Learning Labels Create vs Learn AI

Two primary goals of the recently patented Learning Labels system is to provide standard definitions for learning and jobs in skills to:  1) Combine and make basis of comparisons decisions 2) Build better, more responsive learning pathways With this initial release of Learning Labels Create in IOS, the objective is to allow practitioners, teachers / professors, trainers, or job sourcing personal to create learning, course, or job labels quickly, intuitively and as needed. Learning labels define and quantify what is learned from a task, experience or activity. They could be put together into a series for a project, course, or training module requirements. One public review of this representation compares them to nutritional labels because of their succinctness and readability for all ages and backgrounds. Course labels build from learning labels, actually referencing them, by summarizing higher order learning in skills and Skill Points. A clear advantage with a course label is much of the data and fields are part of another interpretation (and app) Skill Syllabus; both interpretations work together with no duplication of information. Job labels summarize prior experience requirements and first year application of skill for any type of job. The definitions use the same skills and Skill Points referenced in the learning and course labels. To achieve a basis of comparison, compensation is annualized, time constraints clearly stated, and the order of skills change based on the data set (such as a search result). Our tagline sums up the learning pathways objective - Map Learning in Skills. Map Jobs in Skills. Combine to Create Pathways. What differentiates the platform is the attempt to take a scientific approach in mapping the skills to the elements and on a granular level (tasks and experiences).

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