1. A method for correlating job recall materials to educational courses, the method comprising: extracting learning units, by an automated learning units extraction system of an online education platform, from content stored on a platform content catalog database of the online education platform, the extracting comprising: performing text extraction on the content to extract sequences of glyphs and embedded fonts; mapping the extracted sequences of glyphs and embedded fonts into character strings in a Unicode format, a position of each character identified by a horizontal and a vertical location; generating multilayered markup language documents from the character strings by aggregating content layers onto the character strings, the content layers comprising advertising content, social content, and user-generated content; identifying, from the multilayered markup language documents, content media types of the content comprising primary sources and secondary sources, the primary sources comprising one or more of a summary, a syllabus, and a textbook, the secondary sources comprising one or more of related content, user-generated content, Q&A, and testing materials, and sorting the content media types according to reliability of the content media types, wherein reliability of the primary sources is higher than reliability of secondary sources receiving at the online education platform, a job recall material uploaded to the online education platform; extracting properties from the job recall material, the properties comprising content attributes and delivery attributes of the job recall material; mapping, by the online education platform, the job recall material to at least one of the plurality of learning units based on the extracted properties of the job recall material; and responsive to a user of the online education platform completing the at least one of the plurality of learning units to which the job recall material is mapped, recommending the job recall material to the user.
1. A method for correlating job recall materials to educational courses, the method comprising: extracting learning units, by an automated learning units extraction system of an online education platform, from content stored on a platform content catalog database of the online education platform, the extracting comprising: performing text extraction on the content to extract sequences of glyphs and embedded fonts; mapping the extracted sequences of glyphs and embedded fonts into character strings in a Unicode format, a position of each character identified by a horizontal and a vertical location; generating multilayered markup language documents from the character strings by aggregating content layers onto the character strings, the content layers comprising advertising content, social content, and user-generated content; identifying, from the multilayered markup language documents, content media types of the content comprising primary sources and secondary sources, the primary sources comprising one or more of a summary, a syllabus, and a textbook, the secondary sources comprising one or more of related content, user-generated content, Q&A, and testing materials, and sorting the content media types according to reliability of the content media types, wherein reliability of the primary sources is higher than reliability of secondary sources receiving at the online education platform, a job recall material uploaded to the online education platform; extracting properties from the job recall material, the properties comprising content attributes and delivery attributes of the job recall material; mapping, by the online education platform, the job recall material to at least one of the plurality of learning units based on the extracted properties of the job recall material; and responsive to a user of the online education platform completing the at least one of the plurality of learning units to which the job recall material is mapped, recommending the job recall material to the user. 9. The method of claim 1 , wherein the extracting of the learning units further comprises: slicing the content into logical groups by identifying the logical groups, based at least in part on structure of the content, wherein the structure is one or more of a subsection of a textbook, a chapter of a study guide, and a paragraph of a course summary; and dicing the logical groups into key phrases, using language analysis, wherein each key phrase comprises a combination of an operator and an operand.