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

计算思维（CT）是众多STEM相关领域现代能力的基础。学习策略影响着学生处理信息和学习的方式，这反过来又要求对他们的认知过程进行适当的控制和调节。更具体地说，有效的学习策略要求学生对他们所应用的策略有足够的描述性、程序性和条件性知识。本计画将以电脑模拟的方式，研究中学生在学习科学时的学习过程。将进行的研究将帮助我们更好地了解如何有效地将CT和计算融入K-12科学课程。为了研究学习如何演变，以及学生在开发和应用协同过程中面临的困难，将开发一个以任务为导向的框架来分析学生在建模和解决问题任务时的学习行为。这个项目将使用自适应脚手架协同教中学生科学概念与计算建模。学生的学习行为将使用自我调节学习理论进行分析。本研究的主要目的是：（1）建立一个分析学生在模型建立、模型调试和问题解决过程中的学习行为和策略的框架;（2）结合学生的活动日志和眼动数据，了解学生在C2 STEM环境中的认知和元认知过程;及（3）研究自适应支架及反馈生成框架的有效性，分析它如何帮助学生克服困难，并在学习和解决问题的任务中取得进展。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.