Resource-adaptive Selection of Strategies in Learning from Worked-Out Examples

Resource-adaptive Selection of Strategies in Learning from Worked-Out Examples Peter Gerjets, Katharina Scheiter & Werner H. Tack Collaborative Research Center 378: Resource-Adaptive Cognitive Processes University of the Saarland D-66041 Saarbruecken/Germany {pgerjet, katharis, tack}@cops.uni-sb.de Abstract Most tasks can be pursued by using different strategies (Logan, 1985; Reder & Schunn, 1998). In this paper we focus on strategies of learning from worked-out examples. Within a resource-oriented framework these different strategies can be classified according to their costs and benefits. These features may determine which strat- egy will be selected for accomplishing a task in situations with certain resource limitations. We investigate specific hypotheses about strategic adaptations to resource limitations (e.g., time pres- sure or lack of prior knowledge) within a hypertext-based learning environment. A comparison of the strategy selection of good and poor learners is used to assess the degree of subjects’ resource adaptivity. Ideas for modeling resource-adaptive selection of strategies within the ACT-R architecture are discussed. Resource-Adaptive Selection of Strategies According to Reder and Schunn (1998) individual perform- ance differences in learning and problem-solving tasks may not only depend on the variability of cognitive parameters (e.g. speed of processing, working-memory capacity) or on interindividual differences in the availability of strategies for solving the same task. Instead subjects may differ with regard to their ability of shifting strategies as a consequence of changes in task demands or other situational parameters. Therefore, the adaptive selection of strategies should be of major importance for success in learning and problem solv- ing. Theoretically, associative approaches explain strategy selection as a reaction to cues related to certain strategies (cf. Reder & Schunn, 1998). On the contrary, rational ap- proaches assume that subjects choose strategies according to their costs and benefits in terms of resource demands and expected utility (cf. Payne, Bettman & Johnson, 1993; Logan, 1985). In our paper we prefer a rational approach which is based on a wide conception of resources compris- ing all internal and external means that are useful or neces- sary for solving a specific task. We focus on internal re- sources like prior knowledge and external resources like learning time and external information. The costs of adopt- ing a specific strategy increase with its resource demands. Besides differences in costs, strategies may additionally differ with respect to their benefits (e.g., effectiveness in solving the task at hand, success in solving subsequent tasks, acquisition of different kinds of knowledge). To describe processes of strategy selection within a re- source-based framework two different types of resource adaptivity have to be distinguished: (a) On the one hand evolution may have forced cognitive systems to generally employ resource-adapted strategies, i.e. strategies that do not lead to optimal task performance but that are compatible with the usual limitations of processing resources. Accord- ing to this assumption resource-adapted behavior will be even displayed in situations with relatively high resource availability. A well known theoretical approach to resource- adapted behavior that may be applied to strategy selection is the concept of satisficing (Simon, 1990). According to this concept bounded rational agents do not select the most ef- fective strategy for solving a task but rather set a specific aspiration level (probably associated with the value of the respective goal) and select a strategy that exceeds it. (b) On the other hand cognitive systems may be resource-adapting in that the strategies employed to pursue a certain task are additionally constrained by the configuration of resources currently available for the agent. If resources like knowl- edge, time or external information are restricted, strategies should be adopted that are less demanding with respect to these resources. These strategic shifts may compensate for performance impairments expectable without such adapta- tions. Severe limitations of specific resources or certain combinations of resource limitations may prove impossible to compensate. Taken together it can be postulated that subjects generally choose satisficing strategies even when no strong resource limitations are present. Additionally they should adapt to specific limitations by choosing strategies that are more frugal with respect to these limited resources. Therefore, our main aim is to determine whether subjects working under certain resource limitations employ the same strategies as subjects without such limitations or whether they adapt to these limitations in a useful way. Learning from Worked-Out Examples In our empirical work we are specifically interested in strategies of learning from worked-out examples from the domain of probability word problems. Worked-out exam- ples are instances of a certain problem type together with a detailed solution. They facilitate the learning of abstract procedures for later problem solving (Cummins, 1992; Ca- trambone, 1998) and the solving of novel problems by anal- ogy (Gick & Holyoak, 1983; Reed, 1999). Prerequisites for the use of examples for knowledge acquisition and applica- tion are the generation of suitable example representations and the initiation of appropriate cognitive processes work- ing on these representations. For our purposes strategies of learning from worked-out examples can be described on two dimensions: rare versus frequent use of examples and brief versus extensive use of examples. Van Lehn and Jones (1993) found that better learners preferred a rare use of ex- amples and tried to solve training problems on their own. While good learners only inspected examples for getting specific information, poor learners referred back to exam- ples as often as possible. Beyond differences in the frequency of example use learn- ers can use examples more or less extensively depending on the degree of example elaboration during learning. These elaborations may comprise the abstract deep structure of an example problem, the subgoal structure of its solution (Ca-