The Effect of Practice on Strategy Change

The Effect of Practice on Strategy Change Suzanne C. Charman (CharmanSC1@cardiff.ac.uk) School of Psychology, Cardiff University, Cardiff CF10 3YG, Wales, United Kingdom Andrew Howes (HowesA@cardiff.ac.uk) School of Psychology, Cardiff University, Cardiff CF10 3YG, Wales, United Kingdom Abstract Some effort has been made to examine why people often do not adopt efficient strategies, however little attempt has been made to determine the conditions that support strategy generation. This study examines how practice may lead to efficient strategy generation, through perceptual noticing and the elaboration of a device representation. Forty-three participants were required to complete drawing tasks in MS PowerPoint, for which there are a number of possible strategies that share the same component operators, and yet vary in efficiency. Merely by practicing a component of a less efficient strategy, a more efficient strategy was generated earlier on in the experiment. Further, the efficiency of the strategy used at test was correlated with device knowledge. It is proposed that through practice a user’s device representation becomes elaborated, and this in turn leads to strategy generation. The possibility of a perceptual noticing mechanism for problem solving was also investigated, however providing strong perceptual groupings did not aid strategy generation. Introduction A reliable finding in experimental psychology is that practice on a task leads to faster and less erroneous performance of that task. In the 1920s Snoddy (1926, as cited in Anderson, 1995) graphed learning data and demonstrated that a power function characterises the rate of improvement in the performance of a task. This means that with practice the speed at which people complete a task increases with diminishing returns (Newell and Rosenbloom, 1981). A growing body of evidence suggests that power law learning occurs within strategy, and not with regards to the task as a whole (Rickard, 1997; Delaney, Reder, Staszewski and Ritter, 1998). Compton and Logan (1991) suggest that improvement in the performance speed of a task is often due to the adoption of more refined and effective strategies. However, there appears to be evidence that people do not adopt efficient strategies as rapidly as might be expected. Early explanations for this failure centred around the Einstellung effect, where prior experience results in a reluctance to investigate alternative procedures for a novel task (Luchins, 1942). Inefficient use of a device was also investigated using the ACT theory of skill acquisition (Anderson, 1982) where procedures that hinder completion of a task are discarded upon detection, however procedures that are sufficient and yet inefficient are less easily identified and so are maintained. In considering the problem of inefficient device use, Carroll and Rosson (1987) suggest that people are trapped by a production paradox, meaning that users are so focused on completing tasks, that they are unlikely to take time out to learn about a device. Paradoxically, if time were spent learning about the device, performance might be improved in the long term. More recent evidence suggests that even people who are very skilled at a set of tasks are also not likely to operate as efficiently as might be expected (Nilsen et al., 1993; Nilsen, Jong, Olson and Polson, 1992). For example Young and MacLean (1988) found users do not always choose the faster method when presented with several different routines. Bhavnani and John (1997) observed that even after several years of experience and formal training in a particular CAD package, many users had not adopted efficient strategies. Further, the use of inefficient strategies impacted upon completion times and caused an increase in low level error. Bhavnani, John and Flemming (1999) highlight the difficulty of moving from sufficient use to a more efficient use of computer applications (p.183). The reason for this inefficiency, they suggest, is not related to the standard of interface design or experience with the package, but to strategic knowledge. Once participants received both traditional command based training (‘learning to do’) and strategic knowledge training (‘learning to see’ or recognize opportunities for efficient strategies to be used), it was found that most tasks were performed using efficient strategies (Bhavnani et al., 1999; Bhavnani, in press). However, the relative success of those who received strategic knowledge training should not be too surprising, as during the extensive training stages participants were explicitly taught each strategy. The tests therefore are more of an ability to recall and apply efficient strategies to novel situations, rather than strategy generation per se. Some would argue that the use of strategies, that on the surface appear inefficient, could actually be rational.