Work

Learning from Student Browsing Data on E-Learning Platforms: Case Study

Year: 2020

Type: conference-paper

Abstract: Interpretation of the behaviors of students in elearning platforms with machine learning models has become an emerging need in recent years.Increase in the number of registered students on e-learning platforms is one of the reasons for choosing machine learning models.Tracking, modeling and understanding student activities gets more complex when the number of students is increased.This study is focusing modeling student activities on e-learning platforms with Complex Event Processing (CEP), Association Rule Mining (ARM) and Clustering methods based on distributed software architecture.Within the scope of this study, different modules that work real-time have been developed.An admin panel has been also developed in order to control all modules and track the student actions.Performance results of modules also obtained and evaluated on distributed system architecture.

Source: Annals of Computer Science and Information Systems

DOI: 10.15439/2020f138

Authors: Sergen Cansiz, Berk Sudan, Emre Ogretici, Mehmet Siddik Aktas

Institution: Yıldız Technical University

Language: English

Topic: Online Learning and Analytics

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: diamond

OpenAlex ID: W3092586295