16 citations · 67 across the 21 of their papers we have counts for
21 papers
Exploring the Design and Impact of Interactive Worked Examples for Learners with Varying Prior Knowledge
Sutapa Dey Tithi, Xiaoyi Tian, Ally Limke +2
Tutoring systems improve learning through tailored interventions, such as worked examples, but often suffer from the aptitude-treatment interaction effect where low prior knowledge…
Adaptive Scaffolding for Cognitive Engagement in an Intelligent Tutoring System
Sutapa Dey Tithi, Nazia Alam, Tahreem Yasir +4
The ICAP framework defines four cognitive engagement levels: Passive, Active, Constructive, and Interactive, where increased cognitive engagement can yield improved learning. Howev…
Investigating the Impact and Student Perceptions of Guided Parsons Problems for Learning Logic with Subgoals
Sutapa Dey Tithi, Xiaoyi Tian, Min Chi +1
Parsons problems (PPs) have shown promise in structured problem solving by providing scaffolding that decomposes the problem and requires learners to reconstruct the solution. Howe…
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…
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…
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…
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…
Assessing Competency Using Metacognition and Motivation: The Role of Time-Awareness in Preparation for Future Learning
Mark Abdelshiheed, Mehak Maniktala, Tiffany Barnes +1
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 environme…
Metacognition and Motivation: The Role of Time-Awareness in Preparation for Future Learning
Mark Abdelshiheed, Guojing Zhou, Mehak Maniktala +2
In this work, we investigate how two factors, metacognitive skills and motivation, would impact student learning across domains. More specifically, our primary goal is to identify…
HOPE: Human-Centric Off-Policy Evaluation for E-Learning and Healthcare
Ge Gao, Song Ju, Markel Sanz Ausin +1
Reinforcement learning (RL) has been extensively researched for enhancing human-environment interactions in various human-centric tasks, including e-learning and healthcare. Since…
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…
Enhancing a Student Productivity Model for Adaptive Problem-Solving Assistance
Mehak Maniktala, Min Chi, Tiffany Barnes
Research on intelligent tutoring systems has been exploring data-driven methods to deliver effective adaptive assistance. While much work has been done to provide adaptive assistan…
Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks
Yang Shi, Min Chi, Tiffany Barnes +1
Knowledge tracing (KT) models are a popular approach for predicting students' future performance at practice problems using their prior attempts. Though many innovations have been…
Time-Aware Q-Networks: Resolving Temporal Irregularity for Deep Reinforcement Learning
Yeo Jin Kim, Min Chi
Deep Reinforcement Learning (DRL) has shown outstanding performance on inducing effective action policies that maximize expected long-term return on many complex tasks. Much of DRL…
InferNet for Delayed Reinforcement Tasks: Addressing the Temporal Credit Assignment Problem
Markel Sanz Ausin, Hamoon Azizsoltani, Song Ju +2
The temporal Credit Assignment Problem (CAP) is a well-known and challenging task in AI. While Reinforcement Learning (RL), especially Deep RL, works well when immediate rewards ar…
The Impact of Looking Further Ahead: A Comparison of Two Data-driven Unsolicited Hint Types on Performance in an Intelligent Data-driven Logic Tutor
Christa Cody, Mehak Maniktala, Nicholas Lytle +2
Research has shown assistance can provide many benefits to novices lacking the mental models needed for problem solving in a new domain. However, varying approaches to assistance,…
An Adversarial Domain Separation Framework for Septic Shock Early Prediction Across EHR Systems
Farzaneh Khoshnevisan, Min Chi
Modeling patient disease progression using Electronic Health Records (EHRs) is critical to assist clinical decision making. While most of prior work has mainly focused on developin…
Avoiding Help Avoidance: Using Interface Design Changes to Promote Unsolicited Hint Usage in an Intelligent Tutor
Mehak Maniktala, Christa Cody, Tiffany Barnes +1
Within intelligent tutoring systems, considerable research has investigated hints, including how to generate data-driven hints, what hint content to present, and when to provide hi…
Extending the Hint Factory for the assistance dilemma: A novel, data-driven HelpNeed Predictor for proactive problem-solving help
Mehak Maniktala, Christa Cody, Amy Isvik +3
Determining when and whether to provide personalized support is a well-known challenge called the assistance dilemma. A core problem in solving the assistance dilemma is the need t…