The present disclosure relates to computing and data processing, and in particular, to computer automated learning management systems and methods.
One of the key factors in individual and organizational success is the ability of individuals to learn. For an organization, enabling employee learning can result in enormous success across all areas of operation. Similarly, it may be desirable to facilitate learning, and also to find company-specific literature, answer questions, and tag and retrieve content for an organizational glossary.
Traditionally, employee learning was limited to colleges, universities, employee self-motivation, and “on-the-job training,” all of which was typically limited. Some modern learning systems track learning for determining promotions or to ensure compliance of particular organizational functions—i.e., to ensure people performing particular job functions have the appropriate training and/or certifications to perform the jobs and tasks they are assigned. Accordingly, compliance based learning systems are typically restrictive, static, and simplistic—e.g., employee X cannot do task Y unless they have completed course M; job function Z requires at least degree A.
Traditional approaches of learning are inherently limiting in terms of reaction times to new trends, employee reach, and availability of content. Human resource departments need help shifting from a planner and administrator to a curator role. However, internal content is expensive to produce and becomes obsolete fast. Maintaining relevant content becomes a technical and administrative problem, especially if the system is to be automated and highly customized for individual users.
Another problem pertaining to the advancement of automated learning is that employees often do not know what they need to learn. Furthermore, even if they have an idea of what they need to learn, they often cannot easily find the learning content (e.g., courses, articles, etc. . . . ) required to learn it. Compounding the complexity of the problem, computerized identification of relevant learning content, and the presentation of such learning content to individuals in an organization in a highly customized and efficient manner, requires analysis and organization of large amounts of seemingly unrelated elements of data. With existing technology, computational costs are likely high, and meaningful results are likely uncertain.
Thus, it would be desirable to have a computationally efficient mechanism for enabling a highly customized computer automated learning system for individuals in an organization.