Paper Info
Title
A complex event processing framework for an adaptive language learning system.
Abstract
Ubiquitous learning applications and worldwide educational websites such as MOOC (Massive Open Online Courses) are rapidly producing large volume of user data. Current delayed analysis processing in adaptive language learning systems is difficult to cope with the high-speed and high-volume data streams. To overcome this problem, we introduce a complex event processing (CEP) framework for an Adaptive Language Learning System. The system consists of an event adapter sub-system that can process various inputs such as voice, video, text and other interaction events. The event adapter extracts relevant data to support the operational events module, the learning activity events module and the learner knowledge space events module. These three modules in the event hierarchies provide support to the learner adaptation and learner visual analytics modules. In this study, we conduct three simulations to evaluate the initialization time, delay time and throughput of the proposed system. Each of the experiments simulates 1000 learners and 1000 rules and generates 10 events per second. The results indicate the CEP framework is efficient with a processing delay of less than 1.2μs and throughput of 80,000 events per second. We conclude by discussing the study’s implications and suggest ideas for future research.
Year
DOI
Venue
2019
10.1016/j.future.2017.12.032
Future Generation Computer Systems
Keywords
Field
DocType
Big data,Adaptive educational system,Language learning,Complex event processing,Stream processing
Data stream mining,Computer science,Complex event processing,Visual analytics,Adapter (computing),Human–computer interaction,Initialization,Throughput,Knowledge space,Distributed computing,Processing delay
Journal
Volume
ISSN
Citations 
92
0167-739X
1
PageRank 
References 
Authors
0.35
25
5
Name
Order
Citations
PageRank
Dawei Jin161.99
Si Shi2214.26
Yin Zhang3699.92
Haider Abbas439143.88
Tiong-thye Goh5557.21