Learning design guided learning analytics in MOOCs

Poster presentation for our paper Brouns, F., & Firssova, O. (2016, October).The role of learning design and learning analytics in MOOCs. Paper presented at 9th EDEN Research Workshop, Oldenburg, Germany.

Applying standards to systematize learning analytics in serious games

Learning Analytics is an emerging field focused on analyzing learners’ interactions with educational content. One of the key open issues in learning analytics is the standardization of the data collected. This is a particularly challenging issue in serious games, which generate a diverse range of data. This paper reviews the current state of learning analytics, data standards and serious games, studying how serious games are tracking the interactions from their players and the metrics that can be distilled from them. Based on this review, we propose an interaction model that establishes a basis for applying Learning Analytics into serious games. This paper then analyzes the current standards and specifications used in the field. Finally, it presents an implementation of the model with one of the most promising specifications: Experience API (xAPI). The Experience API relies on Communities of Practice developing profiles that cover different use cases in specific domains. This paper presents the Serious Games xAPI Profile: a profile developed to align with the most common use cases in the serious games domain. The profile is applied to a case study (a demo game), which explores the technical practicalities of standardizing data acquisition in serious games. In summary, the paper presents a new interaction model to track serious games and their implementation with the xAPI specification.

Game Learning Analytics: Learning Analytics for Serious Games

Video games have become one of the largest entertainment industries, and their power to capture the attention of players worldwide soon prompted the idea of using games to improve education. However, these educational games, commonly referred to as serious games, face different challenges when brought into the classroom, ranging from pragmatic issues (e.g. a high development cost) to deeper educational issues, including a lack of understanding of how the students interact with the games and how the learning process actually occurs. This chapter explores the potential of data-driven approaches to improve the practical applicability of serious games. Existing work done by the entertainment and learning industries helps to build a conceptual model of the tasks required to analyze player interactions in serious games (gaming learning analytics or GLA). The chapter also describes the main ongoing initiatives to create reference GLA infrastructures and their connection to new emerging specifications from the educational technology field. Finally, it explores how this data-driven GLA will help in the development of a new generation of more effective educational games and new business models that will support their expansion. This results in additional ethical implications, which are discussed at the end of the chapter.

Predictive Analytics and Decision Support for Heart Failure patients

Thesis (Master's)--University of Washington, 2016-08

Incorporación de un sistema de almacenamiento de experiencias a un ecosistema educativo para permitir el uso de técnicas de análisis de datos (Educational Analytics)

La Universidad EAFIT, en los últimos años, por medio de la realización de varias investigaciones, ha estado desarrollado una propuesta con la cual se busca definir los componentes tecnológicos que deben componer un ecosistema de aplicaciones educativas, con el fin de apalancar la adopción del modelo de ubicuidad en las instituciones de educación superior -- Por medio del grupo de investigación de desarrollo e innovación en Tecnologías de la Información y las Comunicaciones (GIDITIC) ha realizado la selección de los primeros componentes del ecosistema en trabajos de tesis de grado de anteriores investigaciones[1, 2] -- Adicionalmente, algunos trabajos realizados por el gobierno local de la Alcaldía de Medellín en su proyecto de Medellín Ciudad Inteligente[3], también realizó una selección de algunos componentes que son necesarios para la implementación del portal -- Ambas iniciativas coinciden en la inclusión de un componente de registro de actividades, conocido como \Sistema de almacenamiento de experiencias" (LRS) -- Dados estos antecedentes, se pretende realizar una implementación de un LRS que cumpla con los objetivos buscados en el proyecto de la Universidad, siguiendo estándares que permitan asegurar la interoperabilidad con los otros componentes del ecosistema de aplicaciones educativas

Real-time analytics on large dynamic graphs

In today's fast-paced and interconnected digital world, the data generated by an increasing number of applications is being modeled as dynamic graphs. The graph structure encodes relationships among data items, while the structural changes to the graphs as well as the continuous stream of information produced by the entities in these graphs make them dynamic in nature. Examples include social networks where users post status updates, images, videos, etc.; phone call networks where nodes may send text messages or place phone calls; road traffic networks where the traffic behavior of the road segments changes constantly, and so on. There is a tremendous value in storing, managing, and analyzing such dynamic graphs and deriving meaningful insights in real-time. However, a majority of the work in graph analytics assumes a static setting, and there is a lack of systematic study of the various dynamic scenarios, the complexity they impose on the analysis tasks, and the challenges in building efficient systems that can support such tasks at a large scale. In this dissertation, I design a unified streaming graph data management framework, and develop prototype systems to support increasingly complex tasks on dynamic graphs. In the first part, I focus on the management and querying of distributed graph data. I develop a hybrid replication policy that monitors the read-write frequencies of the nodes to decide dynamically what data to replicate, and whether to do eager or lazy replication in order to minimize network communication and support low-latency querying. In the second part, I study parallel execution of continuous neighborhood-driven aggregates, where each node aggregates the information generated in its neighborhoods. I build my system around the notion of an aggregation overlay graph, a pre-compiled data structure that enables sharing of partial aggregates across different queries, and also allows partial pre-computation of the aggregates to minimize the query latencies and increase throughput. Finally, I extend the framework to support continuous detection and analysis of activity-based subgraphs, where subgraphs could be specified using both graph structure as well as activity conditions on the nodes. The query specification tasks in my system are expressed using a set of active structural primitives, which allows the query evaluator to use a set of novel optimization techniques, thereby achieving high throughput. Overall, in this dissertation, I define and investigate a set of novel tasks on dynamic graphs, design scalable optimization techniques, build prototype systems, and show the effectiveness of the proposed techniques through extensive evaluation using large-scale real and synthetic datasets.

Scaling up online learning strategic and operational checklist

This checklist highlights key questions for institutions that are considering their online learning provision and relates to the Jisc Scaling up online learning (strategic and operational) guide.

Improving the operational skills of the health personnel working in the emergency departments. A useful tool to give concrete answers to EU policies on patients' safety and to create new working opportunities” (VS/2013/0430) finanziato dalla Commissione Europea nell'ambito del programma PROGRESS.

A useful tool to give concrete answers to EU policies on patients' safety and to create new working opportunities (VS / 2013/0430

Use of satellite observations for operational oceanography: recent achievements and future prospects

The paper gives an overview of the development of satellite oceanography over the past five years focusing on the most relevant issues for operational oceanography. Satellites provide key essential variables to constrain ocean models and/or serve downstream applications. New and improved satellite data sets have been developed and have directly improved the quality of operational products. The status of the satellite constellation for the last five years was, however, not optimal. Review of future missions shows clear progress and new research and development missions with a potentially large impact for operational oceanography should be demonstrated. Improvement of data assimilation techniques and developing synergetic use of high resolution satellite observations are important future priorities.

'Artificial Immune Systems: A new Decision Making Paradigm for Operational Research'

Over the last few years, more and more heuristic decision making techniques have been inspired by nature, e.g. evolutionary algorithms, ant colony optimisation and simulated annealing. More recently, a novel computational intelligence technique inspired by immunology has emerged, called Artificial Immune Systems (AIS). This immune system inspired technique has already been useful in solving some computational problems. In this keynote, we will very briefly describe the immune system metaphors that are relevant to AIS. We will then give some illustrative real-world problems suitable for AIS use and show a step-by-step algorithm walkthrough. A comparison of AIS to other well-known algorithms and areas for future work will round this keynote off. It should be noted that as AIS is still a young and evolving field, there is not yet a fixed algorithm template and hence actual implementations might differ somewhat from the examples given here