SGER: Detecting, Identifying, and Analyzing Cognitive, Affective, Metacognitive, and Motivational (CAMM) States During Self-Regulated Learning with Hypermedia

理解认知、情感、元认知和动机（CAMM）过程在超媒体学习背景下的部署，是理解这些过程在成功和不成功学习者自我调节学习（SRL）过程中线性、迭代和动态展开的关键。然而，这是一个非常困难的问题。这个探索性项目的目标是使用尖端传感器（即生理设备（如肌电传感器）、视频捕捉和记录设备、眼动追踪设备和录音设备），试图系统地捕捉、识别和分析这些过程在复杂学习过程中的部署。本项目将扩展现有的理论、方法和分析方法和工具，通过：(1)通过精心设计超媒体环境，通过实验诱导特定的SRL过程，研究学生如何在使用超媒体的SRL过程中完成学习目标；(2)研究CAMM过程的波动及其与学习成果的关系；(3)建立最大化研究人员的研究方案？在SRL期间，通过使用多个生理传感器，能够收敛并同时收集CAMM状态的时间部署。本研究的广泛影响包括设计和开发适应性计算机学习环境的学习干预措施，旨在检测、建模、跟踪和培养学生。自主学习。更具体地说，教学处方将侧重于关键的学习问题，如适应性、元认知监测和控制的作用，以及超媒体学习过程中动机和影响的调节。

Understanding the deployment of cognitive, affective, metacognitive, and motivation (CAMM) processes in the context of hypermedia learning is key to understanding the linear, iterative, and dynamic unfolding of these processes during self-regulated learning (SRL) among successful and unsuccessful learners. However, this is a very difficult problem. The goal of this exploratory project is to use cutting-edge sensors (i.e., physiological devices (e.g., EMG sensors), video capturing and recording devices, eye-tracking equipment, and voice recording to attempt to systematically capture, identify, and analyze the deployment of these processes during complex learning. This project will extend current theoretical,methodological, and analytical methods and tools by: (1) studying how students accomplish learning goals during SRL with hypermedia by experimentally inducing specific SRL processes through the deliberate design of a hypermedia environment; (2) examining the fluctuations in the CAMM processes and how they are related to learning outcomes; and (3) establishing research protocols that maximize researchers? ability to converge and concurrently collect the temporal deployment of CAMM states during SRL with hypermedia via the use of several physiological sensors. The broader impact of this research includes the design and development of learning interventions for adaptive computer-based learning environments designed to detect, model, trace, and foster students? self-regulated learning. More specifically, instructional prescriptions will be derived focusing on key learning issues such as adaptivity, the role of metacognitive monitoring and control, and the regulation of motivation and affect during hypermedia learning.