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Item Newtons Pen II: An Intelligent, Sketch-Based Tutoring System and its Sketch Processing Techniques(The Eurographics Association, 2012) Lee, Chia-Keng; Jordan, Josiah; Stahovich, Thomas F.; Herold, James; Karan Singh and Levent Burak KaraWe present a pen-based intelligent tutoring system (ITS) for undergraduate Statics which scaffolds students in the construction of free body diagrams and equilibrium equations. Most existing ITSs rely on traditional WIMP (Windows, Icons, Menus, Pointers) interfaces, which often require the student to select the correct answer from among a set of predefined choices. Our system, by contrast, guides students in constructing solutions from scratch, mirroring the way they ordinarily solve problems, which recent research suggests is important for effective instruction. Our system employs several new techniques for sketch understanding, including a simple-to-implement stroke merging technique, a stroke clustering technique, and a technique that uses a Hidden Markov Model to correct interpretation errors in equations. Our tutoring system was deployed in an undergraduate Statics course at our university. Attitudinal surveys indicate that the tutoring system is preferable to traditional WIMP-based systems and is an effective educational tool.Item The One Cent Recognizer: A Fast, Accurate, and Easy-to-Implement Handwritten Gesture Recognition Technique(The Eurographics Association, 2012) Herold, James; Stahovich, Thomas F.; Karan Singh and Levent Burak KaraWe present the One Cent Recognizer, an easy-to-implement, efficient, and accurate handwritten gesture recognizer. By applying time series recognition techniques, we have developed a minimally complex technique that is both much faster than and at least as accurate as the Dollar Recognizer. Additionally, the One Cent Recognizer is much easier to implement than the Dollar Recognizer. Our technique is primarily enabled by a simple and novel one-dimensional representation of handwritten pen strokes. This representation is intrinsically rotation invariant, allowing our technique to avoid costly rotate-and-check searches typically employed in prior template-based gesture recognition techniques. In experiments, our technique has proven to be two orders of magnitude faster than the Dollar Recognizer.