Distributed-based massive processing of activity logs for efficient user modeling in a Virtual Campus

This paper reports on a multi-fold approach for the building of user models based on the identification of navigation patterns in a virtual campus, allowing for adapting the campus’ usability to the actual learners’ needs, thus resulting in a great stimulation of the learning experience. However, user modeling in this context implies a constant processing and analysis of user interaction data during long-term learning activities, which produces huge amounts of valuable data stored typically in server log files. Due to the large or very large size of log files generated daily, the massive processing is a foremost step in extracting useful information. To this end, this work studies, first, the viability of processing large log data files of a real Virtual Campus using different distributed infrastructures. More precisely, we study the time performance of massive processing of daily log files implemented following the master-slave paradigm and evaluated using Cluster Computing and PlanetLab platforms. The study reveals the complexity and challenges of massive processing in the big data era, such as the need to carefully tune the log file processing in terms of chunk log data size to be processed at slave nodes as well as the bottleneck in processing in truly geographically distributed infrastructures due to the overhead caused by the communication time among the master and slave nodes. Then, an application of the massive processing approach resulting in log data processed and stored in a well-structured format is presented. We show how to extract knowledge from the log data analysis by using the WEKA framework for data mining purposes showing its usefulness to effectively build user models in terms of identifying interesting navigation patters of on-line learners. The study is motivated and conducted in the context of the actual data logs of the Virtual Campus of the Open University of Catalonia.Peer ReviewedPostprint (author's final draft

Using bi-clustering algorithm for analyzing online users activity in a virtual campus

Data mining algorithms have been proved to be useful for the processing of large data sets in order to extract relevant information and knowledge. Such algorithms are also important for analyzing data collected from the users' activity users. One family of such data analysis is that of mining of log files of online applications that register the actions of online users during long periods of time. A relevant objective in this case is to study the behavior of online users and feedback the design processes of online applications to provide better usability and adaption to users' preferences. The context of this work is that of a virtual campus in which thousands of students and tutors carry out the learning and teaching activity using online applications. The information stored in log files of virtual campuses tend to be large, complex and heterogeneous in nature. Hence, their mining requires both efficient and intelligent processing and analysis of user interaction data during long-term learning activities. In this paper, we present a bi-clustering algorithm for processing large log data sets from the online daily activity of students in a real virtual campus. Our approach is useful to extract relevant knowledge about user activity such as navigation patterns, activities performed as well as to study time parameters related to such activities. The extracted information can be useful not only to students and tutors to stimulate and improve their experience when interacting with the system but also to the designers and developers of the virtual campus in order to better support the online teaching and learning.Peer ReviewedPostprint (published version

Security in online learning assessment towards an effective trustworthiness approach to support e-learning teams

(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.This paper proposes a trustworthiness model for the design of secure learning assessment in on-line collaborative learning groups. Although computer supported collaborative learning has been widely adopted in many educational institutions over the last decade, there exist still drawbacks which limit their potential in collaborative learning activities. Among these limitations, we investigate information security requirements in on-line assessment, (e-assessment), which can be developed in collaborative learning contexts. Despite information security enhancements have been developed in recent years, to the best of our knowledge, integrated and holistic security models have not been completely carried out yet. Even when security advanced methodologies and technologies are deployed in Learning Management Systems, too many types of vulnerabilities still remain opened and unsolved. Therefore, new models such as trustworthiness approaches can overcome these lacks and support e-assessment requirements for e-Learning. To this end, a trustworthiness model is designed in order to conduct the guidelines of a holistic security model for on-line collaborative learning through effective trustworthiness approaches. In addition, since users' trustworthiness analysis involves large amounts of ill-structured data, a parallel processing paradigm is proposed to build relevant information modeling trustworthiness levels for e-Learning.Peer ReviewedPostprint (author's final draft

Scalability, memory issues and challenges in mining large data sets

(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Data mining is an active field of research and development aiming to automatically extract "knowledge" from analyzing data sets. Knowledge can be defined in different ways such as discovering (structured, frequent, approximate, etc.) patterns in data, grouping/clustering/bi-clustering data according to one or more criteria, finding association rules, etc. Such knowledge is then fed-back to decision support systems enabling end-users (actors) to make more informed decisions, which in economic terms could lead to advantages as compared to traditional decision support systems. It should be noted however, that data mining algorithms and frameworks have been proposed prior to the "Big Data" explosion. While data mining algorithms have considered efficiency and computational complexity as an important requirement, they did not take into account features of Big Data such as very large size, velocity with which data is generated, variety, etc. On the other hand, these features are indeed posing issues and challenges to data mining algorithms and frameworks. In this paper we analyse some of the issues in mining large data sets such as scalability and in-memory needs. We also show some computational results pointing out to such issues.Peer ReviewedPostprint (author's final draft

Using a grid platform for enabling real time user modeling in on-line campus

User modelling in on-line distance learning is an important research field focusing on two important aspects: describing and predicting students' actions and intentions as well as adapting the learning process to students' features, habits, preferences, and so on. The aim is to greatly stimulate and improve the learning experience. Indeed, on the one hand, students' intentions may change during the realization of learning activities and thus their actions evolve accordingly as the learning process moves forward. On the other hand, adaptive systems can effectively plan and design appropriate learning tasks according to students' features, habits and interests with the aim of facilitating the achievement of the learning goal. In this context, user modelling implies a continuous processing and analysis of user interaction data during long-term learning activities, which produces large and considerably complex information. As a consequence, processing this information is costly and could require computational capacity beyond that of a single computer. In this paper, we show how a grid approach can considerably decrease the processing time of log data of on-line distance educational Web-based systems. Our prototype is based on the master-worker paradigm and is implemented using a peer-to-peer platform running on the Planetlab nodes. The results of our study show the feasibility of using grid middleware to speed and scale up the processing of log data and thus achieve an efficient and dynamic user modeling in on-line distance learning.Peer ReviewedPostprint (published version

An information security model based on trustworthiness for enhancing security in on-line collaborative learning

L'objectiu principal d'aquesta tesi és incorporar propietats i serveis de la seguretat en sistemes d'informació en l'aprenentatge col·laboratiu en línia, seguint un model funcional basat en la valoració i predicció de la confiança. Aquesta tesi estableix com a punt de partença el disseny d'una solució de seguretat innovadora, basada en una metodologia pròpia per a oferir als dissenyadors i gestors de l'e-learning les línies mestres per a incorporar mesures de seguretat en l'aprenentatge col·laboratiu en línia. Aquestes guies cobreixen tots els aspectes sobre el disseny i la gestió que s'han de considerar en els processos relatius a l'e-learning, entre altres l'anàlisi de seguretat, el disseny d'activitats d'aprenentatge, la detecció d'accions anòmales o el processament de dades sobre confiança. La temàtica d'aquesta tesi té una naturalesa multidisciplinària i, al seu torn, les diferents disciplines que la formen estan íntimament relacionades. Les principals disciplines de què es tracta en aquesta tesi són l'aprenentatge col·laboratiu en línia, la seguretat en sistemes d'informació, els entorns virtuals d'aprenentatge (EVA) i la valoració i predicció de la confiança. Tenint en compte aquest àmbit d'aplicació, el problema de garantir la seguretat en els processos d'aprenentatge col·laboratiu en línia es resol amb un model híbrid construït sobre la base de solucions funcionals i tecnològiques, concretament modelatge de la confiança i solucions tecnològiques per a la seguretat en sistemes d'informació.El principal objetivo de esta tesis es incorporar propiedades y servicios de la seguridad en sistemas de información en el aprendizaje colaborativo en línea, siguiendo un modelo funcional basado en la valoración y predicción de la confianza. Esta tesis establece como punto de partida el diseño de una solución de seguridad innovadora, basada en una metodología propia para ofrecer a los diseñadores y gestores del e-learning las líneas maestras para incorporar medidas de seguridad en el aprendizaje colaborativo en línea. Estas guías cubren todos los aspectos sobre el diseño y la gestión que hay que considerar en los procesos relativos al e-learning, entre otros el análisis de la seguridad, el diseño de actividades de aprendizaje, la detección de acciones anómalas o el procesamiento de datos sobre confianza. La temática de esta tesis tiene una naturaleza multidisciplinar y, a su vez, las diferentes disciplinas que la forman están íntimamente relacionadas. Las principales disciplinas tratadas en esta tesis son el aprendizaje colaborativo en línea, la seguridad en sistemas de información, los entornos virtuales de aprendizaje (EVA) y la valoración y predicción de la confianza. Teniendo en cuenta este ámbito de aplicación, el problema de garantizar la seguridad en los procesos de aprendizaje colaborativo en línea se resuelve con un modelo híbrido construido en base a soluciones funcionales y tecnológicas, concretamente modelado de la confianza y soluciones tecnológicas para la seguridad en sistemas de información.This thesis' main goal is to incorporate information security properties and services into online collaborative learning using a functional approach based on trustworthiness assessment and prediction. As a result, this thesis aims to design an innovative security solution, based on methodological approaches, to provide e-learning designers and managers with guidelines for incorporating security into online collaborative learning. These guidelines include all processes involved in e-learning design and management, such as security analysis, learning activity design, detection of anomalous actions, trustworthiness data processing, and so on. The subject of this research is multidisciplinary in nature, with the different disciplines comprising it being closely related. The most significant ones are online collaborative learning, information security, learning management systems (LMS), and trustworthiness assessment and prediction models. Against this backdrop, the problem of securing collaborative online learning activities is tackled by a hybrid model based on functional and technological solutions, namely, trustworthiness modelling and information security technologies

Conceptual Design and Implementation of a Cloud Computing Platform Paradigm

In recent times, organizations all over the world have stopped expanding infrastructures and building competencies in IT for enhanced efficiencies. Rather, they focus on their primary lines of businesses and “simply” connect to an existing IT cloud in the neighborhood or on the internet for their IT demands. Cloud computing is a new paradigm of large-scale distributed computing that centralizes the data and computation on the virtual “super computer” with unprecedented storage and computing capabilities. This paper focuses on the design of a conceptual framework and implementation of a cloud computing platform. This study attempts to design a platform on which users can plug-in anytime from anywhere and utilize enormous computing resources at a relatively low cost. Alongside the design, the mathematical model structures that support the design of the framework are explicitly described. The study is of paramount importance because the new framework provides opportunity to avoid network congestions that degrade performance among other shortcomings being experienced in some implementation cases. Keywords: Cloud Computing, Framework, Platform, Paradig

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

Big Data Reference Architecture for e-Learning Analytical Systems

The recent advancements in technology have produced big data and become the necessity for researcher to analyze the data in order to make it meaningful. Massive amounts of data are collected across social media sites, mobile communications, business environments and institutions. In order to efficiently analyze this large quantity of raw data, the concept of big data was introduced. In this regard, big data analytic is needed in order to provide techniques to analyze the data. This new concept is expected to help education in the near future, by changing the way we approach the e-Learning process, by encouraging the interaction between learners and teachers, by allowing the fulfilment of the individual requirements and goals of learners. The learning environment generates massive knowledge by means of the various services provided in massive open online courses. Such knowledge is produced via learning actor interactions. Also, data analytics can be a valuable tool to help e-Learning organizations deliver better services to the public. It can provide important insights into consumer behavior and better predict demand for goods and services, thereby allowing for better resource management. This result motivates to put forward solutions for big data usage to the educational field. This research article unfolds a big data reference architecture for e-Learning analytical systems to make a unified analysis of the massive data generated by learning actors. This reference architecture makes the process of the massive data produced in big data e-learning system. Finally, the BiDRA for e-Learning analytical systems was evaluated based on the quality of maintainability, modularity, reusability, performance, and scalability