Bridging the digital divide for e-learning students through adaptive VLEs

Virtual Learning Environments (VLEs) are required to be highly effective and easy to use as they serve as the primary institutional portal between students and academics. There are currently a number of challenges that are caused due to the modernized digital divide, with a significant limitation being the inability of information systems to adapt to the users' technological platform, broadband quality and device in use to access the online system. This paper focuses on the limitations that students encounter when accessing VLEs within Higher Educational Institutes (HEIs). This research aims to primarily review and provide critical analysis of current VLE frameworks, as well as assess restrictions based on several demographics including content adaptation and technical aspects. An algorithmic system is developed to analyze students' individualistic needs, undertake adaption and personalization of the VLE, and hence ensure consistent and efficient access to academic web resources and functionalitie

Sensor-based Knowledge Discovery from a Large Quantity of Situational Variables

A new methodology called “sensor-based knowledge discovery”, which utilizes wearable sensors and statistical analysis, is proposed and evaluated. This methodology facilitates identifying new knowledge that can improve business outcome. It utilizes wearable sensors to unobtrusively capture people’s location, motion, and social interaction with others. The captured data is converted into multi-dimensional situational variables and then statistically analyzed to deliver a “rule set,” which forms the basis of new knowledge related to business outcome. The methodology was evaluated through a case study at a retail store. A hypothetical rule, that is, a particular area (a so-called “hot spot”) in the store where employee’s presence correlates with average sales per customer, was identified. Based on the identified rule, a measure to concentrate employees in that area was initiated. Consequently, increasing employees’ presence (“staying time”) in the hot spot by 70% increased average sales per customer by 15%. This result demonstrates the effectiveness of the methodology; namely, the new sensor-based knowledge discovery can improve actual business performance

A Consolidated View of Context for Intelligent Systems

This paper's main objective is to consolidate the knowledge on context in the realm of intelligent systems, systems that are aware of their context and can adapt their behavior accordingly. We provide an overview and analysis of 36 context models that are heterogeneous and scattered throughout multiple fields of research. In our analysis, we identify five shared context categories: social context, location, time, physical context, and user context. In addition, we compare the context models with the context elements considered in the discourse on intelligent systems and find that the models do not properly represent the identified set of 3,741 unique context elements. As a result, we propose a consolidation of the findings from the 36 context models and the 3,741 unique context elements. The analysis reveals that there is a long tail of context categories that are considered only sporadically in context models. However, particularly these context elements in the long tail may be necessary for improving intelligent systems' context awareness

User-Centered Context-Aware Mobile Applications―The Next Generation of Personal Mobile Computing

Context-aware mobile applications are systems that can sense clues about the situational environment and enable appropriate mechanisms of interaction between end users and systems, making mobile devices more intelligent, adaptive, and personalized. In order to better understand such systems and the potentials and barriers of their development and practical use, this paper provides a state-of-the-art overview of this emerging field. Unlike previous literature reviews that mainly focus on technological aspects of such systems, we examine this field mainly from application and research methodology perspectives. We will present major types of current context-aware mobile applications, and discuss research methodologies used in existing studies and their limitations, and highlight potential future research

Human centric situational awareness

Context awareness is an approach that has been receiving increasing focus in the past years. A context aware device can understand surrounding conditions and adapt its behavior accordingly to meet user demands. Mobile handheld devices offer a motivating platform for context aware applications as a result of their rapidly growing set of features and sensing abilities. This research aims at building a situational awareness model that utilizes multimodal sensor data provided through the various sensing capabilities available on a wide range of current handheld smart phones. The model will make use of seven different virtual and physical sensors commonly available on mobile devices, to gather a large set of parameters that identify the occurrence of a situation for one of five predefined context scenarios: In meeting, Driving, in party, In Theatre and Sleeping. As means of gathering the wisdom of the crowd and in an effort to reach a habitat sensitive awareness model, a survey was conducted to understand the user perception of each context situation. The data collected was used to build the inference engine of a prototype context awareness system utilizing context weights introduced in [39] and the confidence metric in [26] with some variation as a means for reasoning. The developed prototype\u27s results were benchmarked against two existing context awareness platforms Darwin Phones [17] and Smart Profile [11], where the prototype was able to acquire 5% and 7.6% higher accuracy levels than the two systems respectively while performing tasks of higher complexity. The detailed results and evaluation are highlighted further in section 6.4

A review of the role of sensors in mobile context-aware recommendation systems

Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios

A Knowledge-driven Distributed Architecture for Context-Aware Systems

As the number of devices increases, it becomes a challenge for the users to use them effectively. This is more challenging when the majority of these devices are mobile. The users and their devices enter and leave different environments where different settings and computing needs may be required. To effectively use these devices in such environments means to constantly be aware of their whereabouts, functionalities and desirable working conditions. This is impractical and hence it is imperative to increase seamless interactions between the users and devices,and to make these devices less intrusive. To address these problems, various responsive computing systems, called context- aware systems, have been developed. These systems rely on architectures to perceive their physical environments in order to appropriately and effortlessly respond. Currently, the majority of the existing architectures focus on acquiring data from sensors, interpreting and sharing it with these systems