Towards mobile learning deployment in higher learning institutions : a report on the qualitative inquiries conducted in four universities in Tanzania

Over the past two decades, mobile learning (m-learning) has been a purposeful area of research among educational technologists, educators and instructional designers whereby doubts and controversies over its relevancy and applicability have been keenly addressed. This paper explores stakeholders’ perceptions of m-learning deployment in Higher Learning Institutions (HLIs). Spe- cifically, we examine the potential of m-learning for HLIs in Tanzania and the challenges that hinder successful m-learning deployment. We adopt a comparative qualitative case study design in which four HLIs in Tanzania were purposefully selected. The study uses a combination of de- sign science research approach and qualitative methods including grounded theory, document re- views, and observation. The respondents included university lecturers, students and ICT experts, who were selected for the interviews through theoretical sampling. The transcripts were loaded, coded and analyzed in NVIVO software. The results indicate that mobiles (smartphone, tablets, laptops, feature-phones etc.) are widely used in the HLIs. Stakeholders perceive that m-learning deployment is important and useful because it improves the quality of the learning experience. The results further indicate that there are financial, pedagogical, technological, infrastructural, individuals – and policy – related challenges that hinder successful deployment of m-learning in HLIs in Tanzania, such as limited network coverage, some students ́ inability to afford mobiles, lack of qualified staff for preparation of mobile content and administration, gaps in the exist- ing policies, and faulty course design. However, our results show that participants are optimistic about the potential of m-learning in the HLIs of Tanzania. They expect that m-learning will im- prove access to learning resources, teacher-student and student-student interaction without being restricted by time or place. Thus, m-learning is considered to have the potential to address issues of crowded classrooms, expertise, access to learning materials, flexibility of the learners as well as remote connectivity.
 We recommend that HLIs should prioritize m-learning and commit resources to the success of the related projects. We also recommend that the governments and stakeholders provide policy interventions, subsidize mobile technologies, expand network coverage, build capacity within and outside HLIs, and improve digital literacy by integrating ICT education at all levels of education

The Utilization of Mobile Technology for Crime Scene Investigation in the San Francisco Bay Area

The research presented aims to explore factors affecting the decision to adopt a mobile crime scene investigation application in police departments throughout the San Francisco Bay Area. To accomplish this goal, the mobile technology acceptance model was used in designing a survey for data collection. This model utilizes four categories to interpret the factors that influence a police officer’s decision to accept or reject mobile technologies: performance, security and reliability, management style, and cognitive acceptance. Nine police departments were sampled through a series of in-person and over-the-phone interviews to obtain data regarding factors affecting the adoption of a mobile crime scene investigation application. Results suggest that if a mobile crime scene investigation application were made available, a vast majority of the police departments in the Bay Area would implement this new technology

Human Mobility and Application Usage Prediction Algorithms for Mobile Devices

Mobile devices such as smartphones and smart watches are ubiquitous companions of humans’ daily life. Since 2014, there are more mobile devices on Earth than humans. Mobile applications utilize sensors and actuators of these devices to support individuals in their daily life. In particular, 24% of the Android applications leverage users’ mobility data. For instance, this data allows applications to understand which places an individual typically visits. This allows providing her with transportation information, location-based advertisements, or to enable smart home heating systems. These and similar scenarios require the possibility to access the Internet from everywhere and at any time. To realize these scenarios 83% of the applications available in the Android Play Store require the Internet to operate properly and therefore access it from everywhere and at any time. Mobile applications such as Google Now or Apple Siri utilize human mobility data to anticipate where a user will go next or which information she is likely to access en route to her destination. However, predicting human mobility is a challenging task. Existing mobility prediction solutions are typically optimized a priori for a particular application scenario and mobility prediction task. There is no approach that allows for automatically composing a mobility prediction solution depending on the underlying prediction task and other parameters. This approach is required to allow mobile devices to support a plethora of mobile applications running on them, while each of the applications support its users by leveraging mobility predictions in a distinct application scenario. Mobile applications rely strongly on the availability of the Internet to work properly. However, mobile cellular network providers are struggling to provide necessary cellular resources. Mobile applications generate a monthly average mobile traffic volume that ranged between 1 GB in Asia and 3.7 GB in North America in 2015. The Ericsson Mobility Report Q1 2016 predicts that by the end of 2021 this mobile traffic volume will experience a 12-fold increase. The consequences are higher costs for both providers and consumers and a reduced quality of service due to congested mobile cellular networks. Several countermeasures can be applied to cope with these problems. For instance, mobile applications apply caching strategies to prefetch application content by predicting which applications will be used next. However, existing solutions suffer from two major shortcomings. They either (1) do not incorporate traffic volume information into their prefetching decisions and thus generate a substantial amount of cellular traffic or (2) require a modification of mobile application code. In this thesis, we present novel human mobility and application usage prediction algorithms for mobile devices. These two major contributions address the aforementioned problems of (1) selecting a human mobility prediction model and (2) prefetching of mobile application content to reduce cellular traffic. First, we address the selection of human mobility prediction models. We report on an extensive analysis of the influence of temporal, spatial, and phone context data on the performance of mobility prediction algorithms. Building upon our analysis results, we present (1) SELECTOR – a novel algorithm for selecting individual human mobility prediction models and (2) MAJOR – an ensemble learning approach for human mobility prediction. Furthermore, we introduce population mobility models and demonstrate their practical applicability. In particular, we analyze techniques that focus on detection of wrong human mobility predictions. Among these techniques, an ensemble learning algorithm, called LOTUS, is designed and evaluated. Second, we present EBC – a novel algorithm for prefetching mobile application content. EBC’s goal is to reduce cellular traffic consumption to improve application content freshness. With respect to existing solutions, EBC presents novel techniques (1) to incorporate different strategies for prefetching mobile applications depending on the available network type and (2) to incorporate application traffic volume predictions into the prefetching decisions. EBC also achieves a reduction in application launch time to the cost of a negligible increase in energy consumption. Developing human mobility and application usage prediction algorithms requires access to human mobility and application usage data. To this end, we leverage in this thesis three publicly available data set. Furthermore, we address the shortcomings of these data sets, namely, (1) the lack of ground-truth mobility data and (2) the lack of human mobility data at short-term events like conferences. We contribute with JK2013 and UbiComp Data Collection Campaign (UbiDCC) two human mobility data sets that address these shortcomings. We also develop and make publicly available a mobile application called LOCATOR, which was used to collect our data sets. In summary, the contributions of this thesis provide a step further towards supporting mobile applications and their users. With SELECTOR, we contribute an algorithm that allows optimizing the quality of human mobility predictions by appropriately selecting parameters. To reduce the cellular traffic footprint of mobile applications, we contribute with EBC a novel approach for prefetching of mobile application content by leveraging application usage predictions. Furthermore, we provide insights about how and to what extent wrong and uncertain human mobility predictions can be detected. Lastly, with our mobile application LOCATOR and two human mobility data sets, we contribute practical tools for researchers in the human mobility prediction domain

A Comparison of Information Technology Mediated Customer Services Between the U.S. and China

Information technology mediated customer service is a reality of the 21st century. More and more companies have moved their customer services from in store and in person to online through computer or mobile devices. Using 442 responses collected from one USA university (234 responses) and two Chinese universities (208 responses), the study investigates customer preferences over two service delivery models (either in store or online) on five types of purchasing (retail, eating-out, banking, travel and entertainment) and their perception difference in customer service quality between those two delivery models in the U.S. and China. The results show that the majority of the U.S. and Chinese students prefer in-store and in person for eating out and prefer computer/mobile devices for ordering tickets for travel and entertainment. In addition, more than half of the U.S. students prefer in person services for retail and banking, and this number reduces to 40% for Chinese students. In most customer service quality measurements, the results also show that Chinese students give higher ratings for ordering through a computer/mobile device than ordering in store, indicating ordering through computer/mobile devices has become more acceptable in China and has been perceived as having better customer services quality than in-store ordering

OPEN SOURCE WEB TOOL FOR TRACKING IN A LOWCOST MOBILE MAPPING SYSTEM

During the last decade several Mobile Mapping Systems (MMSs), i.e. systems able to acquire efficiently three dimensional data using moving sensors (Guarnieri et al., 2008, Schwarz and El-Sheimy, 2004), have been developed. Research and commercial products have been implemented on terrestrial, aerial and marine platforms, and even on human-carried equipment, e.g. backpack (Lo et al., 2015, Nex and Remondino, 2014, Ellum and El-Sheimy, 2002, Leica Pegasus backpack, 2016, Masiero et al., 2017, Fissore et al., 2018).<br><br> Such systems are composed of an integrated array of time-synchronised navigation sensors and imaging sensors mounted on a mobile platform (Puente et al., 2013, Tao and Li, 2007). Usually the MMS implies integration of different types of sensors, such as GNSS, IMU, video camera and/or laser scanners that allow accurate and quick mapping (Li, 1997, Petrie, 2010, Tao, 2000). The typical requirement of high-accuracy 3D georeferenced reconstruction often makes such systems quite expensive. Indeed, at time of writing most of the terrestrial MMSs on the market have a cost usually greater than 50000, which might be expensive for certain applications (Ellum and El-Sheimy, 2002, Piras et al., 2008). In order to allow best performance sensors have to be properly calibrated (Dong et al., 2007, Ellum and El-Sheimy, 2002).<br><br> Sensors in MMSs are usually integrated and managed through a dedicated software, which is developed ad hoc for the devices mounted on the mobile platform and hence tailored for the specific used sensors. Despite the fact that commercial solutions are complete, very specific and particularly related to the typology of survey, their price is a factor that restricts the number of users and the possible interested sectors.<br><br> This paper describes a (relatively low cost) terrestrial Mobile Mapping System developed at the University of Padua (TESAF, Department of Land Environment Agriculture and Forestry) by the research team in CIRGEO, in order to test an alternative solution to other more expensive MMSs. The first objective of this paper is to report on the development of a prototype of MMS for the collection of geospatial data based on the assembly of low cost sensors managed through a web interface developed using open source libraries. The main goal is to provide a system accessible by any type of user, and flexible to any type of upgrade or introduction of new models of sensors or versions thereof. After a presentation of the hardware components used in our system, a more detailed description of the software developed for the management of the MMS will be provided, which is the part of the innovation of the project. According to the worldwide request for having big data available through the web from everywhere in the world (Pirotti et al., 2011), the proposed solution allows to retrieve data from a web interface Figure 4. Actually, this is part of a project for the development of a new web infrastructure in the University of Padua (but it will be available for external users as well), in order to ease collaboration between researchers from different areas.<br><br> Finally, strengths, weaknesses and future developments of the low cost MMS are discussed