Learning analytics for reducing student dropout in digital video platforms
Year: 2026
Type: article
Study design: Clinical Trial
Abstract: Digital video platforms (DVPs) are experiencing increased popularity as educational resources, yet they face challenges in achieving sustainability due to dropout among students. This research aims to examine the effect of Learning Analytics (LA) on reducing dropout in a DVP environment. A quasi-experimental design was employed to compare experimental and control groups, each consisting of 30 sixth-grade students. The experimental group utilized DVP with LA, while the control group used DVP without LA. LA data included the completion percentage of full video viewing, time spent watching the video, correct and incorrect answers to the questions embedded within the video, number of views for each segment of the video and date and time of the last video viewing. A dropout scale was developed, encompassing three dimensions: satisfaction, engagement, and continuity. The results showed a significant reduction in dropouts in the LA group. LA also contributed to improving student satisfaction, engagement, and continuity within the DVP environment. The results also showed correlations and mutual influence between satisfaction, engagement, and continuity.
Source: PeerJ Computer Science
Institutions: King Abdulaziz University, Ain Shams University
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $1,395
Funder: King Abdulaziz University
OpenAlex ID: W7125425378