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

了解认知、情感、元认知和动机(CAMM)过程在超媒体学习环境中的部署，是理解成功和不成功学习者在自我调节学习(SRL)过程中这些过程的线性、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(例如，肌电传感器)、视频捕获和记录设备、眼睛跟踪设备和语音记录)，试图系统地捕获、识别和分析复杂学习过程中这些过程的部署。本项目将通过以下方式扩展当前的理论、方法和分析方法和工具：(1)研究学生如何通过超媒体实验诱导特定的超媒体学习过程来实现学习目标；(2)检验CAMM过程中的波动及其与学习结果的关系；以及(3)建立使研究人员最大化的研究方案？通过使用几个生理传感器，能够利用超媒体在SRL期间汇聚和并发收集CAMM状态的临时部署。这项研究的更广泛影响包括为适应性的基于计算机的学习环境设计和开发学习干预措施，旨在检测、建模、跟踪和培养学生？自我调节学习。更具体地说，教学处方将集中于关键的学习问题，如适应性，元认知监测和控制的作用，以及超媒体学习中动机和情感的调节。

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.