What can student-generated diagrams tell us about their understanding of chemical equations?

Chemical equations are representations that use symbols to summarise the net changes occurring in a reaction whereas depictions such as drawings of the submicroscopic level provide representations of the chemical transformations. While the ability to balance and interpret chemical equations is key to understanding many concepts in chemistry, many undergraduate chemistry students struggle to master these skills. The equations contain a great deal of implicit information and novices may not be able to make the connection between the equation and the actual chemical transformations that are occurring. This paper reports on a study which used submicroscopic diagrams to probe students\u27 understanding of chemical equations. Assessment tasks required students to interpret diagrams, construct diagrams and to relate diagrams to symbolic representations. The analysis showed that some students have misconceptions about the molecular nature and chemical formulae and could not distinguish between coefficients and subscripts when representing chemical formulae. While students were generally able to balance a chemical equation presented as a set of diagrams, a significant number could not generate the balanced equation based on a diagram of the progress of a reaction, The study has demonstrated the use of student-generated diagrams to provide insight into students\u27 understandings of chemical equations.<br /

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

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

An Exploration of Student Reasoning about Undergraduate Computer Science Concepts: An Active Learning Technique to Address Misconceptions

Computer science (CS) is a popular but often challenging major for undergraduates. As the importance of computing in the US and world economies continues to grow, the demand for successful CS majors grows accordingly. However, retention rates are low, particularly for under-represented groups such as women and racial minorities. Computing education researchers have begun to investigate causes and explore interventions to improve the success of CS students, from K-12 through higher education. In the undergraduate CS context, for example; student difficulties with pointers, functions, loops, and control flow have been observed. We and others have utilized student responses to multiple choice questions aimed at determining misconceptions, engaged in retroactive examination of code samples and design artifacts, and conducted interviews in an attempt to understand the nature of these problems. Interventions to address these problems often apply evidenced-based active learning techniques in CS classrooms as a way to engage students and improve learning.In this work, I employ a human-centered approach, one in which the focus of data collection is on the student thought processes as evidenced in their speech and writing. I seek to determine what students are thinking not only through what can be surmised in retrospect from the artifacts they create, but also to gain insight into their thoughts as they engage in the design, implementation,and analysis of those artifacts and as they reflect on those processes and artifacts shortly after. For my dissertation work, I have conducted four studies: 1. a conceptual assessment survey asking students to “Please explain your reasoning” after each answer to code tracing/execution questions followed by task-based interviews with a smaller, different group of students 2. a “coding in the wild” think aloud study that recorded the screen and audio of students as they implemented a simple program and explained their thought process 3. interview analyses of student design diagrams/documentation in a software engineering course, tasking students to explain their designs and comparing what they believed they had designed with what is actually shown from their submitted documentation. These first three studies were formative, leading to some key insights including the benefits students can gain from feedback, students’ tendencies to avoid complexity when programming or encountering concepts they do not fully grasp, the nature of student struggles with the planning stages of problem solving, and insight into the fragile understanding of some key CS concepts that students form. I leverage the benefits of feedback with guided prompts using the misconceptions uncovered in my formative studies to conduct a final, evaluative study. This study seeks to evaluate the benefits that can be gained from a guided feedback intervention for learning introductory programming concepts and compare those benefits and the effort and resource costs associated with each variation, comparing the costs and benefits associated with two forms of feedback. The first is an active learning technique I developed and deem misconception-based feedback (MBF), which has peers working in pairs use prompts based on misconceptions to guide their discussion of a recently completed coding assignment. The second is a human autograder (HAG) group acting as a control. HAG simulates typical autograders, supplying test cases and correct solutions, but utilizes a human stand-in for a computer. In both conditions, one student uses provided prompts to guide the discussion. The other student responds/interacts with their code based on the prompts. I captured screen and audio recordings of these discussions. Participants completed conceptual pre-tests and post-tests that asked them to explain their reasoning. I hypothesized that the MBF intervention will offer avaluable way to increase learning, address misconceptions, and get students more engaged that will be feasible in CS courses of any size and have benefits over the HAG intervention. Results show that for questions involving parameter passing with regards to pass by reference versus pass by value semantics, particularly with pointers, there were significant improvements in learning outcomes for the MBF group but not the HAG group

A rapid simulation modelling process for novice software process simulation modellers

In recent years, simulation modelling of software processes have has promoted as a tool to understand, study, control, and manage software development processes. Claims have been made that simulation models are useful and effective at gaining insight into software development processes. However, little has been said about the process of developing simulation models for software engineering problems. Simulation modelling is a young discipline in software engineering. Consequently, many number software process simulation modellers are thought to be novices. The simulation modelling process is believed to have had an effect on the quality of a simulation study. Although there is a body of knowledge available in the general simulation literature to guide and educate novices, the software process simulation modelling literature lacks information for novice software process simulation modellers to understand and adopt a simulation modelling process. This thesis aims to develop a simulation modelling process for novice software process simulation modellers. This thesis reports how the development and evaluation of a simulation modelling process for novice software process simulation modellers. The rapid simulation modelling process (RSMP) is based on an empirical study of the contexts and practices of expert simulation modellers in SPSM and Operational Research (OR). The RSMP is intended to be independent of a particular simulation technique (i. e. system dynamics or discrete event simulation) and guides novice software process simulation modellers through a set of steps that should be undertaken during a simulation study; the RSMP emphasises heavy client contact and provides guidelines for model documentation. The RSMP has been evaluated through controlled experiments with novice software process simulation modellers using system dynamics (SD) modelling. In the future, it will be further evaluated with software process simulation modellers using discrete event simulation. The RSMP has also been evaluated with a panel of expert software process simulation modellers. The main contribution of this study lies in providing novice software process simulation modellers with a simulation modelling process, which embodies real world simulation practice and is intended to be independent of a particular simulation technique