activity
20172023
most citedHow Widely Can Prediction Models be Generalized? Performance Prediction in Blended Courses

55 citations · 203 across the 22 of their papers we have counts for

collaborators

22 papers

cs.CY2023★ 6 cited

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…

cs.CY2023★ 2 cited

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…

cs.HC2023

Mixing Backward- with Forward-Chaining for Metacognitive Skill Acquisition and Transfer

Mark Abdelshiheed, John Wesley Hostetter, Xi Yang +2

Metacognitive skills have been commonly associated with preparation for future learning in deductive domains. Many researchers have regarded strategy- and time-awareness as two met…

cs.HC2023★ 5 cited

The Power of Nudging: Exploring Three Interventions for Metacognitive Skills Instruction across Intelligent Tutoring Systems

Mark Abdelshiheed, John Wesley Hostetter, Preya Shabrina +2

Deductive domains are typical of many cognitive skills in that no single problem-solving strategy is always optimal for solving all problems. It was shown that students who know ho…

cs.HC2023★ 7 cited

Preparing Unprepared Students For Future Learning

Mark Abdelshiheed, Mehak Maniktala, Song Ju +3

Based on strategy-awareness (knowing which problem-solving strategy to use) and time-awareness (knowing when to use it), students are categorized into Rote (neither type of awarene…

cs.CY2022

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…