Dynamic systems as tools for analysing human judgement

With the advent of computers in the experimental labs, dynamic systems have become a new tool for research on problem solving and decision making. A short review on this research is given and the main features of these systems (connectivity and dynamics) are illustrated. To allow systematic approaches to the influential variables in this area, two formal frameworks (linear structural equations and finite state automata) are presented. Besides the formal background, it is shown how the task demands of system identification and system control can be realized in these environments and how psychometrically acceptable dependent variables can be derived

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

Neo-Thomism and the Problem of Animal Suffering

Proponents of the problem of animal suffering claim that the millions of years of apparent nonhuman animal pain and suffering provides evidence against the existence of God. Neo-Cartesianism attempts to avoid this problem mainly by denying the existence of phenomenal consciousness in nonhuman animals. However, neo-Cartesian options regarding animal minds have failed to compel many. In this essay, I explore an answer to the problem of animal suffering inspired by the medieval theologian Thomas Aquinas. Instead of focusing on phenomenal consciousness, the neo-Thomistic view of animal minds focuses on self-awareness. After proposing and providing evidence for this view, I conclude that nonhuman animal suffering is not morally significant

A Cognitive Model for Problem Solving in Computer Science

According to industry representatives, computer science education needs to emphasize the processes involved in solving computing problems rather than their solutions. Most of the current assessment tools used by universities and computer science departments analyze student answers to problems rather than investigating the processes involved in solving them. Approaching assessment from this perspective would reveal potential errors leading to incorrect solutions. This dissertation proposes a model describing how people solve computational problems by storing, retrieving, and manipulating information and knowledge. It describes how metacognition interacts with schemata representing conceptual and procedural knowledge, as well as with the external sources of information that might be needed to arrive at a solution. Metacognition includes higher-order, executive processes responsible for controlling and monitoring schemata, which in turn represent the algorithmic knowledge needed for organizing and adapting concepts to a specificc domain. The model illustrates how metacognitive processes interact with the knowledge represented by schemata as well as the information from external sources. This research investigates the didifferences in the way computer science novices use their metacognition and schemata to solve a computer programming problem. After J. Parham and L. Gugerty reached an 85% reliability for six metacognitive processes and six domain-specific schemata for writing a computer program, the resulting vocabulary provided the foundation for supporting the existence of and the interaction between metacognition, schemata, and external sources of information in computer programming. Overall, the participants in this research used their schemata 6% more than their metacognition and their metacognitive processes to control and monitor their schemata used to write a computer program. This research has potential implications in computer science education and software development through its understanding of the cognitive behavior used to solve computational problems

Metacognition and lifelong e-learning: a contextual and cyclical process

Metacognition is arguably an important conceptualisation within the area of lifelong e- learning, with many theorists and practitioners claiming that it enhances the learning process. However, the lifelong, cyclical and flexible aspects of 'before', 'during' and 'after' metacognitions within lifelong e-learning (inclusive of whether an 'input' necessarily leads to a completed 'output') seem marginal within current areas of practical and theoretical debate. This article analyses Reeves's (1997) model of web-based learning in the context of the ADAPT project; a study of lifelong learners based in small and medium sized enterprises. The article focuses upon an analysis of this model's view of metacognition, and in the light of the project findings and literature review, aims to put forward an extended and expanded version of the model with reference to lifelong e-learnin

'Democracy begins in conversation’: the phenomenology of problem-based learning and legal education

Learning is complex for any number of reasons. One of these is that it doesn’t take place in a laboratory: it happens in real places, within and between real people, and as a consequence it takes place in multi-factorial environments. At every stage of learning in Higher Education (HE), from student choice of institution and programme, to the transfer of learning from theory to practice, to a single institution’s or a teacher’s evaluation of teaching and learning, there are many causal factors that affect educational process and outcome. The complexities and variables created by the interaction of such multiple factors, well known in the field of education, make learning a highly complex phenomenon to analyse and understand

Knowledge mentoring as a framework for designing computer-based agents for supporting musical composition learning

An approach to the design of teaching agents in problem-seeking domains - that is based on a systematic relationship between theoretical framework, analysis of empirical data, computational model and computational implementation - has been developed. The theoretical framework, called the Knowledge Mentoring framework (KMf), was developed to investigate how studies of dialogue and interaction can be exploited in a practical way by designers of computer-based teaching agents. A particular focus was the following musical education problem: when interacting with a computer-based music system, many students do not spontaneously reflect on their activity, they often need to be encouraged to do this. The KMf provides a taxonomy and definitions of the pedagogical goals involved in a 'mentoring' style of teaching. Mentoring is an approach to teaching that aims to support learners' creative, metacognitive and critical thinking, these being essential to musical composition and other open-ended, problem-seeking domains. This theoretical framework was used to guide the analysis and modelling of data produced by an empirical study of human teacher-learner interactions. Information on the temporal ordering of teacher-learner interactions was revealed (modelled as. state transition networks and a mentoring script). Findings from the analysis also included a pause taxonomy (that provided evidence of a link between pause length and learner ability) and the occurrence of reciprocal modelling (where participants in learning interactions built up models of the other participants' expectations). The theoretical framework and the analysis findings were then used to develop a computational model for teaching agents in problem-seeking domains. Aspects of our theory, analysis findings and computational model were incorporated into a computational implementation: a pre-prototype teaching agent called MetaMuse. A Cooperative Evaluation of MetaMuse with teacher-composers showed that it had the potential to promote creative reflection in learners

Mathematical modelling and Problem Solving in Engineering Education

BackgroundInnovation and technology have made the 21st century engineering workplace problems more diverse and challenging. Mathematical modelling is being increasingly used as the primary form of engineering design and is fundamental in everyday engineering problem solving. The demand on engineering education is to prepare graduates to meet the challenges and needs of this rapid changing society. One of the keys to do so is to develop skills to be able to integrate knowledge from multiple domains and adapt to solving novel, complex problems in workplace. This means engineering education needs to create learning environments that are effective and mindful of the authentic practices of engineers. PurposeThe goal of this thesis is to contribute to research efforts of improving engineering education, focusing on developing students’ ability to solve mathematical modelling problems. In pursuit of this goal, this thesis examines an alternative learning design in a mathematical modelling and problem-solving course for engineers and understand how the learning design contributes to students’ learning.Scope/MethodThe two empirical studies presented in this thesis employed a qualitative case study methodology. The case under investigation is a course in mathematical modelling and problem solving offered to undergraduate engineering students at Chalmers University of Technology. The first study aimed to understand how engineering students approach mathematical modelling problems early in the course and how the course impacts their learning. The second study aimed to contribute to the knowledge base of authentic learning by examining students’ perceptions of different elements of authentic learning in the course. FindingsThe results show that students had little experience of mathematical modelling and solving realistic problems that lead to experiencing challenges early in the course. Many were unaware of the importance of understanding the problem and exploring alternatives which related to their lack of self-regulation or metacognitive skills and was impeded by different types of beliefs, attitude and expectations shaped by their prior experiences. The most important impact of the course was on students’ metacognitive development. In the analysis of students’ perception of this alternative learning environment, the results showed that students experienced elements of authentic learning in the course. Even though the tasks were not entirely ‘real’, the student experience them authentic and ‘bought in’. Students engaged in deep reflective thinking in the course and presented several mechanisms of learning that linked elements of authentic learning and the course.ConclusionThe findings in this thesis demonstrates the importance of self-regulation and beliefs in developing students’ mathematical problem-solving abilities and exemplifies how the learning environment in the course contributed to developing students’ mathematical modelling and realistic problem-solving skills as well as metacognitive skills. Furthermore, the thesis presents interesting outlook on students’ perception of authenticity in the course’s learning environment contributing to the knowledge base of authentic learning in engineering education. Finally, we recommend expanding this course’s concept to other engineering programs and offer pointers to design courses that intend to provide authentic learning experience

A Higher-Order Theory of Emotional Consciousness

Emotional states of consciousness, or what are typically called emotional feelings, are traditionally viewed as being innately programed in subcortical areas of the brain, and are often treated as different from cognitive states of consciousness, such as those related to the perception of external stimuli. We argue that conscious experiences, regardless of their content, arise from one system in the brain. On this view, what differs in emotional and non-emotional states is the kind of inputs that are processed by a general cortical network of cognition, a network essential for conscious experiences. Although subcortical circuits are not directly responsible for conscious feelings, they provide non-conscious inputs that coalesce with other kinds of neural signals in the cognitive assembly of conscious emotional experiences. In building the case for this proposal, we defend a modified version of what is known as the higher-order theory of consciousness