Survey dataset and analysis code for digital distraction strategy mining

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Evidence · 6 claims (6 for) · 1 study (1 associational), q2 · 0 of 1 report an effect size · 6 claims rest on one study

Description

An OSF-hosted dataset of self-reported college-student survey responses and accompanying R analysis code for mining associations between online-learning strategies and digital distraction, released by Shi et al.

Components

• Survey dataset and analysis code for digital distraction strategy mining (OSF repository): The article states that the data and code supporting its findings are openly available in an OSF repository. The dataset underlies the two-stage analysis: association rule mining over 447 dichotomized binary variables from 530 valid survey responses, followed by PAM clustering of the resulting rules. The article reports that "The data and code supporting the findings of this study are available at https://doi.org/10.17605/OSF.IO/P85S3". It serves researchers replicating or extending the unsupervised data-mining approach to digital distraction. (Shi et al. (2025))

Claims

• Demographic characteristics (gender, age, race, major) showed no significant association with lower digital distraction in the rule-mining profiles [+W]
• Learner-instructor and learner-content engagement strategies are negatively associated with digital distraction in online courses [+W]
• In association-rule mining of survey data from 530 college students in online courses, goal setting, environment structuring and time management were the strategies most often associated with lower digital distraction [+W]
• Peer-related help-seeking and learner-learner engagement strategies appear less often in profiles of students with lower digital distraction [+W]
• Clustering of association rules yields four distinct strategy-combination profiles characterizing lower digital distraction [+W]
• Technical confidence using digital learning tools is positively associated with lower distraction levels among online learners [+W]

Related Products and Programmes

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Key Sources

• Shi, H., Bi, R., Lin, X., & Dai, Y. (2025). Decreasing Digital Distraction in College Students: Associated Online Learning Strategies Identified by Unsupervised Data Mining Approaches. https://doi.org/10.17605/OSF.IO/P85S3

Page metadata

Field	Value
type	product
id	survey-dataset-and-analysis-code-for-digital-distraction-strategy-mining
title	Survey dataset and analysis code for digital distraction strategy mining
description	An OSF-hosted dataset of self-reported college-student survey responses and accompanying R analysis code for mining associations between online-learning strategies and digital distraction, released by Shi et al.
status	review
product_kind	dataset
generated	by process:settle-candidates on 2026-10-10
trust	unverified — no one has confirmed this page's content

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