Self Organizing Maps (SOMS) for Organizing, Categorizing, Browsing and/or Grading Large Collections of Assignments for Massive Online Education Systems
First Claim
1. A method for use in connection with coursework submissions, the method comprising:
- retrieving from storage, media content to be used in evaluating or organizing the coursework submissions, wherein at least some instances of the retrieved media content constitute, or are derived from, respective ones of the coursework submissions;
for each instance of the media content, extracting a set of computationally defined features, wherein a set of values for the extracted computationally-defined features together constitute a k-dimensional feature vector that characterizes the corresponding instance of media content;
initializing elements of an n-dimensional map, n less than k, with current feature vectors; and
assigning successive instances of the media content to respective elements of the map to which they most closely correspond and iteratively morphing the current feature vectors to produce a self-organized mapping wherein individual instances of the media content are distributed over the map and associated with respective elements thereof.
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Abstract
For courses that deal with media content, such as sound, music, photographic images, hand sketches, video, conventional techniques for automatically evaluating and grading assignments are generally ill-suited to direct evaluation of coursework submitted in media-rich form. Likewise, 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. Instead, it has been discovered that media-rich, indeed even expressive, content can be accommodated as, or as derivatives of, submissions using feature extraction and machine learning techniques. In this way, e.g., 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. Likewise, large collections of coursework submissions (whether or not graded or scored) or media content more generally, may be efficiently browsed and grouped using techniques described herein.
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Citations
29 Claims
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1. A method for use in connection with coursework submissions, the method comprising:
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retrieving from storage, media content to be used in evaluating or organizing the coursework submissions, wherein at least some instances of the retrieved media content constitute, or are derived from, respective ones of the coursework submissions; for each instance of the media content, extracting a set of computationally defined features, wherein a set of values for the extracted computationally-defined features together constitute a k-dimensional feature vector that characterizes the corresponding instance of media content; initializing elements of an n-dimensional map, n less than k, with current feature vectors; and assigning successive instances of the media content to respective elements of the map to which they most closely correspond and iteratively morphing the current feature vectors to produce a self-organized mapping wherein individual instances of the media content are distributed over the map and associated with respective elements thereof. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29)
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Specification