Common process demands of two complex dynamic control tasks : Transfer is mediated by comprehensive strategies

Although individual differences in complex problem solving (CPS) are well–established, relatively little is known about the process demands that are common to different dynamic control (CDC) tasks. A prominent example is the VOTAT strategy that describes the separate variation of input variables (“Vary One Thing At a Time”) for analyzing the causal structure of a system. To investigate such comprehensive knowledge elements and strategies, we devised the real-time driven CDC environment Dynamis2 and compared it with the widely used CPS test MicroDYN in a transfer experiment. One hundred sixty five subjects participated in the experiment, which completely combined the role of MicroDYN and Dynamis2 as source or target problem. Figural reasoning was assessed using a variant of the Raven Test. We found the expected substantial correlations among figural reasoning and performance in both CDC tasks. Moreover, MicroDYN and Dynamis2 share 15.4% unique variance controlling for figural reasoning. We found positive transfer from MicroDYN to Dynamis2, but no transfer in the opposite direction. Contrary to our expectation, transfer was not mediated by VOTAT but by an approach that is characterized by setting all input variables to zero after an intervention and waiting a certain time. This strategy (called PULSE strategy) enables the problem solver to observe the eigendynamics of the system. We conclude that for the study of complex problem solving it is important to employ a range of different CDC tasks in order to identify components of CPS. We propose that besides VOTAT and PULSE other comprehensive knowledge elements and strategies, which contribute to successful CPS, should be investigated. The positive transfer from MicroDYN to the more complex and dynamic Dynamis2 suggests an application of MicroDYN as training device

Cognitive apprenticeship : teaching the craft of reading, writing, and mathtematics

Includes bibliographical references (p. 25-27)This research was supported by the National Institute of Education under Contract no. US-NIE-C-400-81-0030 and the Office of Naval Research under Contract No. N00014-85-C-002

Cue utilization and strategy application in stable and unstable dynamic environments

http://dx.doi.org/10.1016/j.cogsys.2010.12.004 Copyright © 2011, Elsevie

Rich environments for active learning: a definition

Rich Environments for Active Learning, or REALs, are comprehensive instructional systems that evolve from and are consistent with constructivist philosophies and theories. To embody a constructivist view of learning, REALs: promote study and investigation within authentic contexts; encourage the growth of student responsibility, initiative, decision making, and intentional learning; cultivate collaboration among students and teachers; utilize dynamic, interdisciplinary, generative learning activities that promote higher-order thinking processes to help students develop rich and complex knowledge structures; and assess student progress in content and learning-to-learn within authentic contexts using realistic tasks and performances. REALs provide learning activities that engage students in a continuous collaborative process of building and reshaping understanding as a natural consequence of their experiences and interactions within learning environments that authentically reflect the world around them. In this way, REALs are a response to educational practices that promote the development of inert knowledge, such as conventional teacher-to-student knowledge-transfer activities. In this article, we describe and organize the shared elements of REALs, including the theoretical foundations and instructional strategies to provide a common ground for discussion. We compare existing assumptions underlying education with new assumptions that promote problem-solving and higher-level thinking. Next, we examine the theoretical foundation that supports these new assumptions. Finally, we describe how REALs promote these new assumptions within a constructivist framework, defining each REAL attribute and providing supporting examples of REAL strategies in action

Representational task formats and problem solving strategies in kinematics and work

Previous studies have reported that students employed different problem solving approaches when presented with the same task structured with different representations. In this study, we explored and compared students’ strategies as they attempted tasks from two topical areas, kinematics and work. Our participants were 19 engineering students taking a calculus-based physics course. The tasks were presented in linguistic, graphical, and symbolic forms and requested either a qualitative solution or a value. The analysis was both qualitative and quantitative in nature focusing principally on the characteristics of the strategies employed as well as the underlying reasoning for their applications. A comparison was also made for the same student’s approach with the same kind of representation across the two topics. Additionally, the participants’ overall strategies across the different tasks, in each topic, were considered. On the whole, we found that the students prefer manipulating equations irrespective of the representational format of the task. They rarely recognized the applicability of a ‘‘qualitative’’ approach to solve the problem although they were aware of the concepts involved. Even when the students included visual representations in their solutions, they seldom used these representations in conjunction with the mathematical part of the problem. Additionally, the students were not consistent in their approach for interpreting and solving problems with the same kind of representation across the two topical areas. The representational format, level of prior knowledge, and familiarity with a topic appeared to influence their strategies, their written responses, and their ability to recognize qualitative ways to attempt a problem. The nature of the solution does not seem to impact the strategies employed to handle the problem

Supporting Collaborative Learning in Videoconferencing using Collaboration Scripts and Content Schemes

Studies have shown that videoconferences are an effective medium for facilitating communication between parties who are separated by distance. Furthermore, studies reveal that videoconferences are effective when used for distance learning, particularly due to their ability to facilitate complex collaborative learning tasks. However, as in face-to-face communication, learners benefit when they receive additional support for such learning tasks. This article provides an overview of two empirical studies to illustrate more general insights regarding some effective and less effective ways to support collaborative learning with videoconferencing. The focus is on content schemes as content-specific support and task-specific support as collaboration scripts. Based on the results of the two studies, conclusions can be drawn about support measures that promote learning. Conclusions can also be reached about the need for employing both content schemes and collaboration scripts to provide learners with the most benefit.Studien haben gezeigt, dass Videokonferenzen ein effektives Medium für die verteilte Kommunikation sind. Ebenso zeigten erste Studien, dass sich Videokonferenzen auch in Telelernumgebungen einsetzen lassen, insbesondere weil sie komplexe kooperative Lernaufgaben ermöglichen. Lernende profitieren jedoch in solchen Lernaufgaben – ähnlich wie face to face – von zusätzlicher Unterstützung. In diesem Beitrag werden zwei empirische Studien dargestellt, die weiterführende Erkenntnisse hinsichtlich effektiver und weniger effektiver Arten der Unterstützung kollaborativen Lernens in Videokonferenzen erbringen sollen. Der Schwerpunkt liegt dabei auf Wissensschemata als Methode inhaltlicher Unterstützung, und aufgabenspezifischer Unterstützung in Form von Kooperationsskripts. Ausgehend von den Ergebnissen dieser zwei Studien werden Folgerungen über lernförderliche Merkmale der Unterstützungsmaßnahmen formuliert. Befunde weisen auf die Notwendigkeit Wissensschemata und Kooperationsskripts kombiniert anzuwenden hin, um für die Lernenden den größtmöglichen Nutzen zu erreichen

Reflections on the Practicality of Good Theory

Jennifer Kennison noticed something different about the way her high school chemistry students were working together during Complex Instruction rotation. Her attention to the change in her students’ learning caused me to think about how Elizabeth Cohen’s often referenced Kurt Lewin’s comment “There is nothing so practical as a good theory.” As a result, I decided to ask two students who were teaching CI rotations if they would be interested in working together on a conference presentation that looked at their work through the eyes of Lewin’s dictum. They would take on responsibility for documenting and writing about their CI units and I, their advisor, would take on Lewin. Both Jennifer, an experienced teacher and MEd. candidate, and Bethany Brodeur, a senior elementary education major, agreed to this task. The resulting papers formed the core of our presentation at the 2004 conference of the New England Educational Research Organization. Together, they form a short volume that integrates learning about CI with the practical implications of implementation of CI at the elementary and secondary levels. This paper reports my observations of their work confirming Lewin’s dictum and Cohen’s wisdom. C

Reusable Knowledge-based Components for Building Software Applications: A Knowledge Modelling Approach

In computer science, different types of reusable components for building software applications were proposed as a direct consequence of the emergence of new software programming paradigms. The success of these components for building applications depends on factors such as the flexibility in their combination or the facility for their selection in centralised or distributed environments such as internet. In this article, we propose a general type of reusable component, called primitive of representation, inspired by a knowledge-based approach that can promote reusability. The proposal can be understood as a generalisation of existing partial solutions that is applicable to both software and knowledge engineering for the development of hybrid applications that integrate conventional and knowledge based techniques. The article presents the structure and use of the component and describes our recent experience in the development of real-world applications based on this approach