The Impact of Task- and Team-Generic Teamwork Skills Training on Team Effectiveness

This study examined the effects of training team members in three task- and teamgeneric teamwork skills: planning and task coordination, collaborative problem solving, and communication. We first examined the degree to which task- and team-generic teamwork skills training impacted team performance on a task unrelated to the content of the training program.We then examined whether the effects of task- and team-generic teamwork skills training on team performance were due to the transfer of skills directly related to planning and task coordination, collaborative problem solving, and communication. Results from 65 four-person project teams indicated that task- and team-generic teamwork skills training led to significantly higher levels of team performance. Results also indicated that the effects of task- and teamgeneric teamwork skills training on team performance were mediated by planning and task coordination and collaborative problem solving behavior. Although communication was positively affected by the task- and team-generic teamwork skills training, it did not mediate the relationship between task- and team-generic teamwork skills training and team performance.Theoretical and practical implications of these results are discussed, as well as possible limitations and directions for future research

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

Learning with worked-out problems in Manufacturing Technology: The effects of instructional explanations and self-explanation prompts on acquired knowledge acquisition, near and far transfer performance

In the present research, two different explanatory approaches – namely, instructional explanation and self-explanation prompts – were applied in worked-out-problem-based learning (learning with worked-out problems) in a computer-assisted instructional environment in the domain of manufacturing technology. This research aims at comparing the effects of both explanatory approaches on topic knowledge acquisition, near transfer performance, and far transfer performance. Additionally, this research also attempts to examine the impact of topic interest on the aforementioned variables, in addition to the relationships between topic interest, mental effort, and learning outcomes. A total of 76 second-year students were randomly assigned to experimental and control groups. The pre- and post-tests were used to measure topic knowledge acquisition, near-transfer performance, and far-transfer performance, whereas topic interest and mental effort were measured by means of Topic Interest Questionnaire and NASA Task Load Index (NASA-TLX) respectively. The analysis outcomes revealed that the self-explanation prompts approach was significantly superior to the instructional-explanation approach in terms of topic knowledge acquisition and near transfer performance. In addition, the results demonstrated that the impact of topic interest was significantly noticeable on far transfer tasks, but not on topic knowledge acquisition and near transfer tasks. On the other hand, the relationship between mental effort investment and test performance was not statistically significant. Finally, an equivocal relationship, which varied depending on the treatment conditions, was discovered between topic interest, mental effort, and test performance. (DIPF/orig.)In der vorliegenden Untersuchung wurden zwei unterschiedliche Lehrmethoden – instruktionale Erklärung und Aufforderung zur Selbsterklärung – angewandt auf das Lernen mit Lösungsbeispielen in einer computergestützten Lernumgebung, die thematisch im Bereich der Fertigungstechnik angesiedelt ist. Die computergestützte Lernumgebung bestand aus einer vom Autor erstellten Lernsoftware, die mit Macromedia Authorware entworfen und entwickelt wurde. Hauptziel der Studie war ein Vergleich der Effekte beider Lehrmethoden auf die Aneignung von Sachwissen sowie die Leistung beim nahen und weiten Transfer. Außerdem wurden die Auswirkungen von Gegenstandsinteresse auf die zuvor genannten Kriterien untersucht und die Beziehungen zwischen Gegenstandsinteresse, mentaler Anstrengung und Lernergebnissen. Insgesamt wurden 76 Studierende im zweiten Jahr ihres Studiums an der Fakultät für Technische Bildung, Universität Tun Hussein Onn Malaysia (UTHM), nach dem Zufallsprinzip in drei Gruppen aufgeteilt: Selbsterklärungsaufforderung (SE: n = 25), instruktionale Erklärung (IE: n = 25) und Kontrollgruppe (n = 26). Mit Pre- und Post-Tests wurden die Aneignung von Sachwissen sowie die nahe und weite Transferleistung erhoben. Gegenstandsinteresse und mentale Anstrengung wurden mit dem Topic Interest–Fragebogen und dem NASA-TLX gemessen. Das Statistik-Paket für die Sozialwissenschaften (SPSS) wurde verwendet, um die Hypothesen an den gesammelten Daten zu prüfen. Die Hypothesenprüfung erfolgte mittels quantitativ statistischer Auswertungsverfahren (Korrelation, Varianzanalyse). (DIPF/Orig.

Experience, expertise and expert-performance research in public accounting

Bibliography: p. [22-25]

Engineering Agent Systems for Decision Support

This paper discusses how agent technology can be applied to the design of advanced Information Systems for Decision Support. In particular, it describes the different steps and models that are necessary to engineer Decision Support Systems based on a multiagent architecture. The approach is illustrated by a case study in the traffic management domain

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

Modelling the Developing Mind: From Structure to Change

This paper presents a theory of cognitive change. The theory assumes that the fundamental causes of cognitive change reside in the architecture of mind. Thus, the architecture of mind as specified by the theory is described first. It is assumed that the mind is a three-level universe involving (1) a processing system that constrains processing potentials, (2) a set of specialized capacity systems that guide understanding of different reality and knowledge domains, and (3) a hypecognitive system that monitors and controls the functioning of all other systems. The paper then specifies the types of change that may occur in cognitive development (changes within the levels of mind, changes in the relations between structures across levels, changes in the efficiency of a structure) and a series of general (e.g., metarepresentation) and more specific mechanisms (e.g., bridging, interweaving, and fusion) that bring the changes about. It is argued that different types of change require different mechanisms. Finally, a general model of the nature of cognitive development is offered. The relations between the theory proposed in the paper and other theories and research in cognitive development and cognitive neuroscience is discussed throughout the paper

Effects of word processing on text revision

Revising is an evaluating and editing process that is an essential part of text production. Is text revising facilitated by the use of word processors? After examining the related research, it is difficult to conclude with certainty that the use of word processors is always effective in improving writers' revising skills, or that their use necessarily leads to the production of higher quality texts. Their effectiveness depends on a large number of parameters (computer equipment, writing skills, task execution conditions) which psychologists are now starting to measure