Proposal of a mobile learning preferences model

A model consisting of five dimensions of mobile learning preferences – location, level of distractions, time of day, level of motivation and available time – is proposed in this paper. The aim of the model is to potentially increase the learning effectiveness of individuals or groups by appropriately matching and allocating mobile learning materials/applications according to each learner’s type. Examples are given. Our current research investigations relating to this model are described

Visual analysis of sensor logs in smart spaces: Activities vs. situations

Models of human habits in smart spaces can be expressed by using a multitude of representations whose readability influences the possibility of being validated by human experts. Our research is focused on developing a visual analysis pipeline (service) that allows, starting from the sensor log of a smart space, to graphically visualize human habits. The basic assumption is to apply techniques borrowed from the area of business process automation and mining on a version of the sensor log preprocessed in order to translate raw sensor measurements into human actions. The proposed pipeline is employed to automatically extract models to be reused for ambient intelligence. In this paper, we present an user evaluation aimed at demonstrating the effectiveness of the approach, by comparing it wrt. a relevant state-of-the-art visual tool, namely SITUVIS

Mobile-assisted language learning [Revised and updated version]

Mobile-assisted language learning (MALL) is the use of smartphones and other mobile technologies in language learning, especially in situations where portability and situated learning offer specific advantages. A key attraction of mobile learning is the ubiquity of mobile phones. Typical applications can support learners in reading, listening, speaking and writing in the target language, either individually or in collaboration with one another. Increasingly, MALL applications relate language learning to a person’s physical context when mobile, primarily to provide access to location-specific language material or to enable learners to capture aspects of language use in situ and share it with others. Mobile learning can be formal or informal, and mobile devices may form a bridge connecting in-class and out-of-class learning. When learning takes place outside the classroom, it is often beyond the reach and control of the teacher. This can be perceived as a threat, but it is also an opportunity to revitalize and rethink current approaches to teaching and learning. Mobile learning appeals to a wide range of people for a variety of reasons. It may exclude some learners but it is often a mechanism for inclusion. It is likely that the next generation of mobile learning will be more ubiquitous, which means that there will be smart systems everywhere for digital learning. Mobile learning is proving its potential to address authentic learner needs at the point at which they arise, and to deliver more flexible models of language learning

Quality assessment technique for ubiquitous software and middleware

The new paradigm of computing or information systems is ubiquitous computing systems. The technology-oriented issues of ubiquitous computing systems have made researchers pay much attention to the feasibility study of the technologies rather than building quality assurance indices or guidelines. In this context, measuring quality is the key to developing high-quality ubiquitous computing products. For this reason, various quality models have been defined, adopted and enhanced over the years, for example, the need for one recognised standard quality model (ISO/IEC 9126) is the result of a consensus for a software quality model on three levels: characteristics, sub-characteristics, and metrics. However, it is very much unlikely that this scheme will be directly applicable to ubiquitous computing environments which are considerably different to conventional software, trailing a big concern which is being given to reformulate existing methods, and especially to elaborate new assessment techniques for ubiquitous computing environments. This paper selects appropriate quality characteristics for the ubiquitous computing environment, which can be used as the quality target for both ubiquitous computing product evaluation processes ad development processes. Further, each of the quality characteristics has been expanded with evaluation questions and metrics, in some cases with measures. In addition, this quality model has been applied to the industrial setting of the ubiquitous computing environment. These have revealed that while the approach was sound, there are some parts to be more developed in the future

Theoretical perspectives on mobile language learning diaries and noticing for learners,teachers and researchers

This paper considers the issue of 'noticing' in second language acquisition, and argues for the potential of handheld devices to: (i) support language learners in noticing and recording noticed features 'on the spot', to help them develop their second language system; (ii) help language teachers better understand the specific difficulties of individuals or those from a particular language background; and (iii) facilitate data collection by applied linguistics researchers, which can be fed back into educational applications for language learning. We consider: theoretical perspectives drawn from the second language acquisition literature, relating these to the practice of writing language learning diaries; and the potential for learner modelling to facilitate recording and prompting noticing in mobile assisted language learning contexts. We then offer guidelines for developers of mobile language learning solutions to support the development of language awareness in learners

Location-based and contextual mobile learning. A STELLAR Small-Scale Study

This study starts from several inputs that the partners have collected from previous and current running research projects and a workshop organised at the STELLAR Alpine Rendevous 2010. In the study, several steps have been taken, firstly a literature review and analysis of existing systems; secondly, mobile learning experts have been involved in a concept mapping study to identify the main challenges that can be solved via mobile learning; and thirdly, an identification of educational patterns based on these examples has been done. Out of this study the partners aim to develop an educational framework for contextual learning as a unifying approach in the field. Therefore one of our central research questions is: how can we investigate, theorise, model and support contextual learning

Surveying human habit modeling and mining techniques in smart spaces

A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field