Bibliography:
Valentine, S.; Vides, F.; Lucchese, G.; Turner, D.; Kim, H.; Li, W.; Linsey, J.; and Hammond, T. "Mechanix: A Sketch-Based Tutoring and Grading System for Free-Body Diagrams." AI Magazine. Winter 2012. 55-66. Print.Link:
http://www.aaai.org/ojs/index.php/aimagazine/article/view/2437/2347Summary:
Mechanix is a system designed to aide in the instruction and grading of statics problems of mechanical and civil engineering students. The system leverages sketching to teach statics by "actively engaging" students in the learning process. In one mode of the program students are asked to sketch out trusses and are given constant feedback in their drawings. Such immediate feedback on hand drawn sketches is impossible in large class sizes. This is where sketch recognition allows real-time feed back and automated instruction.
Other similar systems were either too general, did not incorporate the action of sketching the drawings, or were too strict in their drawing order. Mecanix utilizes a powerful low-level recognizer, PaleoSketch, to recognize high-level complex shapes.
The interface consists of a problem statement, standard edit tools, a step-by-step checklist, notepad area, drawing pane, feedback area, and equation pane. Students can check their answers at anytime by clicking a submit button and feedback is instantly provided to guide students.The interface is similar for instructors except they provide critical information and constraints about the truss and non-truss drawings. The problems are saved to the server where they can be retrieved by students. The interfaces also provides information on students submissions.
Students can draw, move, label, color, edit parts, or totally delete shapes. Visual feedback about completed shapes signal mistakes to students.
Geometric recognition of shapes is handled by a bottom-up approach by recognizing low-level shapes through PaleoSketch, and then recognizing groupings of shapes as high-level complex shapes. Trusses are described as two or more convex polygons connected by shared edges. Shared edges are found by removing edges from a connectivity graph and doing a breadth-first search for alternate paths between the two points that made up that edge. Checking answers includes comparing students drawing to instructors drawings and comparison of forces. Feedback is given in the case of found errors.
Problems are also given in non-truss free-body diagrams which are just closed shapes. The comparison between student and instructor drawing then employ three similarity metrics: Hausdorff distance, a modified Hausdorff distance, and the Tanimoto coefficient. The first two used measured the closest distances between points of the two drawings and the last used a ratio of overlapping points. These were then averaged and used as metric to determine similarity.
Also creative response problems where the answer was "open-ended" was also supported. In this case the validity of answers was handled by an artificial intelligence which creates a linear system of equations from the student drawn truss. These are then compared to a list of instructor given constraints and feedback is given.
The system is distributed on servers and load-balancing is used to mitigate stoppages when students submit answers.
Students who used Mechanix scored 40 percent higher on homework assignments.
Other similar systems were either too general, did not incorporate the action of sketching the drawings, or were too strict in their drawing order. Mecanix utilizes a powerful low-level recognizer, PaleoSketch, to recognize high-level complex shapes.
The interface consists of a problem statement, standard edit tools, a step-by-step checklist, notepad area, drawing pane, feedback area, and equation pane. Students can check their answers at anytime by clicking a submit button and feedback is instantly provided to guide students.The interface is similar for instructors except they provide critical information and constraints about the truss and non-truss drawings. The problems are saved to the server where they can be retrieved by students. The interfaces also provides information on students submissions.
Students can draw, move, label, color, edit parts, or totally delete shapes. Visual feedback about completed shapes signal mistakes to students.
Geometric recognition of shapes is handled by a bottom-up approach by recognizing low-level shapes through PaleoSketch, and then recognizing groupings of shapes as high-level complex shapes. Trusses are described as two or more convex polygons connected by shared edges. Shared edges are found by removing edges from a connectivity graph and doing a breadth-first search for alternate paths between the two points that made up that edge. Checking answers includes comparing students drawing to instructors drawings and comparison of forces. Feedback is given in the case of found errors.
Problems are also given in non-truss free-body diagrams which are just closed shapes. The comparison between student and instructor drawing then employ three similarity metrics: Hausdorff distance, a modified Hausdorff distance, and the Tanimoto coefficient. The first two used measured the closest distances between points of the two drawings and the last used a ratio of overlapping points. These were then averaged and used as metric to determine similarity.
Also creative response problems where the answer was "open-ended" was also supported. In this case the validity of answers was handled by an artificial intelligence which creates a linear system of equations from the student drawn truss. These are then compared to a list of instructor given constraints and feedback is given.
The system is distributed on servers and load-balancing is used to mitigate stoppages when students submit answers.
Students who used Mechanix scored 40 percent higher on homework assignments.
Comments:
I believe this system is excellent. The advantages of having feedback and sketching the problems by hand had undeniable positive consequences. Some of the tests similarity were foreign to me. I liked how the authors leveraged an existing system, PaleoSketch, to solve a more complex recognition problem.
Your pre-viz idea is very interesting and one of the most unique ones I've seen so far. What sort of methods would you envision using to solve for best camera and lighting placement? I can imagine drawing a few storyboards on a system like this and having it compare things like consistency of the camera angle; is that the sort of thing you're thinking about or do you have something more sophisticated in mind? Either way, it is a great idea!
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