  In deductive domains, three metacognitive knowledge types in ascending order
are declarative, procedural, and conditional learning. This work leverages Deep
Reinforcement Learning (DRL) in providing adaptive metacognitive interventions
to bridge the gap between the three knowledge types and prepare students for
future learning across Intelligent Tutoring Systems (ITSs). Students received
these interventions that taught how and when to use a backward-chaining (BC)
strategy on a logic tutor that supports a default forward-chaining strategy.
Six weeks later, we trained students on a probability tutor that only supports
BC without interventions. Our results show that on both ITSs, DRL bridged the
metacognitive knowledge gap between students and significantly improved their
learning performance over their control peers. Furthermore, the DRL policy
adapted to the metacognitive development on the logic tutor across declarative,
procedural, and conditional students, causing their strategic decisions to be
more autonomous.
