IEEE Transactions on Pattern Analysis and Machine Intelligence

Traditional engineering instruction is deductive, beginning with theories and progressing to the applications of those theories Alternative teaching approaches are more inductive Topics are introduced by presenting specific observations, case studies or problems, and theories are taught or the students are helped to discover them only after the need to know them has been established This study reviews several of the most commonly used inductive teaching methods, including inquiry learning, problem-based learning, project-based learning, case-based teaching, discovery learning, and just-in-time teaching The paper defines each method, highlights commonalities and specific differences, and reviews research on the effectiveness of the methods While the strength of the evidence varies from one method to another, inductive methods are consistently found to be at least equal to, and in general more effective than, traditional deductive methods for achieving a broad range of learning outcomes

Inductive Teaching and Learning Methods: Definitions, Comparisons, and Research Bases

The World is Flat: A Brief History of the Twenty-First Century Thomas L. Friedman Farrar, Straus and Giroux, 2005 Thomas Friedman is a widely-acclaimed journalist, foreign affairs columnist for the New York Times, and author of four best-selling books that include From Beirut to Jerusalem (1989). His eminence as a journalist is clearly demonstrated in the way he prepared for The World is Flat. He traveled throughout the world, interviewing in depth the political and business leaders who have the most direct, hands-on knowledge of the truly incredible developments occurring in the business structures and technology of globalization. Only a journalist who moves freely at the highest levels could interview the likes of Sir John Rose, the chief executive of Rolls-Royce; Nobuyuki Idei, the chairman of Sony; Richard Koo, the chief economist for the Nomura Research Institute; Bill Gates of Microsoft; Wee Theng Tan, the president of Intel China; David Baltimore, president of Caltech; Howard Schultz, founder and chairman of Starbucks; Nandan Nilekani, CEO of Infosys in Bangalore - and many others, each of whom gave him the inside story of how, specifically, the barriers of time and space separating economies, workforces, sources of capital, and technical abilities are crumbling. The result of this unfolding story, already far along but with much farther to go, according to Friedman, is that "the world is flat." With some notable exceptions in sub-Saharan Africa and the Islamic swathe, everything is connected with everything else on a horizontal basis, with distance and erstwhile time-lags no longer mattering. Friedman describes in detail the galloping globalization that has unfolded in even so limited a time as the past five years. Under the impetus of a worldwide network of interconnectivity, the world economy is much-changed from what it was at the turn of the century a mere half-decade ago. Friedman quotes the CEO of India's Infosys: "What happened over the last [few] years is that there was a massive investment in technology, especially in the bubble era, when hundreds of millions of dollars were invested in putting broadband connectivity around the world, undersea cables," while (Friedman paraphrases him) "computers became cheaper and dispersed all over the world, and there was an explosion of software - e-mail, search engines like Google, and proprietary software that can chop up any piece of work and send one part to Boston, one part to Bangalore, and one part to Beijing...." Microprocessors today have 410 million transistors compared to the 2800 they had in 1971. And now, "wireless is what will allow you to take everything that has been digitized, made virtual and personal, and do it from anywhere." The effect on productivity is revolutionary: "It now takes Boeing eleven days to build a 737, down from twenty-eight days just a few years ago. Boeing will build the next generation of planes in three days, because all the parts are computer-designed for assembly." The most strikingly informative aspect of this book, however, is not about technology. Most especially, Friedman explores the rapidly evolving global business systems, each constantly regenerating itself to keep ahead of the others. These are systems that span the continents seeking the lowest-cost providers of everything from expert scientific and engineering work to the lowliest grunt work. Friedman points out that India produces 70,000 accounting graduates each year - and that they are willing to start at $100 a month. It is no wonder that Boeing employs 800 Russian scientists and engineers for passenger-plane design when "a U.S. aeronautical engineer costs $120 per design hour, a Russian costs about one-third of that." Friedman describes a call center in India where outbound callers sell "everything from credit cards to phone minutes," while operators taking inbound calls do "everything from tracing lost luggage for U.S. and European airline passengers to solving computer problems for confused American consumers. …

The World Is Flat: A Brief History of the Twenty-First Century

Traditional engineering instruction is deductive, beginning with theories and progressing to the applications of those theories. Alternative teaching approaches are more inductive. Topics are introduced by presenting specific observations, case studies or problems, and theories are taught or the students are helped to discover them only after the need to know them has been established. This study reviews several of the most commonly used inductive teaching methods, including inquiry learning, problem-based learning, project-based learning, case-based teaching, discovery learning, and just-in-time teaching. The paper defines each method, highlights commonalities and specific differences, and reviews research on the effectiveness of the methods. While the strength of the evidence varies from one method to another, inductive methods are consistently found to be at least equal to, and in general more effective than, traditional deductive methods for achieving a broad range of learning outcomes.

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This is the first handbook where the world’s foremost “experts on expertise” review our scientific knowledge on expertise and expert performance and how experts may differ from non-experts in terms of their development, training, reasoning, knowledge, social support, and innate talent. Methods are described for the study of experts’ knowledge and their performance of representative tasks from their domain of expertise. The development of expertise is also studied by retrospective interviews and the daily lives of experts are studied with diaries. In 15 major domains of expertise, the leading researchers summarize our knowledge of the structure and acquisition of expert skill and knowledge and discuss future prospects. General issues that cut across most domains are reviewed in chapters on various aspects of expertise, such as general and practical intelligence, differences in brain activity, self-regulated learning, deliberate practice, aging, knowledge management, and creativity.

The Cambridge Handbook of Expertise and Expert Performance

An image-based, trainable symbol recognizer for hand-drawn sketches

Freehand sketching is a natural and crucial part of everyday human interaction, yet is almost totally unsupported by current user interfaces. We are working to combine the flexibility and ease of use of paper and pencil with the processing power of a computer, to produce a user interface for design that feels as natural as paper, yet is considerably smarter. One of the most basic steps in accomplishing this is converting the original digitized pen strokes in a sketch into the intended geometric objects. In this paper we describe an implemented system that combines multiple sources of knowledge to provide robust early processing for freehand sketching.

/pdf/sketch-based-interfaces-early-processing-for-sketch-ck7vcj8qdi.pdf

Sketch based interfaces: early processing for sketch understanding

A long standing challenge in pen-based computer interaction is the ability to make sense of informal sketches. A main difficulty lies in reliably extracting and recognizing the intended set of visual objects from a continuous stream of pen strokes. Existing pen-based systems either avoid these issues altogether, thus resulting in the equivalent of a drawing program, or rely on algorithms that place unnatural constraints on the way the user draws. As one step toward alleviating these difficulties, we present an integrated sketch parsing and recognition approach designed to enable natural, fluid, sketch-based computer interaction. The techniques presented in this paper are oriented toward the domain of network diagrams. In the first step of our approach, the stream of pen strokes is examined to identify the arrows in the sketch. The identified arrows then anchor a spatial analysis which groups the uninterpreted strokes into distinct clusters, each representing a single object. Finally, a trainable shape recognizer, which is informed by the spatial analysis, is used to find the best interpretations of the clusters. Based on these concepts, we have built SimuSketch, a sketch-based interface for Matlab's Simulink software package. An evaluation of SimuSketch has indicated that even novice users can effectively utilize our system to solve real engineering problems without having to know much about the underlying recognition techniques.

Hierarchical parsing and recognition of hand-sketched diagrams

We disclose a trainable recognizer for multi-stroke symbols. Pen speed and the curvature are examined to segment a stroke into the constituent primitives. The primitives are determined to be lines or arcs using, for example, a least squares best fit method. Symbol recognition (training) results in the construction of definitions consisting of the constituent geometric primitives (lines and arcs), the properties of individual primitives, and the geometric relationships between them. A definition is learned by examining a few examples of a symbol and identifying which properties and relationships occur frequently. Thereafter, during symbol recognition (matching), an unknown symbol is identified by determining which definition matches the unknown with the least error. We disclose two recognition methods. One assumes that the primitives of a symbol are always drawn in the same order. This method is fast, but requires some care from the drawer. The other method uses a form of best-first search, with a speculative quality metric and pruning, to recognize symbols when the drawing order is varied. During both training and recognition, multiple primitives can be drawn in the same pen stroke.

/pdf/recognizing-multi-stroke-symbols-4tvk2c57m2.pdf

Recognizing multi-stroke symbols

Freehand sketching is a natural and crucial part of everyday human interaction, yet is almost totally unsupported by current user interfaces. We are working to combine the exibility and ease of use of paper and pencil with the processing power of a computer, to produce a user interface for design that feels as natural as paper, yet is considerably smarter. One of the most basic steps in accomplishing this is converting the original digitized pen strokes in a sketch into the intended geometric objects. In this paper we describe an implemented system that combines multiple sources of knowledge to provide robust early processing for freehand sketching.

Thomas F. Stahovich

Papers

An image-based, trainable symbol recognizer for hand-drawn sketches

Sketch based interfaces: early processing for sketch understanding

Hierarchical parsing and recognition of hand-sketched diagrams

Recognizing multi-stroke symbols

Sketch based interfaces: early processing for sketch understanding