Designing Instruction For Recovering Alcoholics: The Role Of Executive Function And Levels Of Guidance In Learning From Visually Complex Simulations

The present study examines the design of visually complex science simulations. Building upon an earlier study by Homer and Plass (2014), the current research determines under which circumstances adult learners, and alcoholics in recovery, would perform better from while learning with different levels of guidance. It was predicted that alcoholic adults in recovery would have impaired Executive Function (EF) as compared to controls selected from the general population and that EF would affect learning. An experiment investigated whether levels of EF predict learning from simulations that offered higher or lower levels of instructional guidance. Participants were 76 adults, half of which were alcoholics in recovery. They were randomly assigned to a treatment condition that taught about the Ideal Gas Laws from either a simulation that allowed them to freely explore the controls or one that used guided animation. Analyses of variance revealed that the control group scored significantly better than the experimental group in EF on tests of processing speed (Stroop S). The experimental group performed slightly better than controls on tests of interference (Stroop I) and scored better on the Stroop (I) as their length of sobriety increased, but there was no significant difference on either. Age had a significant effect on the results of the Stroop. Both groups scored worse with age on the speed tests, but better with age on the interference test. Using a stepwise linear regression analysis it was shown that the best predictor of performance on both tests of comprehension and transfer was the card rotation test (ETS S-1). There was no significant difference between groups on this measure. Results suggest that after a significant time away from a drink there is no difference in learning capabilities between recovering alcoholics and controls when level of education is controlled

AN EXAMINATION OF THE IMPACT OF COMPUTER-BASED ANIMATIONS AND VISUALIZATION SEQUENCE ON LEARNERS' UNDERSTANDING OF HADLEY CELLS IN ATMOSPHERIC CIRCULATION

Research examining animation use for student learning has been conducted in the last two decades across a multitude of instructional environments and content areas. The extensive construction and implementation of animations in learning resulted from the availability of powerful computing systems and the perceived advantages the novel medium offered to deliver dynamic representations of complex systems beyond the human perceptual scale. Animations replaced or supplemented text and static diagrams of system functioning and were predicted to significantly improve learners' conceptual understanding of target systems. However, subsequent research has not consistently discovered affordances to understanding, and in some cases, has actually shown that animation use is detrimental to system understanding especially for content area novices (Lowe 2004; Mayer et al. 2005). This study sought to determine whether animation inclusion in an authentic learning context improved student understanding for an introductory earth science concept, Hadley Cell circulation. In addition, the study sought to determine whether the timing of animation examination improved conceptual understanding. A quasi-experimental pretest posttest design administered in an undergraduate science lecture and laboratory course compared four different learning conditions: text and static diagrams with no animation use, animation use prior to the examination of text and static diagrams, animation use following the examination of text and static diagrams, and animation use during the examination of text and static diagrams. Additionally, procedural data for a sample of three students in each condition were recorded and analyzed through the lens of self regulated learning (SRL) behaviors. The aim was to determine whether qualitative differences existed between cognitive processes employed. Results indicated that animation use did not improve understanding across all conditions. However learners able to employ animations while reading and examining the static diagrams and to a lesser extent, after reading the system description, showed evidence of higher levels of system understanding on posttest assessments. Procedural data found few differences between groups with one exception---learners given access to animations during the learning episode chose to examine and coordinate the representations more frequently. These results indicated a new finding from the use of animation, a sequence effect to improve understanding of Hadley Cells in atmospheric circulation

The effectiveness of using virtual patient educational tools to improve medical students’ clinical reasoning skills: a systematic review

Background: Use of virtual patient educational tools could fll the current gap in the teaching of clinical reasoning skills. However, there is a limited understanding of their efectiveness. The aim of this study was to synthesise the evidence to understand the efectiveness of virtual patient tools aimed at improving undergraduate medical students’ clinical reasoning skills. Methods: We searched MEDLINE, EMBASE, CINAHL, ERIC, Scopus, Web of Science and PsycINFO from 1990 to January 2022, to identify all experimental articles testing the efectiveness of virtual patient educational tools on medical students’ clinical reasoning skills. Quality of the articles was assessed using an adapted form of the MERSQI and the Newcastle–Ottawa Scale. A narrative synthesis summarised intervention features, how virtual patient tools were evaluated and reported efectiveness. Results: The search revealed 8,186 articles, with 19 articles meeting the inclusion criteria. Average study quality was moderate (M=6.5, SD=2.7), with nearly half not reporting any measurement of validity or reliability for their clinical reasoning outcome measure (8/19, 42%). Eleven articles found a positive efect of virtual patient tools on reasoning (11/19, 58%). Four reported no signifcant efect and four reported mixed efects (4/19, 21%). Several domains of clinical reasoning were evaluated. Data gathering, ideas about diagnosis and patient management were more often found to improve after virtual patient use (34/47 analyses, 72%) than application of knowledge, fexibility in thinking and problem-solving (3/7 analyses, 43%). Conclusions: Using virtual patient tools could efectively complement current teaching especially if opportunities for face-to-face teaching or other methods are limited, as there was some evidence that virtual patient educational tools can improve undergraduate medical students’ clinical reasoning skills. Evaluations that measured more case specifc clinical reasoning domains, such as data gathering, showed more consistent improvement than general measures like problem-solving. Case specifc measures might be more sensitive to change given the context dependent nature of clinical reasoning. Consistent use of validated clinical reasoning measures is needed to enable a metaanalysis to estimate efectiveness

Effects and applications of video games and virtual environments

The widespread use of virtual environments in today’s society leads to the importance of researching how using these virtual environments affect us, as well as how we can best use them. Video games are a very commonly used type of virtual environment/application of virtual environments. Video game research is rife with conflicting results, from studies into training, effects on emotion, to effects on visual attention. Chapter 2 considers the impacts of playing video games on visual attention and shows that the effects depend on the type of attentional process measured, and the video game genres played. Chapter 3 looks at how studies measure video game experience, and suggests a more sophisticated measure, including video game genres and platforms. This chapter also considers to what extent different video game genres are linked to different cognitive skills. Chapter 4 covers research between video game playing, task switching, and impulsivity. Chapter 5 shows that home video game playing (i.e. on home console platforms) affects both implicit memory and explicit memory, but mobile video game playing does not. Recent technological advances allowed the development of a newer form of virtual environment, virtual reality. Virtual reality has become more popular over the last few years in manufacturing and entertainment industries. However, studies into applying virtual reality to educational settings are limited. Chapter 6 presents a study that tests the effects of virtual reality on learning. The results show increased motivation and engagement with learning materials, when xxiv compared to learning with textbook-style or video materials. Chapter 7 compares learning in virtual reality, mixed reality and traditional lecture style modalities, and finds that participants report higher levels of engagement in both Virtual Reality and Mixed Reality conditions, and higher levels of positive emotions in the Virtual Reality condition. Implications for how individuals are affected by both of these types of virtual environments is discussed, including how they can be applied to learning

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

Executive function predictors of science achievement in middle-school students

Cognitive flexibility as measured by the Wisconsin Card Sort Task (WCST) has long been associated with frontal lobe function. More recently, this construct has been associated with executive function (EF), which shares overlapping neural correlates. Here, we investigate the relationship between EF, cognitive flexibility, and science achievement in adolescents. This is important because there are fewer educational neuroscience studies of scientific reasoning than of other academically relevant forms of cognition (i.e., mathematical thinking and language understanding). Eighth grade students at a diverse middle school in the Midwestern US completed classroom-adapted measures of three EFs (shifting, inhibition, and updating) and the WCST. Science achievement was indexed by students’ standardized test scores and their end-of-the-year science class grades. Among the EF measures, updating was strongly predictive of science achievement. The association between cognitive flexibility and science achievement was comparatively weaker. These findings illuminate the relationship between EF, cognitive flexibility, and science achievement. A methodological contribution was the development of paper-and-pencil based versions of standard EF and cognitive flexibility measures suitable for classroom administration. We expect these materials to help support future classroom-based studies of EF and cognitive flexibility, and whether training these abilities in adolescent learners improves their science achievement

Thinking Systemically: a Study of Course Communication and Social Processes in Face-to-Face and Online Courses

Traditionally, research that has examined online courses compared course modes, online and face-to-face (f2f). Studies tend to examine the two modes to determine whether online courses are as effective as online courses by comparing student outcomes, such as student learning and satisfaction. Seldom has research examined how the course communication in online and f2f courses impact student outcomes. Moreover, there is little examination of the relationship between the design of the course and the relationship with social processes, in particular, communication. In this study, t-tests indicated that there were no significant differences between antecedents (technological familiarity and instructional characteristics) and outcomes variables (learning, performance, and satisfaction) between online or face-to-face courses. However, there were significant differences in course communication constructs including richness, social presence, learning community, and active learning behaviors. Multiple regression analyses indicated assessment and evaluation in instructional characteristics explained 36% of the variance in social presence, 42% of the variance in richness, and 27% of the variance in a learning community. Two components in instructional characteristics, organization and instructional design and course support, did not contribute to the model predicting these communication variables. However, they did predict 55% of the variance in engagement. Assessment and evaluation did not contribute to the model for predicting engagement. Assessment and evaluation are key factors in predicting communication variables where organization and instructional design and course support are a key factor in predicting engagement. Finally, multiple regression analyses indicated that 67% of the variance of learning can be predicted by communication variables of social presence, richness, engagement, and learning community, 52% of the variance of performance can be predicted by richness and engagement, 72% of the variance of satisfaction can be predicted by richness, engagement, and presence. Self-reported active learning behaviors did not predict learning, performance, or satisfaction