减少大学生的数字干扰：通过无监督数据挖掘方法识别相关在线学习策略
Decreasing Digital Distraction in College Students: Associated Online Learning Strategies Identified by Unsupervised Data Mining Approaches
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中文总结 AI 辅助
本研究运用无监督数据挖掘技术，分析530名大学生数据，识别出与低数字干扰相关的在线学习策略，为教育者设计针对性干预措施提供了依据。
中文摘要 AI 辅助
数字工具在教育领域的普及带来诸多益处，但也引发了重大挑战，尤其是数字干扰会阻碍学业表现，在线学习场景中该问题尤为突出。本研究采用无监督数据挖掘技术，具体为关联规则挖掘与聚类分析，以识别与较低数字干扰水平相关的有效学习策略。530名参与者的数据显示，自我调节学习策略（即目标设定、环境构建与时间管理）与较低数字干扰的共现性最高；此外，学习者与教师的互动策略、学习者与内容的互动策略以及技术能力，也常出现在低干扰的行为模式中。值得注意的是，依赖同伴求助及学习者间互动策略在低干扰模式中出现频率较低。这些发现为教育者设计针对性干预措施提供了可操作的启示，有助于营造专注且高效的在线学习环境。
英文摘要
The proliferation of digital tools in education offers numerous benefits but also introduces significant challenges, notably digital distractions that hinder academic performance, especially in online learning contexts. This study employed unsupervised data mining techniques, specifically association rule mining and clustering analysis, to identify effective learning strategies associated with lower levels of digital distractions among college students. Data from 530 participants revealed that self-regulated learning strategies (i.e., goal setting, environment structuring, and time management) co-occurred most consistently with lower digital distractions. Additionally, learner-instructor and learner-content engagement strategies, as well as technical competencies, also tended to appear in the same profiles as lower distraction. Interestingly, reliance on peer help-seeking and learner-learner engagement strategies appeared less often in those lower distraction profiles. These findings offer actionable implications for educators to design targeted interventions that foster focused and productive online learning environments.
发表机构
- Central China Normal University（华中师范大学）
- SAS Institute Inc（SAS研究所）
- East Carolina University（东卡罗来纳大学）
- Tuskegee University（塔斯基吉大学）
机构由 AI 辅助整理，请以论文原文为准。
