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A complex event processing framework for an adaptive language learning system
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DOI：10.1016/j.future.2017.12.032.png)
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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 1.2 mu s and throughput of 80,000 events per second. We conclude by discussing the study's implications and suggest ideas for future research. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Big data
Adaptive educational system
Language learning
Complex event processing
Stream processing
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