Academic Benchmarks is the market-leading collection of learning standards metadata and concept and topic taxonomies. Stored in a powerful recommendation engine, Academic Benchmarks allows content creators and curators to make relevant content eminently discoverable via content enrichment. Academic Benchmarks covers more than 4 million standards with extensive coverage and deep granularity, supporting highly enriched content.
New Academic Benchmarks Webinar
Jen Salta, VP of Curriculum Development with Glynlyon’s® Odysseyware®describes how AB Connect’s powerful artificial intelligence-based engine provides machine-assisted tagging and alignment of learning assets, with a high degree of efficiency and accuracy.
Comprehensive Metadata Repository
More than standards, our collection of rich learning metadata, enhanced numbering, and content mapping provide myriad possibilities for content enrichment, search and discovery.
Applications and APIs designed to efficiently create, manage and access content alignment and enrichment via intelligent metadata connections and a powerful recommendation engine – all in the Certica Connect platform.
Our specialists have deep expertise with standards and learning metadata, standards alignment and industry best practices to accelerate learning product development.
Since 2003, Academic Benchmarks has organized and augmented publicly available academic standards “documents.” Through this process, Academic Benchmarks leverages subject matter experts and technologies to assign descriptive elements and unique identifiers, commonly referred to as “metadata,” to the standards.
Academic Benchmarks partners have traditionally downloaded files containing standards and some of the descriptive metadata. With the growing market demand and sophistication, Academic Benchmarks introduced cloud-based access to the amassed metadata and connections to its base of users. The volume of metadata, the complexity of the connections, the increased variety of use cases and the availability of a powerful recommendation engine led Academic Benchmarks to adapt its access mechanisms with cloud-based technologies to expand the metadata accessible to partners and the way it is used. The Benchmark Profile, the description of what should be learned, was born.
The Benchmark Profile enables access to the breadth and depth of Academic Benchmarks standards metadata. The manifest of a Benchmark Profile may consist of a selected set of data packages that expose deepening levels of metadata.
AB Connect allows for access and management of standards metadata, enriching content to be easily searched, discovered, recommended and analyzed. AB Connect centralizes content metadata, taxonomies and standards via a unified metadata model. AB Connect supports standards-based metadata enrichment of content as an integral part of the content development workflow. Capabilities include:
More than 200 providers of learning applications, data systems, assessments, and education content use Academic Benchmarks technology and services today. Here’s a sampling of what they’re doing.
Curriculum developers and publishers use AB standards and rich metadata to generate alignments and enrich their content, broadening their product reach. To ensure standards coverage and inform their editorial process, they use reporting and gap analysis to identify standards that may not be covered. Publishers invest in search capabilities and use analytics to better understand utilization of their learning resources.
Given a high volume of content, the need for a flexible environment and user management of content, an OER provider’s primary AB need is to curate the OERs to make them more searchable and discoverable. OER platforms can make robust use of analytics and mine trend and usage data from content resources. Relationships in the data can point to areas that the crowd may not be addressing.
Learning Management System providers can build algorithms based on standards metadata and content profiles, to analyze and recommend learning pathways. LMS providers use analytics and faceting to understand past usage, and inform learning algorithms that recommend future pathways.
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