Analyzing and Supporting Students' Learning Behaviors in Computational STEM Learning Environments

Analyzing and Supporting Students' Learning Behaviors in Computational STEM Learning Environments


Computational thinking (CT) is the foundation of modern competency in a multitude of STEM-related fields. Learning strategies influence the way a student processes information and learns, which in turn, requires the proper control and regulation of their cognitive processes. More specifically, an effective learning strategy requires students to have adequate descriptive, procedural, and conditional knowledge of the strategies they apply. This project will study the learning processes of middle school students when they are involved in learning science using a computational modeling approach. The research that will be performed will help us to better understand how to effectively integrate CT and computing into the K-12 science curricula. To examine how learning evolves, and the difficulties students face in developing and applying synergistic processes, a task-oriented framework will be developed to analyze student learning behaviors as they work on modeling and problem-solving tasks. This project will use adaptive scaffolding to synergistically teach scientific concepts with computational modeling to middle school students. Student learning behaviors will be analyzed using self-regulated learning theory. This project aims to make the following research contributions: (1) develop a framework for analyzing student learning behaviors and strategies as they are involved in their model building, model debugging, and problem solving tasks; (2) use a combination of students activity logs and eye tracking data to understand student cognitive and metacognitive processes as they work in the C2STEM environment; and (3) study the effectiveness of the adaptive scaffolding and feedback generation framework by analyzing how this helps students overcome their difficulties, and progress in their learning and problem solving tasks. An intelligent peer agent in an artificial intelligence system will be developed to achieve these goals.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.


Principal Investigator(s): 


Award Number: 



2020 - 2023


Vanderbilt University Nashville, TN

Project Work State: 

Project Status: