What is the RAD Engine?
The ACT Recommendation and Diagnostics (RAD) Engine is used to continuously track evidence of learning, diagnose skill mastery & generate personalized recommendations.
RAD is delivered through an API and combines the power of an intelligent educational-content delivery platform with state-of-the-art, real-time skill estimate tracking. The RAD engine fully integrates into any existing learning and assessment system and aligns to any subject or set of standards. The RAD diagnostic engine powers adaptive delivery of relevant, free, and personalized content to meet the needs of learners everywhere.
” We aim to provide learners with tools and experiences that are integrated, personalized and adaptive. This requires algorithmic development in computational psychometrics and a focus on developing interfaces that provide evidence-based feedback to learners with actionable advice. The research, innovation and development of the RAD API – and its integration into ACT Academy – is a concrete, applied capability that demonstrates ACTNext delivering on its promise of what is coming next at ACT.”
– Steve Polyak, ACTNext Senior Director for Research Innovation Development
Learn more about the RAD diagnostic engine and download technical details on the project page.
The RAD diagnostic engine offers insights and actions that augment an existing learning and assessment system (LAS).
The RAD API works with any learning platform’s existing measurement resources. It only requires metadata associations linking item response data to one or more underlying skills. These skills can be associated with any hierarchical skill taxonomy, e.g. common core state standards, NGSS, or the ACT Holistic Framework, among others. ACT announced in a November press release that they are “… partnering with Smart Sparrow – a courseware design platform provider – in order to deliver RAD in an easy-to-configure way to thousands of courseware design teams worldwide.”
While RAD uses the OpenEd catalog to find relevant instructional content for learner recommendations, it can work with any learning object repository (LOR) tagged with the skill taxonomy used to perform item alignment.
RAD automatically incorporates the configuration and customization of each learning and assessment environment at runtime. The RAD API parameters allow users to specify both the taxonomy to use (i.e. skill taxonomy) and the level at which to provide insights and actions.
By offering the capability to fine-tune RAD to meet the needs of courseware providers, and by providing learners with rich, personalized recommendations made possible through RAD’s diagnostic engine, ACT takes one step closer to transforming the future of learning through transformational technology.