Nirandika Wanigasekara

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This paper presents affect data collected from periodic emotion detection surveys throughout an introductory Statistics MOOC called "I Heart Stats." This is the first MOOC, to our knowledge, to capture valuable student affect data through self-reported surveys. To collect student affect, we used two self-reporting methods: (1) The Self-Assessment Manikin(More)
The number of connected devices and services available across the Internet of Things (IoT) is rapidly expanding. In this paper, we present a novel mechanism that improves the ability of mobile clients to dynamically discover potentially unknown IoT services across the IoT's increasingly fragmented protocol landscape. Our approach leverages the detected(More)
This paper discusses self-reported emotions experienced by students in a Massive Open Online Course (MOOC) learning context. Emotions have been previously shown to be related to learning in classrooms and laboratory studies and have even been leveraged to improve learning. In this study, frequently occurring discrete emotions as well as frequently,(More)
Smart devices with incompatible protocols and APIs are increasing in everyday environments. Adaptive middleware techniques have enabled smart phones to become smart gateways between these incompatible devices by providing protocol translation services at runtime. Thus, mobile apps can easily use remote services to execute the app logic, which typically(More)
Most virtualization environments demand users to download complete disk images to create virtual machines. However, since system data hardly varies from user to user there is a possibility to have common data blocks amongst the disk images and the hosting environment. Our research evaluates whether deduplication of these common data blocks can rectify(More)
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