PERSONALIZED LEARNING BASED ON FUNCTIONAL SUMMARIZATION

摘要

Personalized learning based on functional summarization is disclosed. One example is a system including a content processor, a plurality of summarization engines, at least one meta-algorithmic pattern, an evaluator, and a selector. The content processor provides course material to be learned, the course material selected from a corpus of educational content, and identifies retained material indicative of a portion of the course material retained by a user. Each of the plurality of summarization engines provides a differential summary indicative of differences between the course material and the retained material. The at least one meta-algorithmic pattern is applied to at least two differential summaries to provide a meta-summary using the at least two differential summaries. The evaluator determines a value of each differential summary and meta-summary. The selector selects a meta-algorithmic pattern or a summarization engine that provides the meta-summary or differential summary, respectively, having the highest assessed value.