Title:	EDUCATIONAL DATA COLLECTION FRAMEWORK BASED ON HYBRID-SENSORS-NETWORK
Author(s):	Gislaine Cristina Micheloti Rosales, Fernando Vieira Duarte, Regina Borges de Araújo and Joice Lee Otsuka
ISBN:	978-989-8533-16-6
Editors:	Bebo White and Pedro Isaías
Year:	2013
Edition:	Single
Keywords:	Logical and physical sensors, framework architecture, educational data collection, acquisition of user’s behaviors.
Type:	Full Paper
First Page:	157
Last Page:	164
Language:	English
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Full Contents:	click to dowload
Paper Abstract:	The increasing use of information and communication technology in teaching and learning process allows users to work in a heterogeneous environment which involves more than just the Learning Management Systems (LMS). Resources such as multimedia players, instant messaging, games, web pages, emails and file sharing programs are often used during learning. Capturing context is the first step to support users during their teaching and learning process. In this paper we present an educational data collection framework based on hybrid sensors networks – composed by physical and logical sensors – that can implicitly collect events of users’ focus of attention and forward a rich data set to interested applications. It facilitates the building context-aware e-learning applications and offers a simple and unified way to the acquisition of user’s behaviors in online learning. Using our framework, it is possible not only to observe the user’s behaviors conventionally monitored in web browsers, or LMS or others databases, but also track user-generated content including their personal digital environment - local desktop application programs, operational system, communication network, etc - and the physical environment surrounding them. The framework implementation allows the easy integration of additional sensors and actuators and makes it domain and platform-independent. To validate our framework, we developed and built three applications: ViTrackeR, that assists students’ self-regulated learning promotion providing tracking data visualization and personalized recommendations; ViMonitor, a tool particularly dedicated to assist in real time both teaching staff and academic management staff providing important pieces of information about students and tutors; and ViAssess, that provides accurate support to teachers in online assessment security issues. This paper focus on how our framework conducts a decentralized data collection from sensors networks abstraction. Further, we show the tools: ViTrackeR, ViMonitor and ViAssess in the context of a concrete online course.
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