Analyzing Multimodal Multichannel Data about Self-Regulated Learning with Advanced Learning Technologies: Issues and Challenges
Analyzing Multimodal Multichannel Data about Self-Regulated Learning with Advanced Learning Technologies: Issues and Challenges
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DOI：
10.1016/j.chb.2019.03.025
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发表时间：
2019-07-01
影响因子：
9.9
通讯作者：
Gasevic, Dragan
中科院分区：
文献类型：
--
作者：
Azevedo, Roger;Gasevic, Dragan
分析在使用智能教学系统、严肃游戏、超媒体和沉浸式虚拟学习环境等先进学习技术过程中获得的关于自我调节学习(SRL)的多通道数据，是理解认知、情感、元认知和社会过程之间的相互作用及其对所有年龄和背景的学习者的学习、问题解决、推理和概念理解的影响的关键。在这期《计算机在人类行为中的作用》特刊中，我们报告了六项由跨学科团队进行的研究，这些研究使用了各种跟踪方法，如眼睛跟踪、日志文件、生理数据、面部情感表达、屏幕录音、并发思维和话语的语言分析。研究集中于如何结合传统统计技术和教育数据挖掘程序来分析这些数据，以检测、测量和推断与跨任务、领域、年龄和背景的自我和他人调节相关的认知、元认知和社会过程。这些研究的结果表明，未来的工作需要跨学科的研究人员合作，使用基于理论和经验的方法来收集、测量和模拟多模式多通道SRL数据，以通过阐明潜在过程的性质、复杂性和时间性来扩展我们当前的模型、框架和理论，使它们更具预测性。最后，通过提供实时的、智能的、自适应的、个性化的支架和反馈来满足学习者的自我调节需求，多通道多通道SRL过程数据的分析可以显著增强先进的学习技术。
Analyzing multimodal multichannel data about self-regulated learning (SRL) obtained during the use of advanced learning technologies such as intelligent tutoring systems, serious games, hypermedia, and immersive virtual learning environments is key to understanding the interplay among cognitive, affective, metacognitive, and social processes and their impact on learning, problem solving, reasoning, and conceptual understanding in learners of all ages and contexts. In this special issue of Computers in Human Behavior, we report six studies conducted by interdisciplinary teams' use of various trace methodologies such as eye tracking, log-files, physiological data, facial expressions of emotions, screen recordings, concurrent think-alouds, and linguistic analyses of discourse. The research studies focus on how these data were analyzed using a combination of traditional statistical techniques as well as educational data-mining procedures to detect, measure, and infer cognitive, metacognitive, and social processes related to regulating the self and others across several tasks, domains, ages, and contexts. The results of these studies point to future work necessitating interdisciplinary researchers' collaboration to use theoretically based and empirically derived approaches to collecting, measuring, and modeling multimodal multichannel SRL data to extend our current models, frameworks, and theories by making them more predictive by elucidating the nature, complexity, and temporality of underlying processes. Lastly, analyses of multimodal multichannel SRL process data can significantly augment advanced learning technologies by providing real-time, intelligent, adaptive, individualized scaffolding and feedback to address learners' self-regulatory needs.