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Toward Scalable Improvement of Large Content Portfolios for Adaptive Instruction

Recent literature demonstrates data-driven improvements to content used in adaptive instructional systems like intelligent tutoring systems, following a multi-method approach to “design loop adaptivity.” Examples from the literature are often relatively bespoke, focusing on a particular piece of content within a system and applying several, often time-consuming, methods to redesign important elements of content and deliver improved learning experiences. We draw attention to the problem of targeting and focusing design-loop adaptivity to make such data-driven improvement more scalable for large content portfolios. Targeting involves choosing the goal to be achieved by this improvement and the content that requires improvement. We build on our recent work on targeting by also considering what we call focusing design-loop adaptivity, which involves determining what aspects of the learning experience require (the most) improvement and the method(s) by which to achieve improvement. We present examples of how targeting may proceed and raise important questions about how to focus data-driven learning engineering improvement processes.