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Traditional approaches to content management have evolved from glorified FTP Servers (File Transfer) Protocol) or combinations of file systems with overlaid databases controlling access in a more elegant manner—but with the cost of higher system complexity and resource overhead. As these systems transformed from document management to web content management, the degree of overlap across their respective technologies became evident.
Most content management systems continue to be built on the idea that all users are aware, and proficient in, the way the system stores and manipulates information. This mindset is really not too far from the file and folder structures embraced wholeheartedly at the end of the last decade.
As the advent of Web 2.0 indicates, the dogmatic notion that users must be on intimate terms with the inner workings of a content management systems is flawed. Realising that Roundbox Global has created the RoundboxCMS™ Learning Object Repository (LOR) that distills content assets and learning objects to an atomic or granular level.We refer to this as Molecular Content Management™. As a key component complementing tools in an organisation’s learning technology portfolio, RoundboxCMS™ enables content, asset and object reuse without complex conversion, import or export.
Molecular Content Management™ is the concept of establishing asset relationships similar to the way molecular bonding creates elements. Content and learning object contributors require little knowledge of the content universe stored or managed within RoundboxCMS.Assets distilled to a granular level can be assembled to deal with learning outcomes, learner remediation and ancillary topics that support learning objectives. A granular ability to address content enables tracking assets for rights and use management, while offering the larger community the ability to access content objects that do not need to be created locally. For example, a math object could be created once and reused across geographies. In addition, semantic analysis – a technique derived from natural language processing – is used to analyse the relationship between documents and the terms they contain.
