The Role of Relapse Prevention and Goal Setting in Training Transfer Enhancement

This article reviews the effect of two post-training transfer interventions (relapse prevention [RP] and goal setting [GS]) on trainees’ ability to apply skills gained in a training context to the workplace. Through a review of post-training transfer interventions literature, the article identifies a number of key issues that remain unresolved or underexplored, for example, the inconsistent results on the impact of RP on transfer of training, the lack of agreement on which GS types are more efficient to improve transfer performance, the lack of clarity about the distinction between RP and GS, and the underlying process through which these two post-training transfer interventions influence transfer of training. We offer some recommendations to overcome these problems and also provide guidance for future research on transfer of training

Modelling dynamic decision making with the ACT-R cognitive architecture

This paper describes a model of dynamic decision making in the Dynamic Stocks and Flows (DSF) task, developed using the ACT-R cognitive architecture. This task is a simple simulation of a water tank in which the water level must be kept constant whilst the inflow and outflow changes at varying rates. The basic functions of the model are based around three steps. Firstly, the model predicts the water level in the next cycle by adding the current water level to the predicted net inflow of water. Secondly, based on this projection, the net outflow of the water is adjusted to bring the water level back to the target. Thirdly, the predicted net inflow of water is adjusted to improve its accuracy in the future. If the prediction has overestimated net inflow then it is reduced, if it has underestimated net inflow it is increased. The model was entered into a model comparison competition-the Dynamic Stocks and Flows Challenge-to model human performance on four conditions of the DSF task and then subject the model to testing on five unseen transfer conditions. The model reproduced the main features of the development data reasonably well but did not reproduce human performance well under the transfer conditions. This suggests that the principles underlying human performance across the different conditions differ considerably despite their apparent similarity. Further lessons for the future development of our model and model comparison challenges are considered

A game based approach to improve traders' decision-making

Purpose: The development of a game based approach to improving the decision-making capabilities of financial traders through attention to improving the regulation of emotions during trading. Design/methodology/approach: The project used a design-based research approach to integrate the contributions of a highly inter-disciplinary team. The approach was underpinned by considerable stakeholder engagement to understand the ‘ecology of practices’ in which this learning approach should be embedded. Findings: Taken together, our 35 laboratory, field and evaluation studies provide much support for the validity of our game based learning approach, the learning elements which make it up, and the value of designing game-based learning to fit within an ecology of existing practices. Originality/value: The novelty of the work described in the paper comes from the focus in this research project of combining knowledge and skills from multiple disciplines informed by a deep understanding of the context of application to achieve the successful development of a Learning Pathway, which addresses the transfer of learning to the practice environment Key words: Design-based research, emotion-regulation, disposition–effect, financial traders, serious games, sensor-based game

Virtual reality and its role in removing the barriers that turn cognitive impairments into intellectual disability

Early expectations of the contribution that virtual reality (VR) could make to education far exceeded actual applications. This was largely due to the initial immaturity of the technology and a lack of evidence base on which to base design and utilisation. While the early developments in computer based learning largely concentrated on mainstream education, leaving those with special needs behind, the potential of VR as an educational tool was exploited for those with intellectual disabilities right from the start. This paper describes the empirical evidence that has contributed to the development of educational virtual reality for those with intellectual disabilities: studies on transfer of learning from the virtual to the real world; how teachers might support those using VR; the design of virtual environments and what input/control devices best facilitate use of desktop VR. Future developments and ethical issues are also considered

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