynamic Capabilities

Zollo

Winter

2002

Organization Science

5,109

132

4,771

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“…Schoenfeld (1979) suggested that students can learn to do mathematical proofs from examples more effectively if heuristics for determining when to apply a particular approach are presented along with the examples. In addition, researchers have argued that learners often need explicit guidance concerning the underlying concepts of examples in order to be able to apply the examples to new problems (Gick & Holyoak, 1987;Mayer & Greeno, 1972;Stein et al , 1982). Lewis and Anderson (1985) showed that learners were more likely to learn correspondences between the givens in a geometry problem and appropriate solution methods for new problems if the leamers explicitly indicated during training wh ich method they thought was appropriate for each example and what information they wou1d need in order to solve it.…”

Section: Methodsmentioning

confidence: 99%

Learning subgoals and methods for solving probability problems

Catrambone

Holyoak

1990

Memory & Cognition

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We hypothesize that typical example problems used in quantitative domains such as algebra and probability can be represented in terms of subgoals and methods that these problems teach learners. The "quality" ofthese subgoals and methods can vary, depending on the features ofthe examples. In addition, the likelihood of these subgoals' being recognized in novel problems and the likelihood of learners' being able to modify an old method for a new problem may be functions of the training examples learners study. In Experiment 1, subjects who studied examples predicted to teach certain subgoals were often able to recognize those subgoals in nonisomorphie transfer problems. Subjects who studied examples demonstrating two methods rather than one exhibited no advantages in transfer. Experiment 2 demonstrated that ifthe conditions for applying a method are highlighted in examples, learners are more likely to appropriately adapt that method in a novel problem, perhaps because they recognize that the conditions do not fully match those required for any of the old methods. Overall, the results indicate that the subgoal/method representational scheme may be useful in predicting transfer performance.A consistent finding in the problem-solving literature is that learners are often unable to make appropriate use of prior information to solve new problems if the new problems differ from training examples in more than minor ways (e.g., Anderson, Farrell, & Sauers, 1984;Gick & Holyoak, 1980Reed, Dempster, & Ettinger, 1985; Spencer & Weisberg, 1986). Learners often seem to acquire primarily superficial knowledge from training examples, consisting of little more than aseries of memorized steps. For instance, some of the examples used in the present experiments involve finding the average number of times an event OCCUfS per trial (such as the average number of errors per game made by the Detroit Tigers' infield) given the frequencies of the various subevents (e.g., the number of games in which the infield made°errors, the number of games in which they made exactly I error, and so on). Learners are good at memorizing, from examples, the steps of multiplying each subevent (0, I, etc.) by its observed frequency, summing the results, and dividing by the number of trials in order to achieve the subgoal of finding the average frequency. However, they rarely notice that the steps of multiplying each subevent by its frequency and then summing them could be viewed as a way of finding the total frequency of the event. As a result, these learners are usually unable to find the average frequency of an event when a problem provides the total frequency of that event directly, rather than requiring that it be derived from the frequencies of the various subevents.In the present paper, we will argue that the knowledge people gain from examples in domains that emphasize solving problems, such as probability , can be represented by subgoals and methods. Furthermore, manipulations of examples can affect which subgoals and methods a learner acquires, th...

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“…The process of a student's cognitive evolution based on the accumulation of incoming information to the low initial level of a student's knowledge is called the process of a student's adaptation to a studied course-material (Gick & Holyoak, 1987). Thus, the most typical feature of adaptation consists of accumulation and immediate use of incoming information aimed at increasing a student's current level of knowledge.…”

Section: A Cybernetic Approach To Cognitive Evolution: the Dte Modelmentioning

confidence: 99%

“…One of the major problems for teachers is to create an educational environment that allows students to direct their own learning-cognitive activity (Garofalakis et.al., 2002;Gick & Holyoak, 1987;Glaserfeld, 1989). Self-directed learning assumed particular importance at the time when on-line learning came into existence (Evans & Sabry, 2003;Dearholf et al, 2004).…”

Section: Management Of Students' Learning-cognitive Activitymentioning

confidence: 99%