Framework for analyzing online asynchronous discussion by integrating content analysis and social network analysis

Online Asynchronous Discussion (OAD) is a powerful way to conduct online conversation and a significant component of online learning. Unfortunately, existing Learning Management System (LMS) that generally provides online discussion cannot afford a comprehensive evaluation on the content of the transcripts and the level of interaction among participants. Therefore, this research explores the analysis process of OAD qualitatively and quantitatively. The work focuses on Content Analysis (CA) and Social Network Analysis (SNA), two popular methods employed by educators and researchers to analyze online discussion in e-learning environment. Although these two methods are well established, the techniques remain manual. Furthermore, presently, these two methods of analysis are conducted and studied independently. Hence, this research proposes a new framework integrating CA with SNA called CASNA, which provides comprehensive information of the result, and automation of the processes. CASNA is applied and embedded in LMS (Moodle) to validate the proposed framework. This research also introduces sentence as the unit of interaction instead of message to assess the level of participation among students. In addition, in order to qualitatively analyze the online discussion, two text classifiers; the Support Vector Machine (SVM) and the Back-propagation Neural Network (BPNN) approaches are employed to categorize the sentences based on Soller’s model and the results are compared. The evaluation of these two classifiers is done based on precision, accuracy, recall and F-Measure. The result shows that SVM outperform BPNN in terms of precision and accuracy; falls behind BPNN in terms of recall and F-Measure. This research also discusses the use of network indicators of SNA. Adjacency matrix, graph theory and network analysis techniques are applied to quantitatively define the network interactions among participants. This framework takes advantage of the strength of each method and offers dynamic analysis of the textual messages. It is expected to be more informative to educators as well as researchers in measuring the quality and quantity of OAD

Visualisation of Interactions in Online Collaborative Learning Environments

Much research in recent years has focused on the introduction of ‘Virtual Learning Environments’ (VLE’s) to universities, documenting practice and sharing experience. Communicative tools are the means by which VLE’s have the potential to transform learning with computers from being passive and transmissive in nature, to being active and constructivist. Attention has been directed towards the importance of online dialogue as a defining feature of the VLE. However, practical methods of reviewing and analysing online communication to encode and trace cycles of real dialogue (and learning) have proved somewhat elusive. Qualitative methods are under-used for VLE discussions, since they demand new sets of research skills for those unfamiliar with those methods. Additionally, it can be time-intensive to learn them. This thesis aims to build an improved and simple-to-use analytical tool for Moodle that will aid and support teachers and administrators to understand and analyse interaction patterns and knowledge construction of the participants involved in ongoing online interactions. After reviewing the strengths and shortcomings of the existing visualisation models, a new visualisation tool called the Virtual Interaction Mapping System (VIMS) is proposed which is based on a framework proposed by Schrire (2004) to graphically represent social presence and manage the online communication patterns of the learners using Moodle. VIMS produces multiple possible views of interaction data so that it can be evaluated from many perspectives; it can be used to represent interaction data both qualitatively and quantitatively. The units of analysis can be represented graphically and numerically for more extensive evaluation. Specifically, these indicators are communication type, participative level, meaningful content of discussion, presence of lurkers, presence of moderators, and performance of participants individually and as a group. It thus enables assessment of the triangular relationship between conversationcontent, online participation and learnin

Parallelizing Maximal Clique Enumeration on GPUs

We present a GPU solution for exact maximal clique enumeration (MCE) that performs a search tree traversal following the Bron-Kerbosch algorithm. Prior works on parallelizing MCE on GPUs perform a breadth-first traversal of the tree, which has limited scalability because of the explosion in the number of tree nodes at deep levels. We propose to parallelize MCE on GPUs by performing depth-first traversal of independent subtrees in parallel. Since MCE suffers from high load imbalance and memory capacity requirements, we propose a worker list for dynamic load balancing, as well as partial induced subgraphs and a compact representation of excluded vertex sets to regulate memory consumption. Our evaluation shows that our GPU implementation on a single GPU outperforms the state-of-the-art parallel CPU implementation by a geometric mean of 4.9x (up to 16.7x), and scales efficiently to multiple GPUs. Our code has been open-sourced to enable further research on accelerating MCE

A novel bioinformatic approach for comprehensive genome scale analysis identifies key regulators of macrophage activation.

The initiation of inflammatory cytokine transcription by bacterial ligands is a central mechanism by which the immune system activates its first line of defense. Macrophage activation by the Toll-like Receptor 4 (TLR4) pathway is initiated with receptor binding of lipopolysaccharides (LPS) and culminates in a large-scale transcriptional response of the inflammatory gene program. Advancements in genome-wide screening technologies have made it possible to interrogate the regulatory landscape of signaling pathways such as those activated by TLR4. Utilizing these high-throughput methods for the comprehensive characterization of pathway components, particularly for regulators that are involved in critical cellular processes such as transcription and translation, however, requires an approach that goes beyond the top scoring and previously characterized hits of genome-scale studies. To address this challenge, I developed the Throughput Ranking by Iterative Analysis of Genomic Enrichment (TRIAGE) method, a bioinformatic analysis model that facilitates the comprehensive identification of likely regulators by iterative sampling of pathway and network databases. I validated the TRIAGE approach by analyzing three previously published genome-wide studies of regulators of early HIV infection and viral transcription. Analysis by TRIAGE showed significantly increased overlap and identified shared novel targets across the three studies. I further developed the TRIAGE analysis method as a globally accessible web-based resource. Applying TRIAGE analysis to three genome-scale studies of LPS treatment in macrophages of mouse and human cell lines, I identified an enrichment for regulators relating to alternative splicing and protein degradation. Using short read and long read RNA-seq of ligand-stimulated macrophages I further characterized the broad transcriptional variation induced by the LPS response and the novel and known transcript variants that define different macrophage activation states. These findings define an approach for comprehensive unbiased discovery of signaling pathway regulators from genome-scale datasets and suggest a model of macrophage activation involving proteasomal removal of negative regulators and remodeling of the macrophage state via a transcriptional shift in splice variant dynamics

Snoop-forge-replay attack on continuous verification with keystrokes

We present a new attack called the snoop-forge-replay attack on the keystroke-based continuous verification systems. We performed the attacks on two levels – 1) feature-level and 2) sample-level. (1) Feature-level attack targets specific keystroke-based continuous verification method or system. In feature-level attacks, we performed a series of experiments using keystroke data from 50 users who typed approximately 1200 to 2300 keystrokes of free text during three different periods. The experiments consisted of two parts. In the first part, we conducted zero-effort verification experiments with two verifiers ( R and S ) and obtained Equal Error Rates (EERs) between 10% and 15% under various verifier configurations. In the second part, we replayed 10,000 forged impostor attempts per user and demonstrated how the zero-effort impostor pass rates became meaningless when impostor attempts were created using stolen keystroke timing information. (2) Sample-level attack is not specific to any particular keystroke-based continuous verification method or system. It can be launched with easily available keyloggers and application programming interfaces (APIs) for keystroke synthesis. Our results from 2640 experiments show that (i) the snoop-forge-replay attacks achieve alarmingly high error rates compared to zero-effort impostor attacks, which have been the de facto standard for evaluating keystroke-based continuous verification systems; (ii) four state-of-the-art verification methods, three types of keystroke latencies, and eleven matching-pair settings (–a key parameter in continuous verification with keystrokes) that we examined in this dissertation were susceptible to the attack; (iii) the attack is effective even when as low as 20 to 100 keystrokes were snooped to create forgeries. In light of our results, we question the security offered by the current keystroke-based continuous verification systems. Additionally, in our experiments, we harnessed virtualization technology to generate thousands of keystroke forgeries within a short time span. We point out that virtualization setup such as the one used in our experiments can also be exploited by an attacker to scale and speed up the attack

EUSN 2021 Book of Abstracts, Fifth European Conference on Social Networks

Book of abstract of the fifth European conference on Social Networks EUSN 202

Analítica visual en eLearning

[ES] La docencia universitaria ha experimentado cambios espectaculares en los últimos años debido al impacto de la tecnología en diferentes actividades cotidianas. El eLearning (o aprendizaje electrónico) y el bLearning (Blended Learning o aprendizaje mixto) nacieron gracias a este fenómeno, ya fuera como alternativa o complemento a la enseñanza tradicional. Esto ha producido cambios de paradigma en los últimos años en la docencia universitaria donde muchos profesores se han apoyado o han sustituido las clases presenciales de la enseñanza tradicional, entendiendo la enseñanza tradicional como cara a cara (del inglés Face to Face, F2F) por el aula virtual. La Web 2.0 ha abierto nuevas posibilidades, que incluyen numerosas formas de aprendizaje y colaboración entre estudiantes y profesores. Por ello, ha surgido la necesidad, por parte de los educadores, de adoptar diferentes estrategias para obtener información sobre el rendimiento de sus estudiantes, así como plantear nuevas formas de evaluación basadas en el análisis de información educativa, que sean capaces de medir y cuantificar la cantidad de trabajo, así como el número y la calidad de las habilidades que han adquirido. Tales estrategias de comunicación requieren de nuevos métodos analíticos que hagan posible comprender y analizar la propia plataforma y el aprendizaje. Estas consideraciones arrojan nueva luz sobre la evaluación de los estudiantes, que ya no puede basarse únicamente en los resultados de los exámenes finales convencionales, sino en un proceso educativo integral que considere y evalúe otras competencias más allá de las académicas. La disponibilidad y facilidad de uso de los recursos web ha permitido el uso extendido de los Learning Management Systems, o plataformas de eLearning. Sin embargo, los educadores que usan estos entornos se encuentran con graves limitaciones a la hora de evaluar las actividades de los estudiantes, de discriminar sus comportamientos online y de evaluar la propia plataforma y la utilidad de esta. Por ello, es necesario encontrar y desarrollar técnicas novedosas para obtener información sobre las pautas de aprendizaje y comportamiento de los estudiantes en un entorno electrónico. En esta tesis doctoral se propone un modelo de visualización analítica en eLearning como base para construir una estrategia de seguimiento y evaluación de la información que proporciona, no solo a los profesores, sino también a gestores académicos y estudiantes, información necesaria para entender el proceso de aprendizaje de los estudiantes en una plataforma de eLearning, que sirva de guía para el alumnado y que proporcione métricas para los gestores sobre la plataforma y el desempeño, además de tomarse como base para desarrollo de futuros sistemas de analítica visual en eLearning. El modelo proporciona los elementos para crear un sistema de analítica visual en eLearning encaminado a perfeccionar el proceso de enseñanza/aprendizaje. Este sistema se ha diseñado mediante una arquitectura constituida por distintas capas. La capa inferior está sustentada en un conjunto de servicios web que permiten la extracción de los datos a analizar del servidor. La siguiente capa, contiene la lógica de pre-procesamiento,estandarización y análisis de los datos. Por último, una tercera capa, en la que se realiza el proceso de analítica visual, que permite al profesor, estudiante o gestor académico llevar a cabo un análisis más exhaustivo, completo e interactivo. Con la finalidad de poner en práctica y realizar una prueba de los alcances de este sistema, se ha desarrollado un prototipo plenamente funcional del mismo. El desarrollo del prototipo se realizó por medio de un conjunto de iteraciones de investigación-acción para la mejora del alcance de las capacidades de análisis del sistema y de la usabilidad del prototipo de visualización analítica visual en eLearning, con el objetivo último de soportar el proceso de aprendizaje, el rendimiento académico y, a su vez, y como ya se mencionó, estas aplicaciones al tomarse como base para desarrollo de futuros sistemas de analítica visual en eLearning. Se utiliza como fuente de datos el sistema de gestión de aprendizaje Moodle. Los resultados obtenidos se complementaron y probaron con un estudio de los patrones de uso de las plataformas de eLearning en dos universidades con distintos contextos pedagógicos y sociales: la Universidad Politécnica de Madrid y la Universidad de Salamanca. Esta valiosa experiencia produjo un caudal de nueva información y conocimiento y, por tanto, una importante fuente de realimentación que han contribuido a la mejora notable de las capacidades de análisis que ofrece la plataforma y cubre adecuadamente las necesidades y funcionalidades que se requieren en el modelo propuesto y descrito en esta Tesis Doctoral

Old Meets New: Media in Education – Proceedings of the 61st International Council for Educational Media and the XIII International Symposium on Computers in Education (ICEM&SIIE'2011) Joint Conference

A conferência ICEM&SIIE'2011 foi organizada pela Universidade de Aveiro (Portugal) – membro do European Consortium of Innovative Universities – e pretendeu reunir investigadores, professores e outros profissionais, a nível nacional e internacional, em torno de um tema aglutinador que pretendeu despoletar e colocar a tónica da discussão na dualidade ―old/new‖, ou seja, os participantes foram convidados a discutir: - os media na educação em ambas as perspetivas, mais tradicionais ou modernas, com incidência numas ou noutras ou, ainda, numa perspetiva comparativa; - a conjugação, adaptação e adoção dos media consoante os contextos e objetivos de utilização; - o que os media implicam em termos de tecnologia, barreiras profissionais e /ou sociais; - a relação custo-benefício da utilização dos media em contexto de aprendizagem; - os media em função dos diversos contextos educativos e dos perfis de aprendizagem dos alunos. Para a conferência foram selecionados 76 artigos organizados em 15 sessões paralelas, 13 posters e 9 workshops. A conferência caracterizou-se pelo caráter internacional dos contributos, reunindo 38 artigos em português, 32 em língua inglesa e 6 em espanhol. Estas atas encontram-se organizadas de acordo com o programa da conferência. Em primeiro lugar incluem-se os artigos (full paper e short paper) por sessão, seguem-se os posters e, finalmente, o resumo relativo aos workshops.The ICEM&SIIE'2011 conference was organised by the University of Aveiro (Portugal) – a member of the European Consortium of Innovative Universities – and aimed at gathering researchers, teachers and other professionals, at national and international level, around a focal topic that might trigger and centre the discussion on the ―old/new‖ duality of media in education. Participants were invited to discuss: - old and new media in education, in isolation or comparatively; - how old and new media in education can be combined, adopted and adapted; - what old and new media in education imply in terms of technological, professional and social barriers; - what cost-benefit relationships old and new media in education entail; - how to compare old and new media in education given their particular educational contexts and the students' learning profiles. 76 papers were selected and organised in 15 paralel sessions, 13 posters and 9 workshops. The conference is characterized by the international character of contributions, gathering 38 papers in Portuguese, 32 in English and 6 in Spanish. These procedings are organised according to the programme of the conference. First we find the full and short papers, per session, then posters and finally the abstracts for the workshops