The implementation of Iban vocabulary module in I-MMAPS (Interactive Multimedia-Based Mobile Application) for learning Iban language

Mobile learning is the next generation of e-learning which utilizes the usage of mobile devices and wireless communication technology in education. Mobile learning is characterized by its potential for learning to be spontaneous, informal, personalized and ubiquitous. The traditional language learning context is experiencing radical changes and challenges as nowadays, language learners are demanding for better and improved access and convenience at lower costs and with the direct application of contents to their environment and needs. Currently, there is no locally produced mobile application to introduce the Iban language to adult learners. Therefore, this project proposes to enhance the interactive mobile application for learning Iban Language, by creating another module which is the vocabulary module. This mobile application will also serve as a repository for Iban Language that can be used to promote and preserve Iban Language among younger generations. The application consists of two modules, vocabulary and conversations, which incorporate pronunciations and spellings. This application is designed by adapting constructivism learning theory to provide the learners, a different approach in learning Iban Language based on their environments and situations. The outcome of this research is an enhanced interactive mobile application which includes Iban vocabulary module to attract users to learn the language. The analysis on users' acceptance toward the usage of the application in learning Iban Language, is done by adapting the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al.,2003). Based on the findings, respondents accepted the use of mobile devices and mobile application as a tool to help them to learn Iban Language. They were keen about the prospect of engaging in language learning using the mobile application in any time or place. In addition, they also perceived that the mobile application is easy to use and mobile learning is enjoyable

Mobile learning as a component of the foreign language learning process for senior school students

The article deals with the directions of using mobile education in modern educational system. It is noted that the use of "mobile-learning" will create more natural conditions for learning and optimizing the process of language training of high school students. Mobile learning is closely linked to e-learning and distance learning, the difference between which is the use of mobile devices, and in which students anywhere and at any time can develop and improve skills and abilities to speak a foreign language (based on the means of synchronous and asynchronous communication), to form socio- cultural and intercultural competencies in order to use a foreign language as a means of communication in the social sphere. The work defines the term "mobile learning", its methodological foundations and the guiding principles for constructing a learning process, based on the usage of mobile devices. The analysis of the articles which are related to the use of mobile devices in teaching and their didactic functions is carried out. The advantages and disadvantages of this type of training, possibilities and features of introduction of mobile education in general educational schools are analyzed. The methodical potential of mobile technologies is considered and it is noted that if properly used, they can be didactic means, which helps to learn more foreign language more quickly and qualitatively, in comparison with traditional means. The main possibilities of using mobile gadgets in teaching activity during visualization of a demonstration material, questioning, testing, and also during distance learning are considered. The systematic use of mobile devices is indicated in order to increase the efficiency of the educational process. The necessity of combining the usage of mobile devices with traditional methods of training is substantiated. Prospects for the study of this topic in the future are described.It is concluded that the introduction of mobile learning contributes to the implementation of personality-oriented foreign language learning, improving research skills of students, as well as the development of teamwork and cooperation skills, which will contribute to the formation of foreign language communicative competence of students

Mobile phone usage for M-Learning: comparing heavy & light Mobile Phone User

Purpose – Mobile technologies offer the opportunity to embed learning in a natural environment. The objective of the study is to examine how the usage of mobile phones for m‐learning differs between heavy and light mobile phone users. Heavy mobile phone users are hypothesized to have access to/subscribe to one type of mobile content than light mobile phone users, to have less frequent access to, subscribe to or purchase mobile content within the last year than light mobile phone users, and to pay less money for mobile learning, its content and mobile games than light mobile phone users. Design/methodology/approach – Data were collected from 436 respondents. An analysis of variance (ANOVA) test was run to examine how the usage of mobile phone for m‐learning differs between heavy and light mobile phone users in terms of access/subscription to several types of mobile content, frequency of access to, subscription to, and purchase of mobile content within the last year, and maximum amount of money paid for mobile learning, its content and mobile games. Findings – Significant differences can be identified when comparing the usage of mobile phones for m‐learning between heavy and light mobile phone users. It was found that heavy mobile phone users access/subscribe to more than one type of mobile content than light mobile phone users, have more frequent access to, subscription to and purchase of mobile content within the last year than light mobile phone users, and to spend more money on mobile learning, its content and mobile games than light mobile phone users. Research limitations/implications – Future research should aim at a deeper understanding of mobile phone usage for learning by including new variables and mediating variables and applying a multivariate analysis of data such as structural equation modelling to interpret the results, as this would allow for a simultaneous relationship among endogenous and exogenous variables, serve as a purposeful representation of the reality from which the data has been extracted, and provide a parsimonious explanation of the data. Practical implications – Mobile content needs to be developed specifically for mobiles, with clear images and good quality sound to enable users to continue to come back and enjoy new segments and features. Mobile phones must be small, reliable, and convenient devices that can provide the full spectrum of information and entertainment options to users. Originality/value – This research provides a new perspective on mobile phone usage for m‐learning among Malaysian mobile phone users

What Students Do While You Are Teaching – Computer and Smartphone Use in Class and Its Implication on Learning

The presence of mobile devices (e.g., smartphones, tablets and computers) in the classroom gives students the possibility of doing off-task activities during lectures. The purpose of this mixed-method field study was to learn more about students' behaviors, reasons, and opinions regarding such activities and their consequences on learning. This study is one of few to take a holistic view on this topic by taking the use of all technical devices in class into account and assessing its con-sequences on learning objectively. This is important to gain a full picture concerning the conse-quences of off-task activities in class. Right after a lecture, bachelor students (N = 125) answered a survey containing questions on their usage of mobile devices during this last class. Further-more, they took a test on the content of that lecture. Qualitative and quantitative analysis of data revealed that students spent an average of more than 19% of their time using a digital device for non-class purposes. Interestingly, this was not significantly linked with learning, although many students reported being aware of this behavior's potential negative consequences. But there was a significant negative link between the number of received notifications and learning. These results suggest that external interruptions have a stronger negative effect than internal interruptions, allowing us to make better recommendations on how to use electronic devices in the classroom

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

Mobile Learning for Communicative Language Teaching: An Exploration of How Higher Education Language Instructors Design Communicative MALL Environments

The purpose of this study was to describe how higher education language instructors design mobile-assted language learning environments for communicative language teaching. As our focus in second language acquisition has moved toward a communicative approach, the media richness and communication savvy of mobile devices can plan a vital role in this new communicative goal. Offering authentic content and dialogue opportunities, language instructors can take advantage of these devices leading language learners to achieve true fluency in another language. While the opportunity for communicative language teaching with MALL is viable, how to best design MALL environments for this purpose is still in its infancy. Answering questions regarding device type, application usage, theoretical foundations, and communicative task type and frequency will add to the richness of research for planning communicative MALL experiences. This study focused on two research questions. The first question explored how higher education language instructors design mobile assisted language learning environments. The second dealt with influences that ignite those decisions. Four higher education language instructors participated in this case study. Data consisted of semi-structured interviews, document reviews, and observations, and were analyzed using general qualitative analysis and the constant comparative method. Three themes emerged in the findings: (1) describing the communicative language learning environment enhanced by mobility, (2) meeting student, organizational, and instructional needs/goals, and (3) planning the implementation of MALL experiences for communicative language purposes. A discussion integrated these findings with interpretations in order to answer the research questions. The data suggested higher education language instructors identify goals, and create authentic learning experiences via communicative modes in order to achieve those goals. Further, the data suggested they have theoretical alignments with constructionism and situated learning, hold strong beliefs in CLT, and have beliefs aobut mobility that inform how they design communicative MALL environments

Investigating Acceptance of Mobile Applications toward English Language Learning: Based On Qualitative Judgments

The concept of mobile entertains the fact of knowledge availability “anywhere and anytime” which suggests that the future will witness multiple changes in the continuum of education in general, and specifically in its mobile format. Mobile Applications for English Language Learning facilitates the process of English learning via mobile devices. Thus, it provides practical solutions to slowly move from old face to face learning into mobile application based learning. This paper shed lights on the links between the English language learning and technology acceptance of mobile applications in academic context.  Teachers could encourage the use of mobile applications by providing activities that utilize mobile applications, such as searching for a word’s meaning, listening to authentic radio application content, listening to audio books, etc. since informal English can be present in most chatting applications, teachers could encourage the use of proper spelling, language, usage, etc. via interaction with their students in a language learning environment. In addition to that, the limitations of the study were addressed. Finally, this paper provided implications for instructional practice along with suggestions and recommendations that discussed possible advantages of acceptance of mobile applications in English language learning. The students participate in this investigation were ten who are studying in University Utara Malaysia. A WhatsApp group of these students, MAELL research, was intended to incorporate the members in the investigation. The data gathering strategies were interview, the WhatsApp group visit log and perception. This subjective contextual analysis looks into was spurred by two research questions: 1. How the mobile applications are useful for English language learning? 2. What are the factors of mobile application use for English language learning? The outcomes of this research study suggest that in addition to the current uses of mobile applications, there are also pros and cons of using mobile application to enhance English language learning skills, specially listening and speaking. The results, implications, and recommendations are also discussed

Driving e-learning towards ubiquitous e-learning

This paper reports the findings of a study that examined learners’ beliefs and actual usage of e-learning in an open and distance learning (ODL) environment. The constructs used include perceived usefulness, perceived ease of use, computer self-efficacy and anxiety. The study was based on 438 usable questionnaires completed by a random sample of learners from the Open University Malaysia (OUM). It was found that the learners were generally receptive towards e-learning, evidenced by their low computer anxiety and positive perceptions for perceived usefulness, perceived ease of use, computer self-efficacy and attitude towards e-learning. Learners also reported a reasonably high usage of various devices such as laptops, mobile phones, MP3/MP4 players and tablet computers for downloading study materials such as HTML modules, iLectures and iRadio learning segments. Through a series of regression analysis, the study found that learners’ perceived usefulness and ease of use, computer self-efficacy and anxiety had an impact on attitude towards e-learning. With regards to usage of e-learning, only perceived usefulness was found to be a significant factor. Learners also indicated that the top five most serious barriers to elearning were (i) technological and academic support, (ii) demand for time and effort , (iii) interface, navigation and platform problems, (iv) awareness of availability of the e-learning materials and (v) costs of devices and Internet access. In its drive to move the present e-learning to ubiquitous e-learning, among others, OUM will have to focus its efforts in reducing the impacts of these barriers and to improve further the usefulness of e-learning materials and technology. (Abstract by authors

The Dark Side(-Channel) of Mobile Devices: A Survey on Network Traffic Analysis

In recent years, mobile devices (e.g., smartphones and tablets) have met an increasing commercial success and have become a fundamental element of the everyday life for billions of people all around the world. Mobile devices are used not only for traditional communication activities (e.g., voice calls and messages) but also for more advanced tasks made possible by an enormous amount of multi-purpose applications (e.g., finance, gaming, and shopping). As a result, those devices generate a significant network traffic (a consistent part of the overall Internet traffic). For this reason, the research community has been investigating security and privacy issues that are related to the network traffic generated by mobile devices, which could be analyzed to obtain information useful for a variety of goals (ranging from device security and network optimization, to fine-grained user profiling). In this paper, we review the works that contributed to the state of the art of network traffic analysis targeting mobile devices. In particular, we present a systematic classification of the works in the literature according to three criteria: (i) the goal of the analysis; (ii) the point where the network traffic is captured; and (iii) the targeted mobile platforms. In this survey, we consider points of capturing such as Wi-Fi Access Points, software simulation, and inside real mobile devices or emulators. For the surveyed works, we review and compare analysis techniques, validation methods, and achieved results. We also discuss possible countermeasures, challenges and possible directions for future research on mobile traffic analysis and other emerging domains (e.g., Internet of Things). We believe our survey will be a reference work for researchers and practitioners in this research field.Comment: 55 page