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

Engaging Girls in Computer Science: Do Single-Gender Interdisciplinary Classes Help?

Computing-driven innovation cannot reach its full potential if only a fraction of the population is involved. Without girls and their non-stereotypical contribution, the innovation potential is severely limited. In computer science (CS) and software engineering (SE), the gender gap persists without any positive trend. Many girls find it challenging to identify with the subject of CS. However, we can capitalize on their interests and create environments for girls through interdisciplinary subcultures to spark and foster enthusiasm for CS. This paper presents and discusses the results of an intervention in which we applied a novel interdisciplinary online course in data science to get girls excited about CS and programming by contributing to the grand goal of solving colony collapse disorder from biology and geoecology. The results show the potential of such programs to get girls excited about programming, but also important implications in terms of the learning environment. The startling results show that girls from single-gender classes (SGCs) are significantly more open to CS-related topics and that the intervention evoked significantly more positive feelings in them than in girls from mixed-gender classes (MGCs). The findings highlight the importance of how CS-related topics are introduced in school and the crucial impact of the learning environment to meet the requirements of truly gender-inclusive education

Evaluating Intention to Use Remote Robotics Experimentation in Programming Courses

The Digital Agenda for Europe (2015) states that there will be 825,000 unfilled vacancies for Information and Communications Technology by 2020. This lack of IT professionals stems from the small number of students graduating in computer science. To retain more students in the field, teachers can use remote robotic experiments to explain difficult concepts. This correlational study used the unified theory of acceptance and use of technology (UTAUT) to examine if performance expectancy, effort expectancy, social influence, and facilitating conditions can predict the intention of high school computer science teachers in Cyprus, to use remote robotic experiments in their classes. Surveys, based on the UTAUT survey instrument, were collected from 90 high school computer science teachers in Cyprus, and a multiple regression analysis was used to measure the correlations between the constructs and finally the model fit of the analysis. The model was able to predict approximately 35% of the variation of the teachers\u27 intent to use remote robotic experiments. The biggest predictor was facilitating conditions followed by effort expectancy. Performance expectancy had little impact, whereas social influence had no impact on the intention of high school teachers to use remote robotic experiments in their classes. These results can help curriculum decision makers in the Ministry of Education in Cyprus to examine what factors affect the acceptance of remote robotic experiments and develop them in ways that would increase their implementation in high schools. By incorporating remote robotic experiments in high schools, students may learn difficult concepts, leading to an increase in computer science graduates and ultimately an increase in IT professionals