Two metacognitive knowledge types in deductive domains are procedural and conditional.
This work presents a preliminary study on the impact of metacognitive knowledge and …

In deductive domains, three metacognitive knowledge types in ascending order are
declarative, procedural, and conditional learning. This work leverages Deep Reinforcement …

This work compares two approaches to provide metacognitive interventions and their impact
on preparing students for future learning across Intelligent Tutoring Systems (ITSs). In two …

Factual knowledge and procedural knowledge are knowing 'That'and 'How,'respectively,
whereas conditional knowledge is the metacognitive knowledge of 'When'and 'Why.'As prior …

This work investigates relationships between consistent attendance—attendance rates in a
group that maintains the same tutor and students across the school year—and learning in …

This work investigates how tutoring discourse interacts with students' proximal knowledge to
explain and predict students' learning outcomes. Our work is conducted in the context of …

One fundamental goal of learning is preparation for future learning (PFL) and being able to
extend acquired skills and problem-solving strategies to different domains and …

Deep Reinforcement Learning (Deep RL) has revolutionized the field of Intelligent Tutoring
Systems by providing effective pedagogical policies. However, the “black box” nature of …

Problem decomposition into sub-problems or subgoals and recomposition of the solutions to
the subgoals into one complete solution is a common strategy to reduce difficulties in …

A modern view of knowledge transfer is the ability to prepare individuals for future learning
by acquiring problem-solving skills and strategies across different domains. Despite the …