Data Modeling
Mathematical and Computational Data Modeling
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Volume 4 Number 1 2023
RESEARCH ARTICLE (Open Access)
Optimizing Online Learning Environments: Investigating Distractions and Strategies in Online Coding Classes
Md. Afraim Bin Zahangir, Md. Shah Jalal, and Khondaker A. Mamun
Data Modeling 4 (1) 1-8 https://doi.org/10.25163/data.4110811
Submitted: 20 September 2023 Revised: 12 November 2023 Accepted: 21 November 2023 Published: 23 November 2023
Abstract
Something curious happens once a programming class moves onto a screen: the very device meant to deliver the lesson quietly becomes, more often than not, the thing pulling attention away from it. This study set out to understand that pull and, perhaps more usefully, what might loosen its grip. Drawing on a mixed-methods design—structured surveys and interviews spanning 300 participants split evenly across parents, students, and teachers—the research traces distraction in online coding classes back to three recurring, and somewhat tangled, sources: thin real-time oversight, a weakened instructor-student connection, and material that's either too dense to follow or too dull to hold attention. What stood out, oddly enough, was how consistently these threads surfaced across all three groups; students were fairly candid about games and social media pulling them away, parents admitted they simply don't know what their children are doing once the screen turns on, and teachers, somewhat self-critically, named their own platforms as part of the trouble. From these overlapping concerns, the paper proposes an integrated online conferencing system built around real-time activity monitoring, parental oversight tools, interactive annotation features, pop-up quizzes that briefly restrict outside browsing, automatic class recording, and dedicated class-wide forums. None of this is offered as a silver bullet—more a grounded, fairly modest step toward classrooms where attention, however fragile, has a slightly better chance of staying put.Keywords: online coding education, student distraction, self-regulated learning, mixed-methods research, parental monitoring, e-learning engagement, instructional technology
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