Learning&Information Technologies Cartography

Nowadays, many researches focus their efforts in studies and applications on the Learning area. However, there is a lack of a reference system that permits to know the positioning and the existing links between Learning and Information Technologies. This paper proposes a Cartography where explains the relationships between the elements that compose the Learning Theories and Information Technologies, considering the own features of the learner and the Information Technologies Properties. This intersection will allow us to know what Information Technologies Properties promote Learning Futures

Geolearners: Location-based informal learning with mobile and social technologies

This paper looks at how mobile and social technologies are influencing informal learning in the context of online community membership. The development of mobile technologies that use Global Positioning System (GPS) data to pinpoint geographical location together with the rapidly evolving Web 2.0 technologies supporting the creation and consumption of content suggest a potential for collaborative informal learning linked to location. The research described in this paper asks whether these technologies can provide an effective focus for community activities and, if so, whether this combination of location-awareness, mobile and Web 2.0 technology results in the creation of novel informal learning opportunities. The community selected for study was the Geocaching community, a geographically dispersed group who use mobile and Web 2.0 technologies to link the virtual social spaces of the internet with the physical spaces that surround them

Machine Learning-based Indoor Positioning Systems Using Multi-Channel Information

The received signal strength indicator (RSSI) is a metric of the power measured by a sensor in a receiver. Many indoor positioning technologies use RSSI to locate objects in indoor environments. Their positioning accuracy is significantly affected by reflection and absorption from walls, and by non-stationary objects such as doors and people. Therefore, it is necessary to increase transceivers in the environment to reduce positioning errors. This paper proposes an indoor positioning technology that uses the machine learning algorithm of channel state information (CSI) combined with fingerprinting. The experimental results showed that the proposed method outperformed traditional RSSI-based localization systems in terms of average positioning accuracy up to 6.13% and 54.79% for random forest (RF) and back propagation neural networks (BPNN), respectively

Pedagogies of Design and Multiliterate Learner Identities

In an era of multiliteracies, teaching and learning have become knowledge performances at multiple levels. Instead of a singular, linear focus upon print technologies, the techno-oriented philosophy of teaching aims at providing a rhizomatic network of texts where there is a close link between, and often an overlap of, different designs—linguistic, visual, spatial, and gestural—to construct the multiliterate learner. In this paper, I discuss the role of multimodal literacies in a primary classroom, affirming the role of multiliteracies and decentring the pre-dominance of linguistic at the cost of other designs. While the print media are acknowledged as significant to literacy, the multimodality of print is enhanced through visual and spatial design (Kenner, 2004). Through graphic examples of ICT applications of designs in a primary classroom, I demonstrate that students are operating through multitextual and digitextual (Everett, 2003) practices. What follows is the complex positioning and re-situating of teacher and learner identities engaged in learning through the knowledge processes of experiencing, identifying, applying and critiquing concepts (Kalantzis & Cope, 2004). In particular, I argue that within the diversity of present day classrooms, the digital oriented, multiliterate learner is implicated in constant identity construction by drawing upon macro and micro social practices. I conclude by reiterating the significance of new technologies and new literacy practices as essential to the construction of new learner identities

Analysis and evaluation of Wi-Fi indoor positioning systems using smartphones

This paper attempts to analyze the main algorithms used in Machine Learning applied to the indoor location. New technologies are facing new challenges. Satellite positioning has become a typical application of mobile phones, but stops working satisfactorily in enclosed spaces. Currently there is a problem in positioning which is unresolved. This circumstance motivates the research of new methods. After the introduction, the first chapter presents current methods of positioning and the problem of positioning indoors. This part of the work shows globally the current state of the art. It mentions a taxonomy that helps classify the different types of indoor positioning and a selection of current commercial solutions. The second chapter is more focused on the algorithms that will be analyzed. It explains how the most widely used of Machine Learning algorithms work. The aim of this section is to present mathematical algorithms theoretically. These algorithms were not designed for indoor location but can be used for countless solutions. In the third chapter, we learn gives tools work: Weka and Python. the results obtained after thousands of executions with different algorithms and parameters showing main problems of Machine Learning shown. In the fourth chapter the results are collected and the conclusions drawn are shown

WIFI BASED INDOOR POSITIONING - A MACHINE LEARNING APPROACH

Navigation has become much easier these days mainly due to advancement in satellite technology. The current navigation systems provide better positioning accuracy but are limited to outdoors. When it comes to the indoor spaces such as airports, shopping malls, hospitals or office buildings, to name a few, it will be challenging to get good positioning accuracy with satellite signals due to thick walls and roofs as obstacles. This gap led to a whole new area of research in the field of indoor positioning. Many researches have been conducting experiments on different technologies and successful outcomes have beenseen. Each technology providing indoor positioning capability has its own limitations. In this thesis, different radio frequency (RF) and non-radio frequency (Non-RF) technologies are discussed but focus is set on Wi-Fi for indoor positioning. A demo indoor positioning app is developed for the Technobothnia building at the University of Vaasa premises. This building is already equipped with Wi-Fi infrastructure. A floor plan of the building, radio maps and a fingerprinting database with Wi-Fi signal strength measurements is created with help of tools from HERE technology. The app provides real-time positioning and routing as a future visitor tool. With the exceeding amounts of available data, one of the highly popular fields is applying Machine Learning (ML) to data. It can be applied in many disciplines from medicine to space. In ML, algorithms learn from the data and make predictions. Due to the significant growth in various sensor technologies and computational power, large amounts of data can be stored and processed. Here, the ML approach is also taken to the indoor positioning challenge. An open-source Wi-Fi fingerprinting dataset is obtained from Tampere University and ML algorithms are applied on it for performing indoor positioning. Algorithms are trained with received signal strength (RSS) values with their respective reference coordinates and the user location can be predicted. The thesis provides a performance analysis of different algorithms suitable for future mobile implementations

Making mature undergraduates’ experience visible:exploring sense of belonging and use of digital technologies

The aim of this paper is to explore the role that informal networks, interactions and digital technologies play in supporting the participation in undergraduate education of eleven mature students coming from widening participation backgrounds in one research-intensive United Kingdom (UK) university. For eighteen months mature students were co-researchers of the longitudinal qualitative study DD-LAB: Digital Diversity Learning and Belonging'. Along with problematizing the heterogeneity of students grouped under the category and home location were as influential as age when understanding their participation in university. Digital technologies played a role in fostering belonging and participation in academic and social spaces. Yet, engagement in a digital world did not necessarily mitigate their positioning as a minority group within a research-intensive institution. Although digital technologies and informal networks helped mature students overcome institutional struggles and expanded modes of belonging, we conclude that the institutional and social positioning constrained mature students sense of academic and social integration, leading to continuing inequalities that universities need to address

Secret Codes: The Hidden Curriculum of Semantic Web Technologies

There is a long tradition in education of examination of the hidden curriculum, those elements which are implicit or tacit to the formal goals of education. This article draws upon that tradition to open up for investigation the hidden curriculum and assumptions about students and knowledge that are embedded in the coding undertaken to facilitate learning through information technologies, and emerging ‘semantic technologies’ in particular. Drawing upon an empirical study of case-based pedagogy in higher education, we examine the ways in which code becomes an actor in both enabling and constraining knowledge, reasoning, representation and students. The article argues that how this occurs, and to what effect, is largely left unexamined and becomes part of the hidden curriculum of electronically mediated learning that can be more explicitly examined by positioning technologies in general, and code in particular, as actors rather than tools. This points to a significant research agenda in technology enhanced learning