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The present study examined how self-regulatory processes influenced problem solving behavior under conditions in which people were either re-exposed to their prior learning, or experienced someone else's. In a series of four experiments, participants solved two complex control tasks that were identical in structure but varied in presentation format. Participants either learnt to solve the second task, based on their original learning phase from the first task, or learnt to solve the second task based on another participant's learning phase. Experiment 1 showed that, under conditions in which participants' learning phase was experienced twice, performance deteriorated in the second task compared to the first. In contrast, when the learning phases in the first and second task differed, performance improved in the second task.Experiment 2 introduced instructional manipulations that induced the same response patterns as Experiment 1, and Experiment 3 showed that participants differentially weighted the learning phase in the second task. In Experiment 4, judgments of selfefficacy were shown to track control performance. The implications of these findings for induction, self-regulation, and insight are discussed. examples share a precise goal that involves accurately and reliably controlling a complex dynamic control task (CDCT-i.e., the aircraft). Control behavior in this, and other types of CDCTs (e.g., air-traffic control, naval mine sweeping, car driving) depends on the development of expertise (e.g., Brehmer, 1992;Funke, 2001; Gonzalez, Lerch, & Lebiere, 2003;Kerstholt, 1996;Randel & Pugh, 1996;Sweller, 1988;.Expertise involves both monitoring-through self-evaluation of goal-directed actions (e.g., Bandura & Locke, 2003; Ericsson & Lehman, 1996;Karoly, 1993;Lerch & Harter, 2001;Rossano, 2003;VanLehn, 1996)-and control, or the generation and selection of goal-directed actions (e.g., Lerch & Harter, 2001; Locke & Latham, 2002;Rossano, 2003;Sweller, 1988;VanLehn, 1996;Vollmeyer, Burns, & Holyoak, 1996). Both behaviors are also interrelated (e.g., Bandura & Locke, 2003; Ericsson & Lehman, 1996;Karoly, 1993;Lerch & Harter, 2001;Rossano, 2003;VanLehn, 1996). However, studies of skill learning in CDCTs have overlooked this relationship, particularly how it influences the transferability of expertise (e.g., Berry, 1991;Berry & Broadbent, 1988;Burns & Vollmeyer, 2002). Without an Anti-Learning 5 understanding of how individuals monitor their behavior, little can be said about how evaluative processes are employed when transferring learnt skills to achieve unpracticed goals.For instance, the critical difference between the examples above is that, in the first case, training (i.e., control performance) and assessment (i.e., monitoring) are based on self-generated behavior; whereas, in the second, assessment is based on comparing self-and other-generated training behavior. These types of training procedures are often used in educational (Pintrich & De Groot, 1990;Pressley, & Ghatala, 1990;Zimmerman, 1990), clin...
The present study examined how self-regulatory processes influenced problem solving behavior under conditions in which people were either re-exposed to their prior learning, or experienced someone else's. In a series of four experiments, participants solved two complex control tasks that were identical in structure but varied in presentation format. Participants either learnt to solve the second task, based on their original learning phase from the first task, or learnt to solve the second task based on another participant's learning phase. Experiment 1 showed that, under conditions in which participants' learning phase was experienced twice, performance deteriorated in the second task compared to the first. In contrast, when the learning phases in the first and second task differed, performance improved in the second task.Experiment 2 introduced instructional manipulations that induced the same response patterns as Experiment 1, and Experiment 3 showed that participants differentially weighted the learning phase in the second task. In Experiment 4, judgments of selfefficacy were shown to track control performance. The implications of these findings for induction, self-regulation, and insight are discussed. examples share a precise goal that involves accurately and reliably controlling a complex dynamic control task (CDCT-i.e., the aircraft). Control behavior in this, and other types of CDCTs (e.g., air-traffic control, naval mine sweeping, car driving) depends on the development of expertise (e.g., Brehmer, 1992;Funke, 2001; Gonzalez, Lerch, & Lebiere, 2003;Kerstholt, 1996;Randel & Pugh, 1996;Sweller, 1988;.Expertise involves both monitoring-through self-evaluation of goal-directed actions (e.g., Bandura & Locke, 2003; Ericsson & Lehman, 1996;Karoly, 1993;Lerch & Harter, 2001;Rossano, 2003;VanLehn, 1996)-and control, or the generation and selection of goal-directed actions (e.g., Lerch & Harter, 2001; Locke & Latham, 2002;Rossano, 2003;Sweller, 1988;VanLehn, 1996;Vollmeyer, Burns, & Holyoak, 1996). Both behaviors are also interrelated (e.g., Bandura & Locke, 2003; Ericsson & Lehman, 1996;Karoly, 1993;Lerch & Harter, 2001;Rossano, 2003;VanLehn, 1996). However, studies of skill learning in CDCTs have overlooked this relationship, particularly how it influences the transferability of expertise (e.g., Berry, 1991;Berry & Broadbent, 1988;Burns & Vollmeyer, 2002). Without an Anti-Learning 5 understanding of how individuals monitor their behavior, little can be said about how evaluative processes are employed when transferring learnt skills to achieve unpracticed goals.For instance, the critical difference between the examples above is that, in the first case, training (i.e., control performance) and assessment (i.e., monitoring) are based on self-generated behavior; whereas, in the second, assessment is based on comparing self-and other-generated training behavior. These types of training procedures are often used in educational (Pintrich & De Groot, 1990;Pressley, & Ghatala, 1990;Zimmerman, 1990), clin...
There is a growing body of evidence suggesting that many traditional instructional techniques may unnecessarily overload limited working memory and impede learning. Based on cognitive load theory, it was hypothesised that instructional design only takes on a crucial role when there is a high level of interaction between learning elements resulting in those elements having to be simultaneously held in working memory. When there is little, if any, interaction between individual learning elements, then the format of presentation should be inconsequential. These hypotheses were tested using a computer‐aided design/computer‐aided manufacture (CAD/CAM) package with trainees from a Sydney company. Results showed that when instructions involved high element interactivity, a self‐contained manual that physically integrated disparate information and did not require the use of the computer hardware was vastly superior to instructional formats that involved continual interaction with the computer. No differences were found between instructional formats when the learning material entailed low element interactivity. Evidence that these findings were due to cognitive load rather than other factors came from secondary task analysis. In light of these and previous results, suggestions are made for cognitively guided instructional packages.
The objective of this article is to review the attributes that influence distributed collaboration groups engaged in engineering tasks in order to guide the research into the development of new tools and methods to support engineering groups. Three comprehensive areas of literature that describe the cognitive, social, and environmental factors influencing collaboration are covered including task characteristics, collaborative technology, and group/individual development. A model is proposed that can contribute to the understanding of the engineering collaboration process, distributed group interaction, and the role of the task within this environment. The relationships between the attributes included in the model are hypothesized to affect the distributed collaboration process. This article discusses the attributes and suggests that further research is required to validate the model's relationships. © 2000 John Wiley & Sons, Inc.
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