Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Impact of California's Transitional Kindergarten Program, 2013-14

Transitional kindergarten (TK)—the first year of a two-year kindergarten program for California children who turn 5 between September 2 and December 2—is intended to better prepare young five-year-olds for kindergarten and ensure a strong start to their educational career. To determine whether this goal is being achieved, American Institutes for Research (AIR) is conducting an evaluation of the impact of TK in California. The goal of this study is to measure the success of the program by determining the impact of TK on students' readiness for kindergarten in several areas. Using a rigorous regression discontinuity (RD) research design,1 we compared language, literacy, mathematics, executive function, and social-emotional skills at kindergarten entry for students who attended TK and for students who did not attend TK. Overall, we found that TK had a positive impact on students' kindergarten readiness in several domains, controlling for students' age differences. These effects are over and above the experiences children in the comparison group had the year before kindergarten, which for more than 80 percent was some type of preschool program

Reinventing College Physics for Biologists: Explicating an epistemological curriculum

The University of Maryland Physics Education Research Group (UMd-PERG) carried out a five-year research project to rethink, observe, and reform introductory algebra-based (college) physics. This class is one of the Maryland Physics Department's large service courses, serving primarily life-science majors. After consultation with biologists, we re-focused the class on helping the students learn to think scientifically -- to build coherence, think in terms of mechanism, and to follow the implications of assumptions. We designed the course to tap into students' productive conceptual and epistemological resources, based on a theoretical framework from research on learning. The reformed class retains its traditional structure in terms of time and instructional personnel, but we modified existing best-practices curricular materials, including Peer Instruction, Interactive Lecture Demonstrations, and Tutorials. We provided class-controlled spaces for student collaboration, which allowed us to observe and record students learning directly. We also scanned all written homework and examinations, and we administered pre-post conceptual and epistemological surveys. The reformed class enhanced the strong gains on pre-post conceptual tests produced by the best-practices materials while obtaining unprecedented pre-post gains on epistemological surveys instead of the traditional losses.Comment: 35 pages including a 15 page appendix of supplementary material

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

Automatic imitation of biomechanically possible and impossible actions: effects of priming movements versus goals

Recent behavioral, neuroimaging, and neurophysiological research suggests a common representational code mediating the observation and execution of actions; yet, the nature of this representational code is not well understood. The authors address this question by investigating (a) whether this observation execution matching system (or mirror system) codes both the constituent movements of an action as well as its goal and (b) how such sensitivity is influenced by top-down effects of instructions. The authors tested the automatic imitation of observed finger actions while manipulating whether the movements were biomechanically possible or impossible, but holding the goal constant. When no mention was made of this difference (Experiment 1), comparable automatic imitation was elicited from possible and impossible actions, suggesting that the actions had been coded at the level of the goal. When attention was drawn to this difference (Experiment 2), however, only possible movements elicited automatic imitation. This sensitivity was specific to imitation, not affecting spatial stimulus–response compatibility (Experiment 3). These results suggest that automatic imitation is modulated by top-down influences, coding actions in terms of both movements and goals depending on the focus of attention

ELABORATIVE AND CRITICAL DIALOG: TWO POTENTIALLY EFFECTIVE PROBLEM-SOLVING AND LEARNING INTERACTIONS

Recent research on learning individual monologs and collaborative problem solving suggests that students learn best when they are required to be active participants in interactive dialogs. However, some interactive dialogs are more conducive to learning than others. Two dialog patterns that seem to be effective in producing successful problem solving and deep learning are elaborative and critical interactions. The goal of the present study is to evaluate the relative impact of each dialog on learning and problem solving by experimentally manipulating the types of conversations in which dyads engage.Undergraduate participants were randomly assigned to one of four conditions: a singleton control, a dyadic control, an elaborative dyad, or a critical dyad. The domain chosen for the experiment was a bridge optimization task in which individuals or dyads modified a simulated bridge, with the goal of making it as inexpensive as possible.Both problem solving and learning from the simulation were assessed. Performance on the task included a combination of two factors: the quality of the design and the price. Overall learning was measured by the gain from pre- to posttest on isomorphic evaluations, and was further decomposed into text-explicit and inferential knowledge. The results suggest elaboration is easier to train and led to stronger problem solving and learning than the control condition, whereas the critical interactions were more difficult to instruct and led to problem solving and learning equal to the control condition