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Found 12 papers, 1 papers with code
Bridging Declarative, Procedural, and Conditional Metacognitive Knowledge Gap Using Deep Reinforcement 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).
Leveraging Deep Reinforcement Learning for Metacognitive Interventions across Intelligent Tutoring Systems
In two consecutive semesters, we conducted two classroom experiments: Exp.
Mixing Backward- with Forward-Chaining for Metacognitive Skill Acquisition and Transfer
In this work, students were trained on a logic tutor that supports a default forward-chaining (FC) and a backward-chaining (BC) strategy.
The Power of Nudging: Exploring Three Interventions for Metacognitive Skills Instruction across Intelligent Tutoring Systems
Deductive domains are typical of many cognitive skills in that no single problem-solving strategy is always optimal for solving all problems.
Investigating the Impact of Backward Strategy Learning in a Logic Tutor: Aiding Subgoal Learning towards Improved Problem Solving
Backward problem-solving strategy is closely related to the process of subgoaling, where problem solving iteratively refines the goal into a new subgoal to reduce difficulty.
Enhancing a Student Productivity Model for Adaptive Problem-Solving Assistance
In this paper, we present a novel data-driven approach to incorporate students' hint usage in predicting their need for help.
Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks
Knowledge tracing (KT) models are a popular approach for predicting students' future performance at practice problems using their prior attempts.
Extending the Hint Factory for the assistance dilemma: A novel, data-driven HelpNeed Predictor for proactive problem-solving help
A core problem in solving the assistance dilemma is the need to discover when students are unproductive so that the tutor can intervene.
Avoiding Help Avoidance: Using Interface Design Changes to Promote Unsolicited Hint Usage in an Intelligent Tutor
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 hints for optimal learning outcomes.
How Widely Can Prediction Models be Generalized? Performance Prediction in Blended Courses
We also evaluate the models on different segments of the courses to determine how early reliable predictions can be made.
Early Prediction of Course Grades: Models and Feature Selection
In this paper, we compare predictive models for students' final performance in a blended course using a set of generic features collected from the first six weeks of class.
The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces
In particular, we extend the Hint Factory by considering data of past students in all states which are similar to the student's current state and creating hints approximating the weighted average of all these reference states.
