Systems and methods to assist an instructor of a course

A system for customizing on-line modular delivery of a course comprising;

• a processor-based server;
• at least one data store;
• and
• an interactive student GUI for collecting and forwarding to said server student behaviors together with associated time factors, where said student behaviors comprise at least mouse and keyboard actions;
• wherein said at least one data store is used for storing a collection of course module with the frequency of its text terms, a syllabus with the frequency of its text terms, at least one user model, and a collection of known behaviors with associated topical understanding;
• and
• said processor-based server provides course modules to a student selected based on determined student topical material understanding, said understanding based on said user model and a comparison of said student events to said known behaviors, implementing a method of:
• with an engine for processing sound, image, and natural language, identifying learning concepts in a module by tagging content words and topics based on word and topic frequencies in each said module, storing said tags in association with a corresponding module and associating said modules with said frequencies and with terms in a course syllabus;
• formulating motifs from behavioral data of prior students, each said motif including a sequence of student actions together with associated time, said motifs formed using a probabilistic mixture model wherein each position in a motif is described using a mixture of (1) a position-dependent motif model where each position is described as a multinomial distribution specifying one or more probabilities and (2) a position-independent background model where each position is described as a multinomial distribution specifying character occurrence independent of position, and said formulating further including a maximum likelihood estimation with an expectation-maximization algorithm to maximize the expectation of the joint likelihood of the mixture model;
• associating said motifs with said learning concepts using said tags;
• further associating said motifs with student understanding by algorithmically extracting similarities between prior user behavioral data and assessments by using collaborative filtering, thereby forming motif vectors;
• storing said motifs and motif vectors in said at least one data store;
• relative to a particular student delivering a first course module in a sequence of modules, said sequence representing a full course, to said GUI, said first course module retrieved from said at least one data store and including at least one of text, video, and images;
• during on-going delivery of said module, said GUI capturing all student actions and associated times, said server receiving said captured student actions relative to said delivered portions of said module and associated times;
• formulating an individual student vector for a particular student, said individual student vector initially formed as a user model for said particular student, said individual student vector modified based on the particular student's behavioral data on-goingly as the student progresses in a module, said student behavioral data including actions and associated times;
• comparing said individual student vector for said particular student with said motif vectors in said data store to form a predictive understanding of the particular student by forming a file-to-file similarity matrix and using said matrix in a supervised learning algorithm to mathematically determine a best match between said individual student vector and said motif vectors;
• based on said best match, selecting alternate content as appropriate for said particular student;
• and
• during on-going module delivery replacing content in a not yet delivered portion of the course with said alternate content in consistency with said best match.