Digital Twin Simulation of Self-Regulated Learning
This project develops an agent-based simulation model that integrates the Self-Regulated Learning (SRL) framework with task complexity and the Community of Inquiry (CoI) constructs of teaching and social presence to explore factors shaping learner performance. The model simulates learners progressing through SRL phases, task definition, goal setting, enacting tactics, and adaptation, while updating attributes such as self-efficacy, effort, and metacognitive awareness. Results show that combined instructor and peer feedback significantly enhance performance, particularly under high task complexity.
I work with
Jeonghyun Lee,
Meryem Yilmaz Soylu,
Steve Harmon,
Eric Sembrat.
Automatic detection of cognitive presence (Spring 2025)
Multilabel classification and detection of cognitive presence in recommendation letters involve analyzing text to identify multiple cognitive indicators (e.g., intellectual engagement, problem-solving) expressed within the letters.
I work with Jeonghyun Lee, Meryem Yilmaz Soylu.
OMSCS Student Success ML Predictions (Fall 2024)
The project uses various machine learning techniques to examine the impact of different preparatory factors, including prior coursework in Python, data structures, and algorithms, as well as self-directed learning, on student success measures, particularly their weighted GPA.
I work with Alex Duncan, Jeonghyun Lee, Meryem Yilmaz Soylu.