Individualized learning potential in stressful times: How to leverage intensive longitudinal data to inform online learning.
Individualized learning potential in stressful times: How to leverage intensive longitudinal data to inform online learning.
复制标题
压力时期的个性化学习潜力：如何利用密集的纵向数据为在线学习提供信息。
DOI：
10.1016/j.chb.2021.106772
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发表时间：
2021
影响因子：
9.9
通讯作者：
Beltz,AdrieneM
中科院分区：
文献类型：
--
作者：
Chaku,Natasha;Kelly,DominicP;Beltz,AdrieneM
社会事件--如自然灾害、政治变化或经济衰退--是时变的，并以独特的方式影响学生的学习潜力。这些影响可能在新冠肺炎大流行期间加剧，那次大流行促使中国突然大规模转向在线教育。不幸的是，这些事件的个人层面的后果很难确定，因为现有的文献侧重于只产生群体层面推断的单一场合调查。为了更好地理解压力和学习的个体水平差异，可以利用密集的纵向数据。本文的目的是通过讨论分析密集纵向数据的三种不同技术来说明这一点：(1)回归分析；(2)多水平模型；(3)特定于个人的网络模型(例如，组迭代多模型估计；GIMME)。对于每种技术，提供了教育研究背景的简要背景，使用来自大学生的数据进行了说明性分析，这些大学生在2016年美国总统大选期间完成了为期75天的认知、躯体症状、焦虑和智力兴趣的密集纵向研究，并考虑了优势和局限性。文章最后对未来的研究提出了建议，特别是对新冠肺炎期间在线教育的深入纵向研究提出了建议。
Societal events – such as natural disasters, political shifts, or economic downturns – are time-varying and impact the learning potential of students in unique ways. These impacts are likely accentuated during the COVID-19 pandemic, which precipitated an abrupt and wholesale transition to online education. Unfortunately, the individual-level consequences of these events are difficult to determine because the extant literature focuses on single-occasion surveys that produce only group-level inferences. To better understand individual-level variability in stress and learning, intensive longitudinal data can be leveraged. The goal of the paper is to illustrate this by discussing three different techniques for the analysis of intensive longitudinal data: (1) regression analyses; (2) multilevel models; and (3) person-specific network models, (e.g., group iterative multiple model estimation; GIMME). For each technique, a brief background in the context of education research is provided, an illustrative analysis is presented using data from college students who completed a 75-day intensive longitudinal study of cognition, somatic symptoms, anxiety, and intellectual interests during the 2016 U.S. Presidential election – a period of heightened sociopolitical stress – and strengths and limitations are considered. The paper ends with recommendations for future research, especially for intensive longitudinal studies of online education during COVID-19.