16 citations · 67 across the 21 of their papers we have counts for
4 papers · 1 filter
Bridging Declarative, Procedural, and Conditional Metacognitive Knowledge Gap Using Deep Reinforcement Learning
Mark Abdelshiheed, John Wesley Hostetter, Tiffany Barnes +1
In deductive domains, three metacognitive knowledge types in ascending order are declarative, procedural, and conditional learning. This work leverages Deep Reinforcement Learning…
Leveraging Deep Reinforcement Learning for Metacognitive Interventions across Intelligent Tutoring Systems
Mark Abdelshiheed, John Wesley Hostetter, Tiffany Barnes +1
This work compares two approaches to provide metacognitive interventions and their impact on preparing students for future learning across Intelligent Tutoring Systems (ITSs). In t…
A Multicriteria Evaluation for Data-Driven Programming Feedback Systems: Accuracy, Effectiveness, Fallibility, and Students' Response
Preya Shabrina, Samiha Marwan, Andrew Bennison +3
Data-driven programming feedback systems can help novices to program in the absence of a human tutor. Prior evaluations showed that these systems improve learning in terms of test…
Investigating the Impact of Backward Strategy Learning in a Logic Tutor: Aiding Subgoal Learning towards Improved Problem Solving
Preya Shabrina, Behrooz Mostafavi, Mark Abdelshiheed +2
Learning to derive subgoals reduces the gap between experts and students and makes students prepared for future problem solving. Researchers have explored subgoal labeled instructi…