Playing for Real: Designing Alternate Reality Games in Learning Contexts

Alternate Reality Game (ARG) represent a new genre of transmedia practice where players hunt for scattered clues, make sense of disparate information, and solve puzzles to advance an ever-evolving storyline. Players participate in ARGs using multiple communications technologies, ranging from print materials to mobile devices. However, many interaction design challenges must be addressed to weave these everyday communication tools together into an immersive, participatory experience. Transmedia design is not an everyday process. Designers must create and connect story bits across multiple media (video, audio, text) and multiple platforms (phones, computers, physical spaces). Furthermore, they must engage with players of varying skill levels. Few studies to-date have explored the design process of ARGs in learning contexts. Fewer still have focused on challenges involved in designing for youth (13-17 years old). In this study, I explore the process of designing ARGs as vehicles for promoting information literacy and participatory culture for adolescents (13-17 years old). Two ARG design scenarios, distinguished by target learning environment (formal and informal context) and target audience (adolescents), comprise the two cases that I examine. Through my analysis of these two design cases, I articulate several unique challenges faced by designers who create interactive, transmedia stories for – and with – youth. Drawing from these design challenges, I derive a repertoire of design strategies that future designers and researchers may use to create and implement ARGs for teens in learning contexts. In particular, I propose a narrative design framework that allows for the categorization of ARGs as storytelling constructs that lie along a continuum of participation and interaction. The framework can serve as an analytic tool for researchers and a guide for designers. In addition, I establish a framework of social roles that designers may employ to craft transmedia narratives before live launch and to promote and scaffold player participation after play begins. Overall, the contributions of my study include theoretical insights that may advance our understanding of narrative design and analysis as well as more practical design implications for designers and practitioners seeking to incorporate transmedia features into learning experiences that target youth.

VirRAD: A New Paradigm for Technology Enhanced Learning

In most e-learning scenarios, communication and on-line collaboration is seen as an add-on feature to resource based learning. This paper will endeavour to present a pedagogical framework for inverting this view and putting communities of practice as the basic paradigm for e-learning. It will present an approach currently being used in the development of a virtual Radiopharmacy community, called VirRAD, and will discuss how theory can lead to an instructional design approach to support technologically enhanced learning.(DIPF/Orig.)

Spectral Frame Analysis and Learning through Graph Structure

This dissertation investigates the connection between spectral analysis and frame theory. When considering the spectral properties of a frame, we present a few novel results relating to the spectral decomposition. We first show that scalable frames have the property that the inner product of the scaling coefficients and the eigenvectors must equal the inverse eigenvalues. From this, we prove a similar result when an approximate scaling is obtained. We then focus on the optimization problems inherent to the scalable frames by first showing that there is an equivalence between scaling a frame and optimization problems with a non-restrictive objective function. Various objective functions are considered, and an analysis of the solution type is presented. For linear objectives, we can encourage sparse scalings, and with barrier objective functions, we force dense solutions. We further consider frames in high dimensions, and derive various solution techniques. From here, we restrict ourselves to various frame classes, to add more specificity to the results. Using frames generated from distributions allows for the placement of probabilistic bounds on scalability. For discrete distributions (Bernoulli and Rademacher), we bound the probability of encountering an ONB, and for continuous symmetric distributions (Uniform and Gaussian), we show that symmetry is retained in the transformed domain. We also prove several hyperplane-separation results. With the theory developed, we discuss graph applications of the scalability framework. We make a connection with graph conditioning, and show the in-feasibility of the problem in the general case. After a modification, we show that any complete graph can be conditioned. We then present a modification of standard PCA (robust PCA) developed by Cand\`es, and give some background into Electron Energy-Loss Spectroscopy (EELS). We design a novel scheme for the processing of EELS through robust PCA and least-squares regression, and test this scheme on biological samples. Finally, we take the idea of robust PCA and apply the technique of kernel PCA to perform robust manifold learning. We derive the problem and present an algorithm for its solution. There is also discussion of the differences with RPCA that make theoretical guarantees difficult.

A cyber exercise post assessment framework: In Malaysia perspectives

Critical infrastructures are based on complex systems that provide vital services to the nation. The complexities of the interconnected networks, each managed by individual organisations, if not properly secured, could offer vulnerabilities that threaten other organisations’ systems that depend on their services. This thesis argues that the awareness of interdependencies among critical sectors needs to be increased. Managing and securing critical infrastructure is not isolated responsibility of a government or an individual organisation. There is a need for a strong collaboration among critical service providers of public and private organisations in protecting critical information infrastructure. Cyber exercises have been incorporated in national cyber security strategies as part of critical information infrastructure protection. However, organising a cyber exercise involved multi sectors is challenging due to the diversity of participants’ background, working environments and incidents response policies. How well the lessons learned from the cyber exercise and how it can be transferred to the participating organisations is still a looming question. In order to understand the implications of cyber exercises on what participants have learnt and how it benefits participants’ organisation, a Cyber Exercise Post Assessment (CEPA) framework was proposed in this research. The CEPA framework consists of two parts. The first part aims to investigate the lessons learnt by participants from a cyber exercise using the four levels of the Kirkpatrick Training Model to identify their perceptions on reaction, learning, behaviour and results of the exercise. The second part investigates the Organisation Cyber Resilience (OCR) of participating sectors. The framework was used to study the impact of the cyber exercise called X Maya in Malaysia. Data collected through interviews with X Maya 5 participants were coded and categorised based on four levels according to the Kirkpatrick Training Model, while online surveys distributed to ten Critical National Information Infrastructure (CNII) sectors participated in the exercise. The survey used the C-Suite Executive Checklist developed by World Economic Forum in 2012. To ensure the suitability of the tool used to investigate the OCR, a reliability test conducted on the survey items showed high internal consistency results. Finally, individual OCR scores were used to develop the OCR Maturity Model to provide the organisation cyber resilience perspectives of the ten CNII sectors.

Independence or Protection - does it have to be a choice? Reflections on the abuse people with learning disabilities in Cornwall

This commentary will use recent events in Cornwall to highlight the ongoing abuse of adults with learning disabilities in England. It will critically explore how two parallel policy agendas – namely, the promotion of choice and independence for adults with learning disabilities and the development of adult protection policies – have failed to connect, thus allowing abuse to continue to flourish. It will be argued that the abuse of people with learning disabilities can only be minimised by policies which reflect an understanding that choice and independence must necessarily be mediated by effective adult protection measures. Such protection needs to include not only an appropriate regulatory framework, access to justice and well-qualified staff, but also a more critical and reflective approach to the current orthodoxy which promotes choice and independence as the only acceptable goals for any person with a learning disability.

Learning Binary Code Representations for Effective and Efficient Image Retrieval

The size of online image datasets is constantly increasing. Considering an image dataset with millions of images, image retrieval becomes a seemingly intractable problem for exhaustive similarity search algorithms. Hashing methods, which encodes high-dimensional descriptors into compact binary strings, have become very popular because of their high efficiency in search and storage capacity. In the first part, we propose a multimodal retrieval method based on latent feature models. The procedure consists of a nonparametric Bayesian framework for learning underlying semantically meaningful abstract features in a multimodal dataset, a probabilistic retrieval model that allows cross-modal queries and an extension model for relevance feedback. In the second part, we focus on supervised hashing with kernels. We describe a flexible hashing procedure that treats binary codes and pairwise semantic similarity as latent and observed variables, respectively, in a probabilistic model based on Gaussian processes for binary classification. We present a scalable inference algorithm with the sparse pseudo-input Gaussian process (SPGP) model and distributed computing. In the last part, we define an incremental hashing strategy for dynamic databases where new images are added to the databases frequently. The method is based on a two-stage classification framework using binary and multi-class SVMs. The proposed method also enforces balance in binary codes by an imbalance penalty to obtain higher quality binary codes. We learn hash functions by an efficient algorithm where the NP-hard problem of finding optimal binary codes is solved via cyclic coordinate descent and SVMs are trained in a parallelized incremental manner. For modifications like adding images from an unseen class, we propose an incremental procedure for effective and efficient updates to the previous hash functions. Experiments on three large-scale image datasets demonstrate that the incremental strategy is capable of efficiently updating hash functions to the same retrieval performance as hashing from scratch.

Assessing the Role of General Chemistry Learning in Higher Education

The inclusion of General Chemistry (GC) in the curricula of higher education courses in science and technology aims, on the one hand, to develop students' skills necessary for further studies and, on the other hand, to respond to the need of endowing future professionals of knowledge to analyze and solve multidisciplinary problems in a sustainable way. The participation of students in the evaluation of the role played by the GC in their training is crucial, and the analysis of the results can be an essential tool to increase success in the education of students and improving practices in various professions. Undeniably, this work will be focused on the development of an intelligent system to assess the role of GC. The computational framework is built on top of a Logic Programming approach to Knowledge Representation and Reasoning, complemented with a problem solving methodology moored on Artificial Neural Networks. The results so far obtained show that the proposed model stands for a good start, being its overall accuracy higher than 95%.

A PHENOMENOLOGY OF MIMETIC LEARNING AND MULTIMODAL COGNITION: INTEGRATING EXPERIENTIAL KNOWLEDGE INTO PROGRAMS IN RHETORIC, COMPOSITION, AND TECHNICAL COMMUNICATION

My dissertation emphasizes a cognitive account of multimodality that explicitly integrates experiential knowledge work into the rhetorical pedagogy that informs so many composition and technical communication programs. In these disciplines, multimodality is widely conceived in terms of what Gunther Kress calls “socialsemiotic” modes of communication shaped primarily by culture. In the cognitive and neurolinguistic theories of Vittorio Gallese and George Lakoff, however, multimodality is described as a key characteristic of our bodies’ sensory-motor systems which link perception to action and action to meaning, grounding all communicative acts in knowledge shaped through body-engaged experience. I argue that this “situated” account of cognition – which closely approximates Maurice Merleau-Ponty’s phenomenology of perception, a major framework for my study – has pedagogical precedence in the mimetic pedagogy that informed ancient Sophistic rhetorical training, and I reveal that training’s multimodal dimensions through a phenomenological exegesis of the concept mimesis. Plato’s denigration of the mimetic tradition and his elevation of conceptual contemplation through reason, out of which developed the classic Cartesian separation of mind from body, resulted in a general degradation of experiential knowledge in Western education. But with the recent introduction into college classrooms of digital technologies and multimedia communication tools, renewed emphasis is being placed on the “hands-on” nature of inventive and productive praxis, necessitating a revision of methods of instruction and assessment that have traditionally privileged the acquisition of conceptual over experiential knowledge. The model of multimodality I construct from Merleau-Ponty’s phenomenology, ancient Sophistic rhetorical pedagogy, and current neuroscientific accounts of situated cognition insists on recognizing the significant role knowledges we acquire experientially play in our reading and writing, speaking and listening, discerning and designing practices.

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.

"Going outside": discussing the connection between pedagogical practices with digital tools and outdoor learning in early childhood and primary education

Technology has an important role in children's lives and education. Based on several projects developed with ICT, both in Early Childhood Education (3-6 years old) and Primary Education (6-10 years old), since 1997, the authors argue that research and educational practices need to "go outside", addressing ways to connect technology with outdoor education. The experience with the projects and initiatives developed supported a conceptual framework, developed and discussed with several partners throughout the years and theoretically informed. Three main principles or axis have emerged: strengthening Children's Participation, promoting Critical Citizenship and establishing strong Connections to Pedagogy and Curriculum. In this paper, those axis will be presented and discussed in relation to the challenge posed by Outdoor Education to the way ICT in Early Childhood and Primary Education is understood, promoted and researched. The paper is exploratory, attempting to connect theoretical and conceptual contributions from Early Childhood Pedagogy with contributions from ICT in Education. The research-based knowledge available is still scarce, mostly based on studies developed with other purposes. The paper, therefore, focus the connections and interpellations between concepts established through the theoretical framework and draws on the almost 20 years of experience with large and small scale action-research projects of ICT in schools. The more recent one is already testing the conceptual framework by supporting children in non-formal contexts to explore vineyards and the cycle of wine production with several ICT tools. Approaching Outdoor Education as an arena where pedagogical and cultural dimensions influence decisions and practices, the paper tries to argue that the three axis are relevant in supporting a stronger connection between technology and the outdoor.

La estrategia en la perdurabilidad empresarial. Un estudio de la segunda aerolínea más antigua del mundo: Avianca

Una de las principales dificultades que se presenta en Colombia, para el desarrollo económico y social, está dada por la falta de sostenibilidad de la gran mayoría de empresas en el país. Por este motivo, este trabajo se ha concentrado en investigar este problema y brindar herramientas que ayuden a fomentar una cultura de perdurabilidad. Con este fin, se ha realizado un estudio acerca de Avianca, una empresa referente en el país en lo que respecta a la perdurabilidad, posicionamiento y estrategia, pues, a lo largo de sus casi cien años de historia, ha superado retos y circunstancias, que, de haber actuado de otra manera, habrían podido llevarla a su fin.

Healthcare Computer Reasoning Addressing Chronically Ill Societies Using IoT: Deep Learning AI to the Rescue of Home-Based Healthcare

The authors present a proposal to develop intelligent assisted living environments for home based healthcare. These environments unite the chronical patient clinical history sematic representation with the ability of monitoring the living conditions and events recurring to a fully managed Semantic Web of Things (SWoT). Several levels of acquired knowledge and the case based reasoning that is possible by knowledge representation of the health-disease history and acquisition of the scientific evidence will deliver, through various voice based natural interfaces, the adequate support systems for disease auto management but prominently by activating the less differentiated caregiver for any specific need. With these capabilities at hand, home based healthcare providing becomes a viable possibility reducing the institutionalization needs. The resulting integrated healthcare framework will provide significant savings while improving the generality of health and satisfaction indicators.