Conventional techniques for automatically evaluating and grading assignments are generally ill-suited to evaluation of coursework submitted in media-rich form. For courses whose subject includes programming, signal processing or other functionally expressed designs that operate on, or are used to produce media content, conventional techniques are also ill-suited. It has been discovered that media-rich, indeed even expressive, content can be accommodated as, or as derivatives of, coursework submissions using feature extraction and machine learning techniques. Accordingly, in on-line course offerings, even large numbers of students and student submissions may be accommodated in a scalable and uniform grading or scoring scheme. Instructors or curriculum designers may adaptively refine assignments or testing based on classifier feedback. Using developed techniques, it is possible to administer courses and automatically grade submitted work that takes the form of media encodings of artistic expression, computer programming and even signal processing to be applied to media content.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for use in connection with automated evaluation of coursework submissions, the method comprising: receiving from an instructor or curriculum designer a selection of exemplary media content to be used in evaluating the coursework submissions, the exemplary media content including a training set of examples each assigned at least one quality score by the instructor or curriculum designer; accessing computer readable encodings of the exemplary media content that together constitute the training set and extracting from each instance of exemplary media content a first set of computationally defined features; for each instance of exemplary media content, supplying a classifier with both the instructor or curriculum designer's assigned quality score and values for the computationally defined features extracted therefrom; based on the supplied quality scores and extracted feature values, training the classifier, wherein the training includes updating internal states thereof; accessing a computer readable encoding of media content that constitutes, or is derived from, the coursework submission and extracting therefrom a second set of computationally defined features; and applying the trained classifier to the extracted second set of computationally defined features and, based thereon, assigning a particular quality score to the coursework submission. 2. A method as in claim 1, further comprising: supplying plural additional classifiers with respective instructor or curriculum designer's assigned quality scores and values for computationally defined features extracted from respective instances of the exemplary media content; training the additional classifiers; and applying the trained additional classifiers. 3. A method as in claim 1, wherein the quality score is, or is a component of, a grading scale for an assignment- or test question-type coursework submission. 4. A method as in claim 1, wherein the media content that constitutes, or is derived from, the coursework submission includes an image or video signal encoding, the method further comprising: segmenting the image or video frame to define at least one region of interest; and extracting, for the segmented at least one region of interest, a particular computationally defined feature of the second set. 5. A method as in claim 4, wherein the particular computationally defined feature characterizes one of: size or position of the region of interest relative to a rule of thirds alignment or point; and focus of, or within, the region of interest relative at least one other portion of the image or video frame. 6. A method as in claim 4, wherein the segmenting provides foreground/background segmentation. 7. A method as in claim 4, wherein the segmenting includes adaptively refined keypoint sets, wherein adaptive refinement is based on at least one of: iteration on the keypoint sets using automatically updated keypoint detection parameters; and computational clustering to refine the keypoint sets. 8. A method as in claim 1, wherein the coursework submission includes software code submitted in satisfaction of a programming assignment or test question, the software code executable to perform, or compilable to execute and perform, digital signal processing to produce output media content; wherein the exemplary media content includes exemplary output media content produced using exemplary software codes; and wherein the particular quality score assigned to the coursework submission is based on the applying of the classifier to the second set of computationally defined features extracted from the output media content produced by execution of the submitted software code. 9. A method as in claim 8, wherein the software code coursework submission is executable to perform digital signal processing on input media content to produce the output media content; and wherein the exemplary output media content is produced from the input media content using the exemplary software codes. 10. A method as in claim 8, wherein the media content includes images or video processed or rendered by the software code coursework submission. 11. A method as in claim 1, wherein the coursework submission includes a computer readable media encoding of expressive media content selected from the set of: sketches, paintings, photographic images or other artistic still visuals; and synchronized audiovisual content, computer animation or other video that is itself expressive or visually captures underlying expression such as dance, acting, or other performance. 12. A method as in claim 1, wherein the media content that constitutes, or is derived from, the coursework submission includes an audio signal encoding; and wherein for the first and second sets, at least some of the computationally defined features are selected or derived from: a root mean square energy value; a number of zero crossings per frame; a spectral flux; a spectral centroid; a spectral roll-off measure; a spectral tilt; a mel-frequency cepstral coefficients (MFCC) representation of short-term power spectrum; a beat histogram; and/or a multi-pitch histogram computed over at least a portion of the audio signal encoding. 13. A method as in claim 1, wherein the media content that constitutes, or is derived from, the coursework submission includes an image or video signal encoding; and wherein for the first and second sets, at least some of the computationally defined features are selected or derived from: color histograms; two-dimensional transforms; edge, corner or ridge detections; curve or curvature features; a visual centroid; and/or optical flow computed over at least a portion of the image or video signal encoding. 14. A method as in claim 1, wherein the classifier implements an artificial neural network (NN), k-nearest neighbor (KNN), Gaussian mixture model (GMM), support vector machine (SVN) or other statistical classification technique. 15. A method as in claim 1, further comprising: iteratively refining the classifier training based on supply of successive instances of the exemplary media content to the classifier and updates to internal states thereof. 16. A method as in claim 1, wherein the classifier is implemented using one or more logical binary decision trees, blackboard voting-type methods, or rule-based classification techniques. 17. A method as in claim 15, further comprising: continuing the iterative refining until an error metric based on a current state of classifier training falls below a predetermined or instructor or curriculum designer-defined threshold. 18. A method as in claim 1, further comprising: supplying the instructor or curriculum designer with an error metric based on a current state of classifier training. 19. A method as in claim 1, further comprising: supplying the instructor or curriculum designer with a coursework task recommendation based on a particular one or more of the computationally defined features that contribute most significantly to classifier performance against the training set of exemplary media content. 20. A method as in claim 1, wherein the first and second sets of computationally defined features are the same. 21. A method as in claim 1, wherein the second set of computationally defined features includes a subset of the first set features selected based on contribution to classifier performance against the training set of exemplary media content. 22. A method as in claim 1, wherein the quality score is, or is a component of a grading scale for an assignment- or test question-type coursework submission. 23. A method as in claim 1, further comprising: receiving from the instructor or curriculum designer at least an initial definition of the first set of computationally defined features. 24. A computational system including one or more operative computers programmed to perform the method of claim 1. 25. The computational system of claim 24 embodied, at least in part, as a network deployed coursework submission system, whereby a large and scalable plurality (>50) of geographically dispersed students may individually submit their respective coursework submissions in the form of computer readable information encodings. 26. The computational system of claim 25 including a student authentication interface for associating a particular coursework submission with a particular one of the geographically dispersed students. 27. A non-transient computer readable encoding of instructions executable on one or more operative computers to perform the method of claim 1. 28. A coursework management system for automated evaluation of coursework submissions, the system comprising: an instructor or curriculum designer interface for selecting or receiving exemplary media content to be used in evaluating the coursework submissions, the exemplary media content including a training set of examples each assigned at least one quality score by the instructor or curriculum designer; a training subsystem coupled and programmed to access computer readable encodings of the exemplary media content that together constitute the training set and to extracting from each instance of exemplary media content a first set of computationally defined features; the training subsystem further programmed to, for each instance of exemplary media content, supply a classifier with both the instructor or curriculum designer's assigned quality score and values for the computationally defined features extracted therefrom, and to, based on the supplied quality scores and extracted feature values, train the classifier, wherein the training includes updating internal states thereof; and a coursework evaluation deployment of the trained classifier coupled and programmed to access a computer readable encoding of media content that constitutes, or is derived from, the coursework submissions and to extract therefrom a second set of computationally defined features, wherein the coursework evaluation deployment applies the trained classifier to the extracted second set of computationally defined features and, based thereon, assigns a particular quality score to the coursework submission. 29. The coursework management system as in claim 28, wherein the training subsystem supplies plural additional classifiers with respective instructor or curriculum designer's assigned quality scores and values for computationally defined features extracted from respective instances of the exemplary media content and trains the additional classifiers; and wherein coursework evaluation deployment also applies the trained additional classifiers. 30. The coursework management system as in claim 28, further comprising: an execution environment, wherein the coursework submission includes software code submitted in satisfaction of a programming assignment or test question, the software code executable in the execution environment to perform, or compilable to execute in the execution environment and perform, digital signal processing to produce output media content; wherein the exemplary media content includes the output media content produced using the submitted software code; and wherein the particular quality score assigned to the coursework submission is based on the applying of the classifier to the second set of computationally defined features extracted from the output media content produced using the submitted software code. 31. The coursework management system as in claim 30, wherein the output media content includes audio signals processed or rendered by the software code coursework submission. 32. The coursework management system as in claim 28, wherein the classifier implements an artificial neural network (NN), k-nearest neighbor (KNN), Gaussian mixture model (GMM), support vector machine (SVN) or other statistical classification technique. 33. The coursework management system as in claim 28, wherein the training subsystem allows the instructor or curriculum designer to iteratively refine the classifier training based on supply of successive instances of the exemplary media content to the classifier and updates to internal states thereof. 34. The coursework management system as in claim 28, wherein the classifier is implemented using one or more logical binary decision trees, blackboard voting-type methods, or rule-based classification techniques. 35. A coursework management system comprising: means for selecting or receiving exemplary media content to be used in evaluating the coursework submissions, the exemplary media content including a training set of examples each assigned at least one quality score by the instructor or curriculum designer; means for extracting from each instance of exemplary media content a first set of computationally defined features, for supplying a classifier with both the instructor or curriculum designer's assigned quality score and values for the computationally defined features extracted therefrom and, based on the supplied quality scores and extracted feature values, for training the classifier; and means for extracting from the coursework submissions a second set of computationally defined features, for applying the trained classifier to the extracted second set of computationally defined features and, based thereon, for assigning a particular quality score to the coursework submission.