Transfer, and the Effects of Context Outside of the Training Task

Transfer, and the Effects of Context Outside of the Training Task Samuel B. Day (day9@indiana.edu) Sarah Manlove (smanlove@indiana.edu) Robert L. Goldstone (rgoldsto@indiana.edu) Dept. of Brain & Behavioral Sciences, 1001 E. 10 th St. Bloomington, IN 47405 USA Abstract While the use of concrete, contextualized and personally relevant examples can benefit learners in terms of comprehension and motivation, these types of examples can come with a cost. Examples may become too bound to their particular context, and individuals may have a difficult time recognizing when the underlying principles are relevant in new situations. In the current study, we provide evidence that contextualization may impair knowledge transfer even when that context occurs outside of the training example itself. Specifically, when students were taught about positive feedback systems in the context of polar ice-albedo effects, those individuals that had previously learned about the effects of global warming on polar bear populations showed reliably poorer transfer performance. Introduction In virtually all educational domains, the ultimate goal of learning is not simply to acquire some static body of specific facts. Rather, the objective is to gain more general kinds of knowledge structures that can be applied in new situations and under novel conditions. For example, computer programmers may learn very general algorithms that can be used in a variety of different tasks and instantiated in very different programming languages; students of literature or history learn about themes and patterns that can occur across a wide variety of situations that are superficially dissimilar; and those learning about science may discover principles that are relevant not only across different contexts, but even across disciplines. In short, the goal of learning is primarily to acquire generalizable knowledge that may be used productively. Unfortunately, it is not immediately clear what the most effective means of conveying this kind of knowledge might be, and the data on this topic can seem counterintuitive or contradictory. This is a critical area to understand, however. Instructors have a great deal of latitude in their selection of teaching examples and methods, and research suggests that even subtle differences in these choices may have an important impact on students’ learning. The current study extends our understanding of this issue. Specifically, we explore the ways in which the context surrounding a particular training example may influence what students learn, and their ability to apply this knowledge to new cases. One straightforward approach of conveying generalizable knowledge is to present information in a highly abstracted way, removing any context-specific details and features. For example, consider the concept of positive feedback systems. Such systems are ubiquitous in science, and can be instantiated in an almost limitless number of ways. In order to maximize the set of situations where a learner’s knowledge can be applied, this concept could be presented in a way that is not specific to any particular context, such as: “A system in which increases to a variable cause still further increases to that variable.” Such a general definition would make the concept applicable to a wide range of domains, capturing positive feedback phenomena in biology, physics, chemistry and even interpersonal interactions. However, it has long been argued that while such abstract presentations might capture the relevant information efficiently, they do so at the expense of comprehensibility (e.g., Bruner, 1966), making this approach ultimately counterproductive. Learners cannot apply information that they do not understand. Consistent with this, research has found that people tend to rely on more concrete examples when possible. For example, in one study (LeFevre & Dixon, 1986) researchers provided participants with explicit verbal instructions on how to perform a task, while also giving them a concrete example of the task being performed. However, for some individuals, those sources of information conflicted with each other, and actually reflected different tasks and goals. Under those conditions, participants were overwhelmingly more likely to act on the basis of the concrete example rather than the more abstract verbal instructions. Similarly, Ross (1987) found that even when students were given the appropriate mathematical formula to use in solving a story problem, their performance was influenced by the concrete examples they had previously seen (see also Anderson, Farrell, & Sauers, One of the most striking examples of the merits of contextualization is the Wason selection task, which leads to uniformly poor performance (typically about 10% correct) when presented in its abstract symbolic form, but is often solved when instantiated in a familiar context (e.g., Johnson-Laird, Legrenzi, & Legrenzi, 1972; Wason & Shapiro, 1971). As we will discuss, however, there are issues with learning from contextualized training examples as well. First, it can be challenging to define exactly what contextualization means. At one end of the spectrum, it could simply refer to the concrete perceptual features that are associated with a situation. For example, instructors could teach a principle with an animated simulation, using