Decreasing Digital Distraction in College Students: Associated Online Learning Strategies Identified by Unsupervised Data Mining Approaches

2026-09-03

AI Summary

研究通过无监督数据挖掘技术识别与减少大学生数字分心相关的在线学习策略，发现自我调节学习策略等与较低的数字分心相关。

Abstract

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.

Problem

• digital distractions
• online learning
• academic performance

Innovation

• unsupervised data mining
• association rule mining
• clustering analysis
• self-regulated learning strategies
• digital distractions