Block-Based Development of Mobile Learning Experiences for the Internet of Things

The Internet of Things enables experts of given domains to create smart user experiences for interacting with the environment. However, development of such experiences requires strong programming skills, which are challenging to develop for non-technical users. This paper presents several extensions to the block-based programming language used in App Inventor to make the creation of mobile apps for smart learning experiences less challenging. Such apps are used to process and graphically represent data streams from sensors by applying map-reduce operations. A workshop with students without previous experience with Internet of Things (IoT) and mobile app programming was conducted to evaluate the propositions. As a result, students were able to create small IoT apps that ingest, process and visually represent data in a simpler form as using App Inventor's standard features. Besides, an experimental study was carried out in a mobile app development course with academics of diverse disciplines. Results showed it was faster and easier for novice programmers to develop the proposed app using new stream processing blocks.Spanish National Research Agency (AEI) - ERDF fund

Practitioner Track Proceedings of the 6th International Learning Analytics & Knowledge Conference (LAK16)

Practitioners spearhead a significant portion of learning analytics, relying on implementation and experimentation rather than on traditional academic research. Both approaches help to improve the state of the art. The LAK conference has created a practitioner track for submissions, which first ran in 2015 as an alternative to the researcher track. The primary goal of the practitioner track is to share thoughts and findings that stem from learning analytics project implementations. While both large and small implementations are considered, all practitioner track submissions are required to relate to initiatives that are designed for large-scale and/or long-term use (as opposed to research-focused initiatives). Other guidelines include: • Implementation track record The project should have been used by an institution or have been deployed on a learning site. There are no hard guidelines about user numbers or how long the project has been running. • Learning/education related Submissions have to describe work that addresses learning/academic analytics, either at an educational institution or in an area (such as corporate training, health care or informal learning) where the goal is to improve the learning environment or learning outcomes. • Institutional involvement Neither submissions nor presentations have to include a named person from an academic institution. However, all submissions have to include information collected from people who have used the tool or initiative in a learning environment (such as faculty, students, administrators and trainees). • No sales pitches While submissions from commercial suppliers are welcome; reviewers do not accept overt (or covert) sales pitches. Reviewers look for evidence that a presentation will take into account challenges faced, problems that have arisen, and/or user feedback that needs to be addressed. Submissions are limited to 1,200 words, including an abstract, a summary of deployment with end users, and a full description. Most papers in the proceedings are therefore short, and often informal, although some authors chose to extend their papers once they had been accepted. Papers accepted in 2016 fell into two categories. • Practitioner Presentations Presentation sessions are designed to focus on deployment of a single learning analytics tool or initiative. • Technology Showcase The Technology Showcase event enables practitioners to demonstrate new and emerging learning analytics technologies that they are piloting or deploying. Both types of paper are included in these proceedings

Skills and Knowledge for Data-Intensive Environmental Research.

The scale and magnitude of complex and pressing environmental issues lend urgency to the need for integrative and reproducible analysis and synthesis, facilitated by data-intensive research approaches. However, the recent pace of technological change has been such that appropriate skills to accomplish data-intensive research are lacking among environmental scientists, who more than ever need greater access to training and mentorship in computational skills. Here, we provide a roadmap for raising data competencies of current and next-generation environmental researchers by describing the concepts and skills needed for effectively engaging with the heterogeneous, distributed, and rapidly growing volumes of available data. We articulate five key skills: (1) data management and processing, (2) analysis, (3) software skills for science, (4) visualization, and (5) communication methods for collaboration and dissemination. We provide an overview of the current suite of training initiatives available to environmental scientists and models for closing the skill-transfer gap

Point-of-care testing for disasters: needs assessment, strategic planning, and future design.

Objective evidence-based national surveys serve as a first step in identifying suitable point-of-care device designs, effective test clusters, and environmental operating conditions. Preliminary survey results show the need for point-of-care testing (POCT) devices using test clusters that specifically detect pathogens found in disaster scenarios. Hurricane Katrina, the tsunami in southeast Asia, and the current influenza pandemic (H1N1, "swine flu") vividly illustrate lack of national and global preparedness. Gap analysis of current POCT devices versus survey results reveals how POCT needs can be fulfilled. Future thinking will help avoid the worst consequences of disasters on the horizon, such as extensively drug-resistant tuberculosis and pandemic influenzas. A global effort must be made to improve POC technologies to rapidly diagnose and treat patients to improve triaging, on-site decision making, and, ultimately, economic and medical outcomes

Revisión tecnológica del aprendizaje de idiomas asistido por ordenador: una perspectiva cronológica

El presente artículo aborda la evolución y el avance de las tecnologías del aprendizaje de lenguas asistido por ordenador (CALL por sus siglas en inglés, que corresponden a Computer- Assisted Language Learning) desde una perspectiva histórica. Esta revisión de la literatura sobre tecnologías del aprendizaje de lenguas asistido por ordenador comienza con la definición del concepto de CALL y otros términos relacionados, entre los que podemos destacar CAI, CAL, CALI, CALICO, CALT, CAT, CBT, CMC o CMI, para posteriormente analizar las primeras iniciativas de implementación del aprendizaje de lenguas asistido por ordenador en las décadas de 1950 y 1960, avanzando posteriormente a las décadas de las computadoras centrales y las microcomputadoras. En última instancia, se revisan las tecnologías emergentes en el siglo XXI, especialmente tras la irrupción de Internet, donde se presentan el impacto del e-learning, b-learning, las tecnologías de la Web 2.0, las redes sociales e incluso el aprendizaje de lenguas asistido por robots.The main focus of this paper is on the advancement of technologies in Computer-Assisted Language Learning (CALL) from a historical perspective. The review starts by defining CALL and its related terminology, highlighting the first CALL attempts in 1950s and 1960s, and then moving to other decades of mainframes and microcomputers. At the final step, emerging technologies in 21st century will be reviewed