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Media Learning Behavior Analysis Research Report Study on Learning Duration, Course Completion Rate, and Grade Distribution

Year: 2026
Type: preprint
Abstract: With the in-depth promotion of educational informatization, online learning has become an important form of education, but it faces the problems of low pass rate and high dropout rate. This study focuses on the analysis of online learning behavior, examines the learning time, course completion rate, score distribution, and explores the optimization direction of platform effectiveness. Adopt the combination of literature review, empirical analysis and case study to build a theoretical framework, establish a behavioral characteristic engineering that includes learning time, course completion rate, performance distribution and interaction characteristics, and explore the construction of prediction models such as classification, integrated learning, deep learning and clustering. The study found that there is a positive correlation between learning duration, course completion rate and score distribution, which is an important indicator for measuring the effect of online learning; behavior analysis technology has developed into a deep learning model, and the prediction accuracy rate reaches more than 95%; learners show different behavior patterns such as high-input, low-input, jumping, strategic, etc.; in Online teaching design has an important impact on learning behavior. Based on the conclusion, optimization suggestions are put forward from the three levels of platform, teaching design and teaching management: the platform should strengthen the ability of data collection and analysis, and establish a learning early warning and personalized recommendation mechanism; teaching design should create situations, enrich resources, strengthen interaction, and improve feedback; teaching management should strengthen behavior supervision and establish early warning machines. System, carry out teacher training, and establish a learning incentive mechanism. This research provides a scientific basis for personalized recommendation and curriculum design of online education platforms, which is of great value for improving the quality of online education.
DOI: 10.21203/rs.3.rs-10202959/v1
Author: Yunyi Wang
Language: English
Open Access status: green
OpenAlex ID: W7203835706
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