Viewing mobile learning from a pedagogical perspective

Mobile learning is a relatively new phenomenon and the theoretical basis is currently under development. The paper presents a pedagogical perspective of mobile learning which highlights three central features of mobile learning: authenticity, collaboration and personalisation, embedded in the unique timespace contexts of mobile learning. A pedagogical framework was developed and tested through activities in two mobile learning projects located in teacher education communities: Mobagogy, a project in which faculty staff in an Australian university developed understanding of mobile learning; and The Bird in the Hand Project, which explored the use of smartphones by student teachers and their mentors in the United Kingdom. The framework is used to critique the pedagogy in a selection of reported mobile learning scenarios, enabling an assessment of mobile activities and pedagogical approaches, and consideration of their contributions to learning from a socio-cultural perspective

The entangled cyberspace: an integrated approach for predicting cyber-attacks

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonSignificant studies in cyber defence analysis have predominantly revolved around a single linear analysis of information from a single source of evidence (The Network). These studies were limited in their ability to understand the dynamics of entanglements related to cyber-incidents. This research integrates evidence beyond the network in an attempt to understand and predict phases of the kill-chain across the information space. This research provides a multi-dimensional phased analysis of the traditional kill-chain model using structural vector autoregressive models. In the ‘Entangled Cyberspace Framework’, each phase of the kill-chain corresponds to a single dimension of the information space based on time observations of certain events. Events are represented as time signals, where each phase is characterised by multiple time signals representing multiple events on that phase. Multiple time signals are analysed using structural models for multiple time series analysis (Vector Auto-Regressive models). At each phase of the kill-chain, we perform a lagged co-integration analysis of events across the information space. This nature of analysis detects hidden entanglements that characterise events in the kill-chain beyond the network. The measured prediction accuracy and error measured at each stage of the experiment represents the usefulness of selected events in characterising the defined stage of the kill-chain. The entangled cyberspace, in theory, is the fusion of three conceptual foundations: a) A multi-dimensional characterisation of cyberspace, b) A sequential phased model for perpetrating cyber-attacks and c) A structural model for integrating and simultaneously analysing multiple sources of evidence. It starts with the characterisation of the information space into different dimensions of interest. The framework goes further to identify evidence sources across these characterised dimensions and integrates them in the analytical context under consideration (e.g. Malware Injection). The concrete findings show that our approach and analytical methodology are capable of detecting entanglements when applied to a set of entangled activities across the information space. The findings also prove that activities beyond the network have significant effects on the nature of the unfolding cyber-attack vector. The predictive features of events across the kill-chain were also presented in this research as opinion and emotion drivers on the social dimension, packet data details and social and cultural events on the economic layer. Finally, co-integration detected between events across and within dimensions of the information space proves the existence of both inter-dimensional and intra-dimensional entanglements that affect the nature of events unfolding during the kill-chain (from the adversary’s point of view). The novelty of this research rests in the ability to hop across the information space for detecting evidential clues of activities that are related-to cyber-incidents. This research also expands the standard multi-dimensional information space to include SPEC factors as indicators of cyber-incidents. This research improves the current information security management model, specifically in the monitoring, analysis and detection phases. This research provides a methodology that accommodates a robust evidence base for understanding the attack surface. Practically, this research provides a basis for creating applications and tools for protecting critical national infrastructure by integrating data from social platforms, real-world political, cultural and economic events and the cyber-physical

Predictive Analysis on Twitter: Techniques and Applications

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories

The Role of Social Media in Developing Online Learning Communities

The purpose of this study was to examine the role of social media in develop-ing learning communities in both formal and informal learning contexts. The study was based on a theoretical framework to examine online learning communities from three levels: individual, interactional and group. This study selected two cases: the first case was a formal learning group that used networked learning via Twitter and WhatsApp within a blended learning environment in an academic module; this for-mal learning group was controlled by the teacher of the module. The second case was an informal learning group that used Twitter and WhatsApp to learn and prac-tise English as a second language; this group was created and informally organised by an active member on Twitter who was interested in teaching and practising Eng-lish. Semi-structured interviews, focus groups and WhatsApp discussion samples were the three main data collection methods of this study. The data were analysed using three procedures. Firstly, a thematic analysis of the interviews was conducted to generate a thematic research map and create a coding scheme for analysing the content of the WhatsApp discussions. Secondly, a social network analysis (SNA) was applied to the WhatsApp group discussions to map out the interactions among group members and select the sample of WhatsApp discussion for the third data analysis procedure. The third procedure was content analysis (CA), which was ap-plied to the WhatsApp conversations that occurred during the selected sample (the three most active and connected weeks). Findings from the SNA and CA were used to triangulate the results of the thematic analysis. The findings revealed that the ex-istence of similar learning needs, interactive communication among members and using appropriate communication tools are the main factors that develop online learning communities on social media. Also, it showed that the main function of us-ing Twitter for learning purposes was to develop the academic and social presence of the students/learners, while the main learning function of using WhatsApp was to provide an instant and open communication environment for online learning com-munity members. However, there were different uses of these applications in formal and informal learning contexts, which were described in the study

MASELTOV Deliverable Report 7.1.2: Incidental Learning Framework

This document describes progress in the development of an ‘Incidental Learning Framework’, building on the work reported in Deliverable D7.1.1, submitted July 2012. The goal of the Incidental Learning Framework (ILF) is to facilitate the creation of technology rich learning opportunities for immigrants within cities. The framework is a descriptive mechanism that permits analysis, and a generative tool to support software system design, and it facilitates the communication of learning design ideas both visually and textually. The framework focuses on incidental learning i.e. learning that is spontaneous and unplanned, in the knowledge domains of interest to the MASELTOV project including health care, culture, and language and information access. Its use should encourage links and triggers to structured and reflective learning to back up and deepen learning that happens incidentally. This document describes the Incidental Learning Framework developed for the MASELTOV project, presents a examples of its use, and describes some conclusions and recommendations for future work - Introduction - Purpose of the framework - Challenges with ILF from its initial conception - Work carried out developing ILF for use in the project --Literature --Alternative visualisations --Focus workshops in OU --Partner testing --Template for testing --Examples of the partners’ testing their tools against the template -Reporting on incidental learning reflections with language learning and serious games - Conclusions and recommendations It should be noted that this document is a high level review, identifying significant literature and the on-going development of the framework through dialogue with educational experts and MASELTOV partners. This document offers recommendations therefore in general terms. Decisions about the specific implementation of the learner’s journey as framed by an incidental learning approach will be made in coordination with technical partners as the dialogue progresses