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

了解部署的认知，情感，元认知，和动机（CAMM）过程中的超媒体学习的背景下，是理解的线性，迭代和动态展开这些过程中的自我调节学习（SRL）的成功和不成功的学习者。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，生理装置（例如，EMG传感器）、视频捕获和记录设备、眼动跟踪设备和语音记录，以尝试系统地捕获、识别和分析复杂学习期间这些过程的部署。本研究将扩展现有的理论、方法论和分析方法与工具，包括：（1）通过精心设计的超媒体环境，实验性地诱导特定的自主学习过程，研究学生在超媒体自主学习过程中如何实现学习目标;（2）研究CAMM过程中的波动及其与学习结果的关系;和（3）建立研究协议，最大限度地发挥研究人员？通过使用多个生理传感器，在SRL期间利用超媒体收敛并同时收集CAMM状态的时间部署的能力。更广泛的影响，这项研究包括设计和开发的学习干预措施，自适应计算机为基础的学习环境，旨在检测，建模，跟踪，并培养学生？自我调节学习。更具体地说，教学处方将集中在关键的学习问题，如适应性，元认知监测和控制的作用，以及在超媒体学习的动机和影响的调节。

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.