Abstract not found

A goal of successful learning is the transfer of learned knowledge to novel situations. However, spontaneous transfer is notoriously difficult to achieve. In this research, we argue that learning and transfer can be facilitated when knowledge is expressed in an abstract, generic form. In Experiments 1 and 2, undergraduate students learned two isomorphic domains, which were based on the same algebraic group, with one domain expressed in a more abstract, generic form and the other expressed in a more concrete form. In both experiments, transfer from more abstract to more concrete was greater than the reverse. In Experiment 3, undergraduate students learned the same algebraic group under varying degrees of concreteness. Our results demonstrate that the use of perceptually rich, concrete symbols may hinder learning. This research indicates that concreteness may have substantial learning and transfer costs, whereas abstractness may have benefits. A goal of successful learning is transfer, or the ability to apply acquired knowledge outside of the learned situation. For example, if one learned how to calculate the probability of heads occurring twice on two fair coin tosses in a mathematics classroom, one should be able to

Goldstone for useful chats about learning, abstraction and surrogate situations. What do linguistic symbols do for minds like ours, and how (if at all) can basic embodied, dynamical and situated approaches do justice to high-level human thought and reason? These two questions are best addressed together, since our answers to the first may inform the second. The key move in ‘scaling-up ’ simple embodied cognitive science is, I argue, to take very seriously the potent role of human-built structures in transforming the spaces of human learning and reason. In particular, in this paper I look at a range of cases involving what I dub ‘surrogate situations’. Here, we actively create restricted artificial environments that allow us to deploy basic perception-actionreason routines in the absence of their proper objects. Examples include the use of real-world models, diagrams and other concrete external symbols to support dense looping interactions with a variety of stable external structures that stand in for the absent states of affairs. 1 Language itself, I shall finally suggest, is the most potent and fundamental form of such surrogacy. Words are both cheap stand-ins for gross behavioral outcomes, and the concrete objects that structure new spaces for basic forms of learning and reason. A good hard look at surrogate situatedness thus turns the standard skeptical challenge on its head. But it raises important questions concerning what really matters about these new approaches, and it helps focus what I see as the major challenge for the future: how, in detail, to conceptualize the role of symbols (both internal and external) in dynamical cognitive processes.

Contemporary theories of learning postulate one or at most a small number of different learning mechanisms. However, people are capable of mastering a given task through qualitatively different learning paths such as learning by instruction and learning by doing. We hypothesize that the knowledge acquired through such alternative paths differs with respect to the level of abstraction and the balance between declarative and procedural knowledge. In a laboratory experiment we investigated what was learned about patterned letter sequences via either direct instruction in the relevant patterns or practice in solving letter-sequence extrapolation problems. Results showed that both types of learning led to mastery of the target task as measured by accuracy performance. However, behavioral differences emerged in how participants applied their knowledge. Participants given instruction showed more variability in the types of strategies they used to articulate their knowledge as well as longer solution times for generating the action implications of that knowledge as compared to the participants given practice. Results are discussed regarding the implications for transfer, generalization, and procedural application. Learning theories that claim generality should be tested against cross-scenario phenomena, not just parametric variations of a single learning scenario.

The opinions and positions expressed in this practice guide are the authors ’ and do not necessarily represent the opinions and positions of the Institute of Education Sciences or the U.S. Department of Education. This practice guide should be reviewed and applied according to the specific needs of the educators and education agencies using it and with full realization that it represents only one approach that might be taken, based on the research that was available at the time of publication. This practice guide should be used as a tool to assist in decision-making rather than as a “cookbook.” Any references within the document to specific education products are illustrative and do not imply endorsement of these products to the exclusion of other products that are not referenced. U.S. Department of Education

One of the most important applications of grounded cognition theories is to science and mathematics education, where the primary goal is to foster knowledge and skills that are widely transportable to new situations. This presents a challenge to those grounded cognition theories that tightly tie knowledge to the specifics of a single situation. In this

Abstract- The field of Complex Adaptive Systems (CAS) is approximately 20 years old, having been established by physicists, economists, and others studying complexity at the Santa Fe Institute in New Mexico, USA. The field has spawned much work, such as Holland’s contributions of genetic algorithms, classifier systems, and his ecosystem simulator, which assisted in provoking the fields of evolutionary computation and artificial life. The framework of inducted principles derived from many natural and artificial examples of complex systems has assisted in the investigation in such diverse fields of study as psychology, anthropology, genetic evolution, ecology, and business management theory, although a unified theory of such complex systems still appears to be a long way off. This work reviews the principles of complex adaptive systems as a framework, providing a number of interpretations from eminent researches in the field. Many example works are cited, and the theory is used to phrase some ambiguus work in the field of artificial immune systems and artificial life. The methodology of using simulations of CAS as the starting point for models in the field of biological inspired computation is postulated as an important contribution of CAS to that field. Keywords- Complex Adaptive Systems, CSA, General Principles I.

E-Learning has been shown to compare favorably to traditional teaching methods as measured by standard achievement tests and learner satisfaction.(1,2,3,28) And though there is a paucity of such studies demonstrating that the increased knowledge gained is of greater practical use, one recent example is provided by Fordis et al. (2005) where online CME was shown to effect changes in physician practice behavior better than traditional (live) CME.(1) As well, an online review of medical and educational literature databases (Medline, ERIC, and Cinahl) revealed a paucity of investigations which objectively compare pedagogical methods in medical education using an online CBL component to traditional methods. Practical criteria for evaluating online patient simulations are only recently being proposed.(20) Sakowski, Rich, and Turner (2001) found that students using Web-based case simulations as an individual exercise did not perform differently on the clerkship written examination than those in the traditional clerkship curriculum.(22) However, this study used only a small number of subjects and cases were not truly interactive, because no user input was elicited and no feedback was given. Leong, Baldwin, & Adelman (2003) compared different methods of case delivery and concluded that computer cases are better than paper cases or simple study articles.(23) And Swagerty et al. (2001) showed pretest to post-test improvement in third year medical students enrolled in a case-oriented Web-based

Case-based reasoning is ubiquitous in real-world medical diagnosis, and yet most technology-enhanced medical training systems fail to provide new information and training in a manner consistent with the way professionals need to later access learned information. Medulator provides an ecologically valid alternative to these systems and also provides the opportunity to optimize learning through application of principles of transfer from the analogical reasoning literature. Analogical reasoning involves comparison of structured information between two cases and allows the reasoner to make inferences about one case (the target) based on prior knowledge of another case (the source). Numerous laboratory studies have suggested conditions that may facilitate transfer via analogy (see 11-13 for reviews); however, few of these methods have been evaluated in complex learning environments such as the domain of medicine. In the present study we use Medulator to evaluate two potential methods for the optimization of learning. By definition analogy involves comparison of cases based on structure (i.e., a comparison of the pattern of relations present in each case). For example, the diagnoses for two patients may be said to be analogous if they have similar patterns of symptoms and test results. However, the objects in the source and target of an analogy can also be similar at a surface level (e.g., two patients may be the same race, ages or have a similar occupation). These non-diagnostic surface characteristics can frequently be quite

The opinions and positions expressed in this practice guide are the authors ’ and do not necessarily represent the opinions and positions of the Institute of Education Sciences or the U.S. Department of Education. This practice guide should be reviewed and applied according to the specific needs of the educators and education agencies using it and with full realization that it represents only one approach that might be taken, based on the research that was available at the time of publication. This practice guide should be used as a tool to assist in decision-making rather than as a “cookbook.” Any references within the document to specific education products are illustrative and do not imply endorsement of these products to the exclusion of other products that are not referenced. U.S. Department of Education