Toward a Modular Reinforcement Learning Framework for Tutorial Planning in GIFT
نویسندگان
چکیده
Intelligent tutoring systems (ITSs) are highly effective at fostering learning gains across a broad range of educational domains (VanLehn, 2011; Woolf, 2008). Tutorial planning is a critical component of ITSs, controlling how scaffolding is structured and delivered to learners. Tutorial planners operate at multiple levels, including the macro-level (e.g., selecting problems for learners to solve) and micro-level (e.g., delivering tailored hints about specific problems). Devising computational models that scaffold effectively—determining when to scaffold, what type of scaffolding to deliver, and how scaffolding should be realized—is a critical challenge for the field.
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