Using Data in Undergraduate Science Classrooms

Provides pedagogical insight concerning the skill of using data The resource being annotated is: http://www.dlese.org/dds/catalog_DATA-CLASS-000-000-000-007.htm

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

Software tools for the cognitive development of autonomous robots

Robotic systems are evolving towards higher degrees of autonomy. This paper reviews the cognitive tools available nowadays for the fulfilment of abstract or long-term goals as well as for learning and modifying their behaviour.Peer ReviewedPostprint (author's final draft

Applying science of learning in education: Infusing psychological science into the curriculum

The field of specialization known as the science of learning is not, in fact, one field. Science of learning is a term that serves as an umbrella for many lines of research, theory, and application. A term with an even wider reach is Learning Sciences (Sawyer, 2006). The present book represents a sliver, albeit a substantial one, of the scholarship on the science of learning and its application in educational settings (Science of Instruction, Mayer 2011). Although much, but not all, of what is presented in this book is focused on learning in college and university settings, teachers of all academic levels may find the recommendations made by chapter authors of service. The overarching theme of this book is on the interplay between the science of learning, the science of instruction, and the science of assessment (Mayer, 2011). The science of learning is a systematic and empirical approach to understanding how people learn. More formally, Mayer (2011) defined the science of learning as the “scientific study of how people learn” (p. 3). The science of instruction (Mayer 2011), informed in part by the science of learning, is also on display throughout the book. Mayer defined the science of instruction as the “scientific study of how to help people learn” (p. 3). Finally, the assessment of student learning (e.g., learning, remembering, transferring knowledge) during and after instruction helps us determine the effectiveness of our instructional methods. Mayer defined the science of assessment as the “scientific study of how to determine what people know” (p.3). Most of the research and applications presented in this book are completed within a science of learning framework. Researchers first conducted research to understand how people learn in certain controlled contexts (i.e., in the laboratory) and then they, or others, began to consider how these understandings could be applied in educational settings. Work on the cognitive load theory of learning, which is discussed in depth in several chapters of this book (e.g., Chew; Lee and Kalyuga; Mayer; Renkl), provides an excellent example that documents how science of learning has led to valuable work on the science of instruction. Most of the work described in this book is based on theory and research in cognitive psychology. We might have selected other topics (and, thus, other authors) that have their research base in behavior analysis, computational modeling and computer science, neuroscience, etc. We made the selections we did because the work of our authors ties together nicely and seemed to us to have direct applicability in academic settings

Active learning of manipulation sequences

We describe a system allowing a robot to learn goal-directed manipulation sequences such as steps of an assembly task. Learning is based on a free mix of exploration and instruction by an external teacher, and may be active in the sense that the system tests actions to maximize learning progress and asks the teacher if needed. The main component is a symbolic planning engine that operates on learned rules, defined by actions and their pre- and postconditions. Learned by model-based reinforcement learning, rules are immediately available for planning. Thus, there are no distinct learning and application phases. We show how dynamic plans, replanned after every action if necessary, can be used for automatic execution of manipulation sequences, for monitoring of observed manipulation sequences, or a mix of the two, all while extending and refining the rule base on the fly. Quantitative results indicate fast convergence using few training examples, and highly effective teacher intervention at early stages of learning.Peer ReviewedPostprint (author’s final draft

Prototype system for supporting the incremental modelling of vague geometric configurations

In this paper the need for Intelligent Computer Aided Design (Int.CAD) to jointly support design and learning assistance is introduced. The paper focuses on presenting and exploring the possibility of realizing learning assistance in Int.CAD by introducing a new concept called Shared Learning. Shared Learning is proposed to empower CAD tools with more useful learning capabilities than that currently available and thereby provide a stronger interaction of learning between a designer and a computer. Controlled computational learning is proposed as a means whereby the Shared Learning concept can be realized. The viability of this new concept is explored by using a system called PERSPECT. PERSPECT is a preliminary numerical design tool aimed at supporting the effective utilization of numerical experiential knowledge in design. After a detailed discussion of PERSPECT's numerical design support, the paper presents the results of an evaluation that focuses on PERSPECT's implementation of controlled computational learning and ability to support a designer's need to learn. The paper then discusses PERSPECT's potential as a tool for supporting the Shared Learning concept by explaining how a designer and PERSPECT can jointly learn. There is still much work to be done before the full potential of Shared Learning can be realized. However, the authors do believe that the concept of Shared Learning may hold the key to truly empowering learning in Int.CAD

The pragmatic nature of creativity : exploring the structuring process of expert choreographers

To date, empirical studies in choreography mainly focused on the movement generation and manipulation stages. Often, the structuring phase, which entails selection, ordering, and refinement of movement materials, remained untouched. Thus, this empirical study examines the cognitive processes that are associated with structuring and explores how expert choreographers navigate their way through endless possibilities prior to choosing a final dance design. Building from the assumption that choreographers operate in a manner similar to designers, a model of design heuristics (Yilmaz et al. 2011) was used to frame questions and investigate the transformation of dance structures over time. Generally, changes in the detail level were labeled as ‘local strategies’ and changes to the overall structure were categorized as ‘transitional’. These two groups were divided further based on the type of strategies used by expert choreographers during their creative process. The classification system that emerged enabled the identification of patterns of strategy use, leading toward a better understanding of the structuring process. Sue Healey and Gideon Obarzanek, the choreographers participating in this study, worked with a group of five dancers and had five days to generate three different dance compositions under several restrictions. They were given the same theme and soundtrack and could only work with movement material that was generated in day one. Moreover, they were asked to incorporate a design strategy during the creation of their second piece, so that its effect on the structuring process could be evaluated. Observations and interviews enabled the extraction of quantitative and qualitative information indicating how many strategies were used per piece, which were used more often, what influenced decision making, which combinations and outcomes were mostly favored by the choreographers, and what the effect was of using an explicit strategy on the structuring process. I found that choreographers transform their composition through the application of multiple strategies, particularly local ones. Still, each choreographer relies on a particular set of strategies based on the problems they identify in each process and their own personal preferences. Notably, while the use of explicit strategies led to ambiguous results, real innovation was enabled through the application of ‘Process Strategies’. These techniques guided the choreographers’ overall approach through the solution space by forcing changes in a particular direction. As a result, new dance forms were discovered. Rehearsal time seemed to be a major influence on the structuring process. The more time that was spent on creating a piece, the more alterations were applied. However, having more creation time did not always guarantee a better outcome. In fact, finding the right framework and constraints appeared to be more valuable to the creative process