Dedication Preface Foreword PART I: FRAME OF REFERENCE We begin with the idea of giving students the tools that increase their capacity for learning. The primary role of education is to increase student capacity for personal growth, social growth, and academic learning. Models of Teaching is an avenue to liberate student learning capacity and, by doing so, to help teachers take charge of their lives as teachers. CHAPTER 1: BEGINNING THE INQUIRY Creating Communities of Expert Learners On the whole, students are in schools and classes within those schools. Both need to be developed into learning communities and provided with the models of learning that enable them to become expert learners. We study how to build those learning communities. CHAPTER 2: WHERE MODELS OF TEACHING COME FROM Multiple Ways of Constructing Knowledge The history of teacher researchers comes to us in the form of models of teaching that enable us to construct vital environments for our students. Models have come from the ages and from teacher-researchers who have invented new ways of teaching. Some of these are submitted to research and development and how teachers can learn to use them. Those are the models that are included in this book. CHAPTER 3: STUDYING THE SLOWLY-GROWING KNOWLEDGE BASE IN EDUCATION A Basic Guide Through the Rhetorical Thickets We draw on descriptive studies, experimental studies, and experience to give us a fine beginning to what will eventually become a research-based profession. Here we examine what we have learned about how to design good instruction and effective curriculums. And, we learn how to avoid some destructive practices. CHAPTER 4: MODELS OF TEACHING AND TEACHING STYLES Three Sides of Teaching--Styles, Models, and Diversity We are people and our personalities greatly affect the environments that our students experience. And, as we use various models of teaching our selves -- our natural styles -- color how those models work in the thousands of classrooms in our society. Moreover, those models and our styles affect the achievement of the diverse students in our classes and schools. PART II: THE INFORMATION-PROCESSING FAMILYOF MODELS How can we and our students best acquire information, organize it, and explain it? For thousands of years philosophers, educators, psychologists, and artists have developed ways to gather and process information. Here are several live ones. CHAPTER 5: LEARNING TO THINK INDUCTIVELY Forming Concepts by Collecting and Organizing Information Human beings are born to build concepts. The vast intake of information is sifted and organized and the conceptual structures that guide our lives are developed. The inductive model builds on and enhances the inborn capacity of our students. CHAPTER 6: ATTAINING CONCEPTS Sharpening Basic Thinking Skills Students can develop concepts. They also can learn concepts developed by others. Concept attainment teaches students how to learn and use concepts and develop and test hypotheses. CHAPTER 7: THE PICTURE-WORD INDUCTIVE MODEL Developing Literacy across the Curriculum Built on the language experience approach, the picture-word inductive model enables beginning readers to develop sight vocabularies, learn to inquire into the structure of words and sentences, write sentences and paragraphs, and, thus, to be powerful language learners. In Chapter 19 the outstanding results from primary curriculums and curriculums for older struggling readers are displayed. CHAPTER 8: SCIENTIFIC INQUIRY AND INQUIRY TRAINING The Art of Making Inferences From the time of Aristotle, we have had educators who taught science-in-the-making rather than teaching a few facts and hoping for the best. We introduce you to a model of teaching that is science on the hoof, so to speak. This model has had effects, among other things, on improving the capacity of students to learn. We concentrate on the Biological Sciences Study Group, where for 40 years science teachers have shared information and generated new ideas. And, Inquiry training is a "best yet" model for teaching basic inquiry skills. CHAPTER 9: MEMORIZATION Getting the Facts Straight Memorization has had something of a bad name, mostly because of deadly drills. Contemporary research and innovative teachers have created methods that not only improve our efficiency in memorization, but also make the process delightful. CHAPTER 10: SYNECTICS The Arts of Enhancing Creative Thought Creative thought has often been thought of as the province of a special few, and something that the rest of us cannot aspire to. Not so. Synectics brings to all students the development of metaphoric thinking -- the foundation of creative thought. The model continues to improve. CHAPTER 11: LEARNING FROM PRESENTATIONS Advance Organizers Learning from presentations has almost as bad a name as learning by memorization. Ausubel developed a system for creating lectures and other presentations that will increase learner activity and, subsequently, learning. PART III: THE SOCIAL FAMILY OF MODELS Working together might just enhance all of us. The social family expands what we can do together and generates the creation of democracy in our society in venues large and small. In addition, the creation of learning communities can enhance the learning of all students dramatically. CHAPTER 12: PARTNERS IN LEARNING From Dyads to Group Investigation Can two students who are paired in learning increase their learning? Can students organized into a democratic learning community apply scientific methods to their learning? You bet they can. Group Investigation can be used to redesign schools, increase personal, social, and academic learning among all students, and -- is very satisfying to teach. CHAPTER 13: THE STUDY OF VALUES Role Playing and Public Policy Education Values provide the center of our behavior, helping us get direction and understand other directions. Policy issues involve the understanding of values and the costs and benefits of selecting some solutions rather than others. In these models, values are central. Think for a moment about the issues that face our society right now -- research on cells, international peace, including our roles in Iraq and the rest of the Middle East, the battle against AIDS, poverty, and who controls the decisions about pregnancy and abortion. Not to mention just getting along together. PART IV: THE PERSONAL FAMILY OF MODELS The learner always does the learning. His or her personality is what interacts with the learning environment. How do we give the learner centrality when we are trying to get that same person to grow and respond to tasks we believe will enhance growth? CHAPTER 14: NONDIRECTIVE TEACHING The Learner at the Center How do we think about ourselves as learners? As people? How can we organize schooling so that the personalities and emotions of students are taken into account? Let us inquire into the person who is the center of the education process. CHAPTER 15: DEVELOPING POSITIVE SELF-CONCEPTS The Inner Person of Boys and Girls, Men and Women If you feel great about yourself, you are likely to become a better learner. But you begin where you are. Enhancing self concept is a likely avenue. The wonderful work by the SIMs group in Kansas (see Chapter 3) has demonstrated how much can be accomplished. PART V: THE BEHAVIORAL SYSTEMS FAMILY OF MODELS We are what we do. So how do we learn to practice more productive behaviors? Let's explore some of the possibilities. CHAPTER 16: LEARNING TO LEARN FROM MASTERY LEARNING Bit by bit, block by block, we climb our way up a ladder to mastery. CHAPTER 17: DIRECT INSTRUCTION Why beat around the bush when you can just deal with things directly? Let's go for it! However, finesse is required, and that is what this chapter is all about. CHAPTER 18: LEARNING FROM SIMULATIONS Training and Self-Training How much can we learn from quasi-realities? The answer is, a good deal. Simulations enable us to learn from virtual realities where we can experience environments and problems beyond our present environment. Presently, they range all the way to space travel, thanks to NASA and affiliated developers. PART VI: INDIVIDUAL DIFFERENCES, DIVERSITY, AND CURRICULUM The rich countryside of humanity makes up the population of our schools. The evidence suggests that diversity enhances the energy of schools and classrooms. However, some forms of teaching make it difficult for individual differences to flourish. We emphasize the curriculums and models of teaching that enable individual differences to thrive. CHAPTER 19: LEARNING STYLES AND MODELS OF TEACHING Making Discomfort Productive By definition, learning requires knowing, thinking, or doing things we couldn't do before the learning took place. Curriculums and teaching need to be shaped to take us where we haven't been. The trick is to develop an optimal mismatch in which we are pushed but the distance is manageable. CHAPTER 20: EQUITY Gender, Ethnicity, and Socioeconomic Background The task here is to enable differences to become an advantage. The best curriculums and models of teaching do just that. In other words, if differences are disadvantages, it is because of how we teach. CHAPTER 21: CREATING AND TESTING CURRICULUMS The Conditions of Learning Robert Gagne's framework for building curriculums is discussed and illustrated. This content is not simple, but it is powerful. CHAPTER 22: TWO WORDS ON THE FUTURE The Promise of Distance Learning and Using Models of Teaching to Ensure that No Child is Left Behind. Afterword APPENDIX PEER COACHING GUIDES Related Literature and References Index

教学模式 = Models of Teaching

: Significant progress has been made with solution of location problems and in preprocessing and decomposition for discrete optimization. There has also been research on the application of techniques from combinational optimization to nonlinear problems.

http://www.dtic.mil/dtic/tr/fulltext/u2/a273552.pdf

Discrete Applied Mathematics

https://repository.usfca.edu/cgi/viewcontent.cgi?article=1091&context=nursing_fac

Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy

Preface 1. Nonlinear behavior in the physical sciences and biology: some typical examples 2. Quantitative formulation 3. Dynamical systems with a finite number of degrees of freedom 4. Linear stability analysis of fixed points 5. Nonlinear behavior around fixed points: bifurcation analysis 6. Spatially distributed systems, broken symmetries, pattern formation 7. Chaotic dynamics Appendices References Index.

Introduction to Nonlinear Science

Survey of machine learning techniques for malware analysis

In the domain of software watermarking, we have proposed several graph theoretic watermarking codec systems for encoding watermark numbers $w$ as reducible permutation flow-graphs $F[\pi^*]$ through the use of self-inverting permutations $\pi^*$. Following up on our proposed methods, we theoretically study the oldest one, which we call W-RPG, in order to investigate and prove its resilience to edge-modification attacks on the flow-graphs $F[\pi^*]$. In particular, we characterize the integer $w\equiv\pi^*$ as strong or weak watermark through the structure of self-inverting permutations $\pi^*$ which encodes it. To this end, for any integer watermark $w \in R_n=[2^{n-1}, 2^n-1]$, where $n$ is the length of the binary representation $b(w)$ of $w$, we compute the minimum number of 01-modifications needed to be applied on $b(w)$ so that the resulting $b(w')$ represents the valid watermark number $w'$; note that a number $w'$ is called valid (or, true-incorrect watermark number) if $w'$ can be produced by the W-RPG codec system and, thus, it incorporates all the structural properties of $\pi^* \equiv w$.

Characterizing Watermark Numbers encoded as Reducible Permutation Graphs against Malicious Attacks.

In this paper, we address the problem of computing a maximum-size subgraph of a P4-sparse graph which admits a perfect matching; in the case where the graph has a perfect matching, the solution to the problem is the entire graph. We establish a characterization of such subgraphs, and describe an algorithm for the problem which for a P4-sparse graph on n vertices and m edges, runs in O(n+m) time and space. The above results also hold for the class of complement reducible graphs or cographs, a well-known subclass of P4-sparse graphs.

/pdf/maximum-size-subgraphs-of-p4-sparse-graphs-admitting-a-xlw4gezpb5.pdf

Maximum-Size subgraphs of p4-sparse graphs admitting a perfect matching

We present efficient parallel algorithms for recognizing chordal graphs and locating all maximal cliques of a chordal graph G=(V,E). Our techniques are based on partitioning the vertex set V using information contained in the distance matrix of the graph. We use these properties to formulate parallel algorithms which, given a graph G=(V,E) and its adjacency-level sets, decide whether or not G is a chordal graph, and, if so, locate all maximal cliques of the graph in time O(k) by using δ2·n2/k processors on a CRCW-PRAM, where δ is the maximum degree of a vertex in G and 1≤k≤n. The construction of the adjacency-level sets can be done by computing first the distance matrix of the graph, in time O(logn) with O(nβ+DG) processors, where DG is the output size of the partitions and β=2.376, and then extracting all necessary set information. Hence, the overall time and processor complexity of both algorithms are O(logn) and O(max{δ2·n2/logn, nβ+DG}), respectively. These results imply that, for δ≤√nlogn, the proposed algorithms improve in performance upon the best-known algorithms for these problems.

/pdf/parallel-recognition-and-location-algorithms-for-chordal-31wu1c962v.pdf

Parallel Recognition and Location Algorithms for Chordal Graphs Using Distance Matrices

Sub-optimal solutions to track detection problem using graph theoretic concepts

We present a fast parallel algorithm for finding the blocks or biconnected components of an undirected graph G = (V,E ) having n vertices and m edges. Our techniques arc based on partitioning the vertex set V into adjacency-level sets using information contained in the distance matrix D of the graph. Let t D and p D be the time and number of processors, respectively, for the computation of the distance matrix of a graph G on a CRCW-PRAM computational model. We show that the location of all cut vertices and bridges of a graph can be done in time O(logδ + t D) by using O(n m/t d) processors, where δ is the maximum degree of a vertex in G. Based on these results, we define a digraph G d and we prove certain properties on its distance matrix leading to a parallel block-finding algorithm running in time O(logδ + t D) with O(n m/t D) processors on the same computational model. We also show that other connectivity-related problems can be efficiently solved using distance matrices.

/pdf/parallel-block-finding-using-distance-matrices-4isekdj00r.pdf

Stavros D. Nikolopoulos

Papers

Characterizing Watermark Numbers encoded as Reducible Permutation Graphs against Malicious Attacks.

Maximum-Size subgraphs of p4-sparse graphs admitting a perfect matching

Parallel Recognition and Location Algorithms for Chordal Graphs Using Distance Matrices

Sub-optimal solutions to track detection problem using graph theoretic concepts

Parallel block-finding using distance matrices