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.