New developments in the science of learning science of learning overview mind and brain how experts differ from novices how children learn learning and transfer the learning environment curriculum, instruction and commnity effective teaching - examples in history, mathematics and science teacher learning technology to support learning conclusions from new developments in the science of learning.

How people learn: Brain, mind, experience, and school.

Research in the area of educational technology has often been critiqued for a lack of theoretical grounding. In this article we propose a conceptual framework for educational technology by building on Shulman’s formulation of ‘‘pedagogical content knowledge’’ and extend it to the phenomenon of teachers integrating technology into their pedagogy. This framework is the result of 5 years of work on a program of research focused on teacher professional development and faculty development in higher education. It attempts to capture some of the essential qualities of teacher knowledge required for technology integration in teaching, while addressing the complex, multifaceted, and situated nature of this knowledge. We argue, briefly, that thoughtful pedagogical uses of technology require the development of a complex, situated form of knowledge that we call Technological Pedagogical Content Knowledge (TPCK). In doing so, we posit the complex roles of, and interplay among, three main components of learning environments: content, pedagogy, and technology. We argue that this model has much to offer to discussions of technology integration at multiple levels: theoretical, pedagogical, and methodological. In this article, we describe the theory behind our framework, provide examples of our teaching approach based upon the framework, and illustrate the methodological contributions that have resulted from this work.

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Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge

Running head: WHY MINIMALLY GUIDED INSTRUCTION DOES NOT WORK Why Minimal Guidance during Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching

Evidence for the superiority of guided instruction is explained in the context of our knowledge of human cognitive architecture, expert–novice differences, and cognitive load. Although unguided or minimally guided instructional approaches are very popular and intuitively appealing, the point is made that these approaches ignore both the structures that constitute human cognitive architecture and evidence from empirical studies over the past half-century that consistently indicate that minimally guided instruction is less effective and less efficient than instructional approaches that place a strong emphasis on guidance of the student learning process. The advantage of guidance begins to recede only when learners have sufficiently high prior knowledge to provide "internal" guidance. Recent developments in instructional research and instructional design models that support guidance during instruction are briefly described.

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Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching

1 On behalf of NASA’s Mars Exploration Program, this lesson was prepared by Arizona State University’s Mars Education Program, under contract to NASA’s Jet Propulsion Laboratory, a division of the California Institute of Technology. These materials may be distributed freely for non-commercial purposes. Copyright 2014; 2012; 2010; 2000. Last edited: April 24, 2014 Marsbound! Mission to the Red Planet

A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas

In this article, the authors first indicate the range of purposes and the variety of settings in which design experiments have been conducted and then delineate five crosscutting features that collectively differentiate design experiments from other methodologies. Design experiments have both a pragmatic bent—“engineering” particular forms of learning—and a theoretical orientation—developing domain-specific theories by systematically studying those forms of learning and the means of supporting them. The authors clarify what is involved in preparing for and carrying out a design experiment, and in conducting a retrospective analysis of the extensive, longitudinal data sets generated during an experiment. Logistical issues, issues of measure, the importance of working through the data systematically, and the need to be explicit about the criteria for making inferences are discussed.

Design Experiments in Educational Research

Beyond Modularity: A Developmental Perspective on Cognitive Science.

D. Kuhn, Introduction. The Development of Scientific Thinking. Related Work. D. Kuhn and E. Amsel with the assistance of L. Schauble, The Evaluation of Evidence. The Interpretation of Covariation and Noncovariation Evidence. The Influence of Theory on Evaluation of Evidence. The Reconstruction of Theory and Evidence. D. Kuhn and M. O'Loughlin with the assistance of W. Yotive, The Coordination of Theory and Evidence. Replication: The Evaluation of Evidence. The Interpretation of Insufficient and Mixed Evidence. The Coordination of Evidence with Multiple Theories. The Generation of Evidence to Evaluate Theories. The Development of Skills in Coordinating Theory and Evidence. D. Kuhn and B. Leadbeater, The Connection of Theory and Evidence. The Interpretation of Divergent Evidence. D. Kuhn, Conclusion. Summary and Conclusions. References. Index.

The development of scientific thinking skills

In this study of the development of scientific reasoning, 10 5th-6th-grade children (5 boys and 5 girls) and 10 noncollege adults conducted experiments over 6 half-hour sessions to explore the causal structure of 2 physical science domains. Feedback in these systems, though relevant to discriminating among hypotheses, was noisy as a result of varying effect sizes and measurement error. After 2 hr on each task, both age groups demonstrated changes in their understanding of the content and in their strategies for generating and interpreting evidence. In general, the adults outperformed the children. Neither valid strategies nor correct beliefs alone was sufficient to guarantee success, suggesting that regarding experimentation either as domain-general induction or as domain-specific learning may oversimplify its complexity.

Leona Schauble

Papers

Design Experiments in Educational Research

Beyond Modularity: A Developmental Perspective on Cognitive Science.

The development of scientific thinking skills

The development of scientific reasoning in knowledge-rich contexts.

Belief revision in children: the role of prior knowledge and strategies for generating evidence.