Mobile Learning: location, collaboration and scaffolding inquiry

Critiques of mobile learning pedagogy are concerned with whether such approaches are technology led. This chapter discusses how the particular features of mobile learning can be harnessed to provide new learning opportunities in relation to collaboration, inquiry and location-based learning. Technology supported inquiry learning is a situation rich with possibilities for collaboration. In particular, mobile learning offers new possibilities for scaffolding collaboration together with its other better-known features such as scaffolding the transfer between settings and making learning relevant by making use of the possibilities of location-based learning. These features are considered as part of mobile learning models, in particular mobile collaborative learning models

Exploring value co-creation within buyer-seller relationship in mobile applications services : a model development

Mobile phones have become an indispensable part of consumers’ life where they access core and supporting services via mobile applications services (m-applications). The focus of the present study is to explore dyadic buyer-seller roles in m-applications services’ value creation taking mobile banking applications services (MB-applications) as a case study. While prior research on value co-creation in service dominant logic (S-d logic) serves as a foundation for this study, it does not provide adequate guidance on how buyer and seller co-create value in m-applications services.To address this shortcoming, semi-structured interviews were carried out with 12 banks’ officials in banks’ headquarters of Saudi Arabia. Also, six focus groups were conducted; three with MB-application services users and three with non-users which were held in Riyadh College of Technology (RCT). In addition, a content analysis of MB-applications services was conducted to support suppliers’ perspectives regarding value propositions (service offering). A conceptual framework is developed for managing co-creation to illustrate practical application of the framework.The findings pointed to six factors that shape shape service suppliers’ ability to offer and deliver value via MB-applications, namely; brand image building, bank’s business vision, customer culture-orientation, bank’s internal environment, information technology system and positioning strategy. These factors combine to establish a value proposition for banks’ customers in the MB-applications services domain.Customer’s value creation as value in-use during usage emerged in different usage situations. A value framework incorporating value consumptions (Sheth et al., 1991a) is proposed. It identifies the main value-adding elements in m-applications and the primary drivers for adopting m-applications. Findings revealed that bank managers attempted to support customers’ value creation, which was reflected in MB-application content. However, support was constrained by some insufficient assumptions about customers and the m-commerce architecture. Factors that impede MB-applications use include consumers’ banking habits, perceived risk (security and privacy); usability hindrance, marketing and promotion, technical problems, and socio-cultural barriers. Implications are drawn for service delivery value perception and mobile marketing theory, and recommendations are made to service suppliers and commercial banks to achieve sustained returns of investment from MB-applications services

Impact of Location Spoofing Attacks on Performance Prediction in Mobile Networks

Performance prediction in wireless mobile networks is essential for diverse purposes in network management and operation. Particularly, the position of mobile devices is crucial to estimating the performance in the mobile communication setting. With its importance, this paper investigates mobile communication performance based on the coordinate information of mobile devices. We analyze a recent 5G data collection and examine the feasibility of location-based performance prediction. As location information is key to performance prediction, the basic assumption of making a relevant prediction is the correctness of the coordinate information of devices given. With its criticality, this paper also investigates the impact of position falsification on the ML-based performance predictor, which reveals the significant degradation of the prediction performance under such attacks, suggesting the need for effective defense mechanisms against location spoofing threats

Twitter Bots’ Detection with Benford’s Law and Machine Learning

Online Social Networks (OSNs) have grown exponentially in terms of active users and have now become an influential factor in the formation of public opinions. For this reason, the use of bots and botnets for spreading misinformation on OSNs has become a widespread concern. Identifying bots and botnets on Twitter can require complex statistical methods to score a profile based on multiple features. Benford’s Law, or the Law of Anomalous Numbers, states that, in any naturally occurring sequence of numbers, the First Significant Leading Digit (FSLD) frequency follows a particular pattern such that they are unevenly distributed and reducing. This principle can be applied to the first-degree egocentric network of a Twitter profile to assess its conformity to such law and, thus, classify it as a bot profile or normal profile. This paper focuses on leveraging Benford’s Law in combination with various Machine Learning (ML) classifiers to identify bot profiles on Twitter. In addition, a comparison with other statistical methods is produced to confirm our classification results

Robustness of Image-Based Malware Analysis

In previous work, “gist descriptor” features extracted from images have been used in malware classification problems and have shown promising results. In this research, we determine whether gist descriptors are robust with respect to malware obfuscation techniques, as compared to Convolutional Neural Networks (CNN) trained directly on malware images. Using the Python Image Library (PIL), we create images from malware executables and from malware that we obfuscate. We conduct experiments to compare classifying these images with a CNN as opposed to extracting the gist descriptor features from these images to use in classification. For the gist descriptors, we consider a variety of classification algorithms including k-nearest neighbors, random forest, support vector machine, and multi-layer perceptron. We find that gist descriptors are more robust than CNNs, with respect to the obfuscation techniques that we consider