Goal Driven Learning

Brings together a diversity of research on goal-driven learning to establish a broad, interdisciplinary framework that describes the goal-driven learning process. In cognitive science, artificial intelligence, psychology, and education, a growing body of research supports the view that the learning process is strongly influenced by the learner's goals. The fundamental tenet of goal-driven learning is that learning is largely an active and strategic process in which the learner, human or machine, attempts to identify and satisfy its information needs in the context of its tasks and goals, its prior knowledge, its capabilities, and environmental opportunities for learning. This book brings together a diversity of research on goal-driven learning to establish a broad, interdisciplinary framework that describes the goal-driven learning process. It collects and solidifies existing results on this important issue in machine and human learning and presents a theoretical framework for future investigations. The book opens with an an overview of goal-driven learning research and computational and cognitive models of the goal-driven learning process. This introduction is followed by a collection of fourteen recent research articles addressing fundamental issues of the field, including psychological and functional arguments for modeling learning as a deliberative, planful process; experimental evaluation of the benefits of utility-based analysis to guide decisions about what to learn; case studies of computational models in which learning is driven by reasoning about learning goals; psychological evidence for human goal-driven learning; and the ramifications of goal-driven learning in educational contexts. The second part of the book presents six position papers reflecting ongoing research and current issues in goal-driven learning. Issues discussed include methods for pursuing psychological studies of goal-driven learning, frameworks for the design of active and multistrategy learning systems, and methods for selecting and balancing the goals that drive learning. A Bradford Book

Knowledge-based approaches to planning and control offer benefits over classical techniques in applications that involve large yet structured state spaces. However, knowledge bases are time consuming and costly to construct. In this dissertation I introduce a framework for analytical learning that enables the agent to acquire generalizable, domain-specific procedural knowledge in the form of goal-indexed hierarchical task networks by observing a small number of successful demonstrations of goal-driven tasks. I discuss how, in contrast with most algorithms for learning by observation, my approach can learn from unannotated input demonstrations by automatically inferring the purpose of each solution step using the background knowledge about the domain. I discuss the role of hierarchical structure, distributed applicability conditions, and goals in the generalizability of the acquired knowledge. I also introduce an approach for adaptively determining the structure of the acquired knowledge that strikes a balance between generality and operationality, and for making the algorithm robust to changes in the structure of background knowledge. This involves resolving interdependencies among goals using temporal information. I present experimental studies on a number of domains which demonstrate that the quality of acquired knowledge is comparable to handcrafted content in terms of both coverage and complexity. In closing, I review related work and directions for future research.

This book is more than a collection of activities or ready-made lesson plans to add to a teaching repertoire. Instead, Goal-Driven Lesson Planning is intended to empower teachers and help them create a principled framework for their teaching—a framework that will shape the varied activities of the ESL classroom into a coherent teaching and learning partnership. After reading this book, teachers and prospective teachers will be able to articulate their individual teaching philosophies. Goal-Driven Lesson Planning shows readers how to take any piece from English language materials—an assigned text, a random newspaper article, an ESL activity from a website, etc.—and use it to teach students something about language. Readers are walked through the process of reflecting on their role in diagnosing what that “something” is—what students really need—and planning how to get them there and how to know when they got there in a goal-driven principled manner. This book has chapters on the theory of setting specific language goals for students; how to analyze learner needs (including an initial diagnostic and needs-analysis); templates to use when planning goal-driven English language lessons; explicit instruction on giving corrective feedback; how to recognize and assess student progress; and the mechanics and logistics that facilitate the goal-driven language classroom.

Using Goals to Amplify Student Learning Step Into Student Goal Setting provides an action plan for answering the question: What does this student know and how do I build from it? Research-driven and practical, this guide shows teachers how to integrate formative assessment, student metacognition, and motivational strategies to make goal setting an integral instructional strategy. Author Chase Nordengren weaves research and case studies with practical strategies to demonstrate how goal setting, with clear learning intentions and plenty of scaffolded support by teachers, can lead to high learning growth and student agency. Readers will find: Actionable strategies for incorporating goal setting in instructional practice Tips for using goals as motivational strategies to drive learning growth Guidance on how to coach students through setting their own goals – recalibrating and celebrating along the way Vignettes and examples to demonstrate what goal setting looks like in the classroom By demonstrating how to set, monitor, and evaluate goals, this guide equips teachers with the tools they need to help students take ownership of their learning journeys.

This book constitutes the refereed proceedings of the 12th International Conference on Web-Based Learning, ICWL 2013, held in Kenting, Taiwan, in October 2013. The 34 revised full papers presented were carefully reviewed and selected from about 117 submissions. The papers are organized in topical sections on interactive learning environments, design, model and framework of e-learning systems, personalized and adaptive learning, Web 2.0 and social learning environments, intelligent tools for visual learning, semantic Web and ontologies for e-learning, and Web-based learning for languages learning.

Teachers try to help their students learn. But why do they make the particular teaching choices they do? What resources do they draw upon? What accounts for the success or failure of their efforts? In How We Think, esteemed scholar and mathematician, Alan H. Schoenfeld, proposes a groundbreaking theory and model for how we think and act in the classroom and beyond. Based on thirty years of research on problem solving and teaching, Schoenfeld provides compelling evidence for a concrete approach that describes how teachers, and individuals more generally, navigate their way through in-the-moment decision-making in well-practiced domains. Applying his theoretical model to detailed representations and analyses of teachers at work as well as of professionals outside education, Schoenfeld argues that understanding and recognizing the goal-oriented patterns of our day to day decisions can help identify what makes effective or ineffective behavior in the classroom and beyond.

Goal-Directed Decision Making: Computations and Neural Circuits examines the role of goal-directed choice. It begins with an examination of the computations performed by associated circuits, but then moves on to in-depth examinations on how goal-directed learning interacts with other forms of choice and response selection. This is the only book that embraces the multidisciplinary nature of this area of decision-making, integrating our knowledge of goal-directed decision-making from basic, computational, clinical, and ethology research into a single resource that is invaluable for neuroscientists, psychologists and computer scientists alike. The book presents discussions on the broader field of decision-making and how it has expanded to incorporate ideas related to flexible behaviors, such as cognitive control, economic choice, and Bayesian inference, as well as the influences that motivation, context and cues have on behavior and decision-making. Details the neural circuits functionally involved in goal-directed decision-making and the computations these circuits perform Discusses changes in goal-directed decision-making spurred by development and disorders, and within real-world applications, including social contexts and addiction Synthesizes neuroscience, psychology and computer science research to offer a unique perspective on the central and emerging issues in goal-directed decision-making