Academic journal article Memory & Cognition

# Goal Specificity and Knowledge Acquisition in Statistics Problem Solving: Evidence for Attentional Focus

Academic journal article Memory & Cognition

# Goal Specificity and Knowledge Acquisition in Statistics Problem Solving: Evidence for Attentional Focus

## Article excerpt

Solving training problems with nonspecific goals (NG; i.e., solving for all possible unknown values) often results in better transfer than solving training problems with standard goals (SG; i.e., solving for one particular unknown value). In this study, we evaluated an attentional focus explanation of the goal specificity effect. According to the attentional focus view, solving NG problems causes attention to be directed to local relations among successive problem states, whereas solving SG problems causes attention to be directed to relations between the various problem states and the goal state. Attention to the former is thought to enhance structural knowledge about the problem domain and thus promote transfer. Results supported this view because structurally different transfer problems were solved faster following NG training than following SG training. Moreover, structural knowledge representations revealed more links depicting local relations following NG training and more links to the training goal following SG training. As predicted, these effects were obtained only by domain novices.

The goals imposed by a problem-solving task influence what is learned during problem solution. Sweller and Levine (1982) were among the first to show that nonspecific goals (NG; e.g., solving for as many unknowns as possible) benefit learning relative to specific goals (SG; e.g., solving for specific unknowns), a finding that is called the goal specificity effect. As a demonstration of the goal specificity effect, Sweller, Mawer, and Ward (1983) had participants solve multistep transformation problems in the domain of physics (Experiments 2 and 3). Half of the participants were given problems with specific goals (e.g., "In 18 sec a racing car can start from rest and travel 305.1 m. What speed will it reach?"), whereas the other half were given problems with nonspecific goals (e.g., "In 18 sec a racing car can start from rest and travel 305.1 m. Calculate the value of as many variables as you can."). Participants given problems with nonspecific goals showed more expert-like performance than did those given problems with specific goals. Nonspecific goals led to fewer errors, and to use of forward-working strategies, which have been shown to be characteristic of experts (Larkin, McDermott, Simon, & Simon, 1980). Since then, the goal specificity effect has been shown with geometry (Ayres, 1993; Sweller et al., 1983, Experiments 4-7), trigonometry (Owen & Sweller, 1985; Sweller, 1988), and several more complex, dynamic tasks (e.g., Burns & Vollmeyer, 2002; Geddes & Stevenson, 1997; Miller, Lehman, & Koedinger, 1999; Vollmeyer, Burns, & Holyoak, 1996). The aim of this article is to examine the processes underlying the goal specificity effect and the generality of knowledge acquired as a result of training with nonspecific versus specific goals. We first introduce two related explanations of the goal specificity effect, and we then describe the different types of knowledge that are presumed to result from specific versus nonspecific goal training.

In the course of problem solving, individuals are assumed to learn schematic knowledge about the problem domain, or at least to learn about the particular type of problem being solved. By schematic knowledge, we mean either general knowledge of the relationships between important domain concepts, as exemplified in high-level principles, or specific knowledge of particular problem types and the appropriate equations needed to solve them. More will be said later about the generality of the knowledge acquired in the course of problem solving. In either the general or more specific sense, schematic knowledge is believed to underlie expertise (e.g., Chi, Feltovich, & Glaser, 1981) and to allow relatively quick and error-free solutions. Consider the physics problem given earlier. Individuals solving this type of problem may learn the relationships between time, distance, and final velocity, or they may learn to recognize this problem type and what equations to apply in order to solve it (i. …

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