M-learning System Research Articles

AHP-Based Evaluation Model for Context-Aware Mobile Learning System

There has been a dramatic growth of research concerned about the mobile learning (m-learning) in recent years. It has an urgent need that M-learning systems have to be continuously evaluated and improved for ensuring the system reliability. However, there are only few or none of existent m-learning system evaluation researches. Therefore, this study develops a m-learning evaluation method supporting meaningful learning. According to the principles of meaningful learning and m-learning, the study blends the characters of them to form five dimensions and ten criteria based on which the evaluation method is developed. The evaluation method adopts both the statistics and the analytic hierarchy process (AHP) to check the degree of meaningful learning that includes overall and inner aspects. Initially, the statistics approach can be treated as entirety analysis to develop a scale of meaningful learning for m-learning which can present the overall degree of meaningful learning. Subsequently, the AHP method can be an inner inspection to collect learners' opinions after practicing learning activities and then the results could reveal the related importance of functions in m-learning. According to the overall and inner analysis, m-learning system developers and designers can realize the strength and weakness of the m-learning system. Consequently, existing m-learning systems can be reassessed by our evaluation method and m-learning system can be improved toward the meaningful learning according to the results of the evaluation method.

Towards a Model for Evaluating the Usability of M-learning Systems: from a Mapping Study to an Approach

This paper presents the analysis of recent research on mobile learning and usability areas, applying a systematic mapping study. It also presents the main focus adopted by each publication, the types of mobile device and operative systems. The aim is also to understand the tendencies and needs in the fields of design and evaluation of the m-learning systems. Results demonstrate that research in the area has grown significantly since the 2012, and due to the recent boom of mobile devices in education, we believe that the number of studies will continue to grow in the following years. Also we identify a necessity when we see that not all the m-learning applications have used usability tests, we did not find guidelines or frameworks to evaluate them either. With this results and tendencies, we propose the creation of a model for evaluating the development phases in m-learning applications, considering pedagogical usability, student´s experience and user interface usability, to improve the quality of m-learning applications. Within this proposal we have also created a framework that supports different stages of our model.

Development and testing of a m-learning system for the professional development of academics through design-based action research

<p>In the present study, a mobile learning system for the professional development of academics was developed by design based action research, and the perceptions and experiences of the academics using this system were examined. In the first phase of this design-based action research, the research question was defined. In the second phase, a m-learning system called “Mobile Academic Research Support” (MARS) was designed as a solution to the problem, and the IOS mobile application for this design was developed. In the third phase of the study, the MARS application was regularly tested and evaluated by the academics over eight weeks. At the end of the research process, the results were reflected upon. It was found that the primary and important professional development needs of the academics were at the scientific research level. It was also observed that the m-learning system developed for the professional development of the academics regarding scientific research was appropriate to the overall purpose, accessible, adaptable and appealing; that it served both as a m-learning and as an academic support system; that its content was satisfactory; and that the tools used in the system were useful. In addition, it was observed that the academics were able to use mobile technologies for learning. Also, it was stated that such a system could provide positive contributions to the professional development of academics. </p>

Design and Implementation of a Content Model for m-Learning

It is difficult for mobile learners to maintain a high level of concentration when learning content for more than an hour while they are on the move. Despite the attention span issue, many m-learning systems still provide their mobile learners with the same content once used in e-learning systems. This has called for an investigation to identify the suitable characteristics of the m-learning environment. With this in mind, we have conducted a survey in hopes of determining the requirements for developing more suitable m-learning content. Based on the results of the survey, we have developed a content model comprised of two types: a segment type and a supplement type. In addition, we have implemented a prototype system of the content model for Apple iPhones and Android smartphones in order to investigate a feasibility study of the model application.

An m-learning framework for ERP systems in higher education

Purpose – The purpose of this paper is to investigate the factors of m-learning approaches which can be used for enterprise resource planning (ERP) system training and to propose a theoretical framework for m-learning of ERP systems. Design/methodology/approach – A literature review of several theories relating to success factors for mobile learning (m-learning) and electronic learning (e-learning) are analysed and a theoretical framework of success factors for m-learning of ERP systems is proposed. Two field studies are undertaken to identify the features of e-learning and m-learning systems which users enjoyed and which related to the factors identified in the theoretical framework. The technology acceptance model (TAM) was used to evaluate the acceptance, usefulness and perceived ease of use (PEOU) of the two systems evaluated in the field study, the openSAP e-learning application and the SAP Learn Now m-learning application. Findings – The results confirmed several of the theoretical elements identified in the framework and the m-learning system was rated positively for PEOU and perceived usefulness (PU). The findings confirmed other studies showing the importance of the quality of course content in e-learning and m-learning projects. Research limitations/implications – The empirical study was limited to a small number of participants in higher education. However, a deeper understanding of the factors influencing m-learning for ERP systems was obtained. Practical implications – The study provides a valuable practical contribution because the framework can be used in the improved design of an ERP m-learning approach, which in turn can lead to an improvement in ERP training and education programmes and ultimately ERP project success. Originality/value – Several studies propose the use of m-learning systems. However, research related to the factors impacting on m-learning projects for ERP system training is limited. The paper presents original work and the results provide a valuable contribution to several theories of m-learning.

The Design of the M-Learning System Simulation Model Based on Streaming Media Technology

In this years, the human society has entered information period . many aspects of human life has changed , the traditional teaching system and teaching model are facing the challenge of the transform , so the M-learning has become a new learning style . Nowadays, the Mobile Learning has based on the mobile Intern technology and mobile communication technology . So this stylize is very popular .

The Effectiveness and Students’ Perception of an Adaptive Mobile Learning System based on Personalized Content and Mobile Web

As the whole world is going mobile, application of mobile devices in education, also known as m-learning, is becoming one of the most popular areas of educational research. This paper presents the implementation and evaluation of the effectiveness and students’ attitudes toward an adaptive mobile learning system based on personalized content and mobile web. Personalization of learning materials is based on the Felder-Silverman learning style model and the features of the accessing mobile device were identified using the device library. The results of the study confirm students’ positive attitudes toward mobile learning and the developed adaptive m-learning system. They also prove the effectiveness of the system and m-learning as an additional educational tool in terms of increasing students’ knowledge and scores.

Open Source Based M-Learning Application for Supporting Distance Learning

The concept of learning anywhere and anytime is unable to be fully implemented because user need to find comfortable place, which is required, to follow the study. User is unable to access learning material in a way that practical, easy to use and easy to access. Based on that fact, system or application for mobile device such as hand phone, PDA or smart phone is needed.The m-learning application is developed by Computer-Based System Engineering Method based on the principal of software engineering using development steps of System Development Life Cycle (SDLC). SDLC consists of the following steps, analysis, design, implementation, testing and evaluation. Trials as alpha-test, carried out to determine operability of m-learning system on handheld devices. Trial is taken by creating trial class followed by 25 students to receive feedback about the m-learning system. Feedback result shows almost 90% of respondents consider the m-learning system is easy to use.

Collaborative M-Learning Adoption Model: A Case Study for Jordan

This work investigates university students’ acceptance and readiness for adopting collaborative and context-aware mobile learning services. An acceptance evaluation study was conducted to identify challenges affecting successful implementation and adoption of collaborative m-learning system. The acceptance study has focused on learning contextual factors and learners requirements available at developing countries, where Jordan was considered as the case of this research. Results have confirmed that learning style, mobile device capability and perceived ease of use are having the most positive contribution towards learners’ behavior to use collaborative m-learning services. In light of the achieved results, this work provides a new user acceptance model focused toward the adoption of collaborative m-learning services. Finally, this research draws fundamental recommendations allowing for learning context adaptation and successful collaborative m-learning services implementation.

Designing Ensemble Based Security Framework for M-Learning System

Mobile Learning has a potential to improve efficiency in the education sector and expand educational opportunities to underserved remote area in higher learning institutions. However there are multi challenges in different altitude faced when introducing and implementing m-learning. Despite the evolution of technology changes in education, unfocused issues are security management even though statistically proven; threats are increasing each day on mobile application and ensemble devices. In order to provide a secure guideline for m-learning platform, an ensemble based security framework for mobile learning is proposed and improved. One of the major benefits in the framework is it integrates the security with dependability to provide trustworthiness in learner and providers perspective.

Individual Syllabus for Personalized Learner-Centric E-Courses in E-Learning and M-Learning

Most of e-learning and m-learning systems are course-centric. These systems provided services that concentrated on course material and pedagogical. They did not take into account varieties of student levels, skills, interests or preferences. This paper provides a design of an approach for personalized and self-adapted agent-based learning systems for enhancing e-learning and mobile learning (m-learning) services to be learner-centric. It presents a modeling of goals of different learners of a corporate training in computer courses in an educational institute. It figures how to customize and personalize learning paths (course syllabus) for e-learning and m-learning platforms. The delivering of e-courses become personalized learner-centric, which improves learning outcome, satisfaction of learners and enhances education.

Factors influencing students’ acceptance of m-learning: An investigation in higher education

<p>M-learning will play an increasingly significant role in the development of teaching and learning methods for higher education. However, the successful implementation of m-learning in higher education will be based on users’ acceptance of this technology. Thus, the purpose of this paper is to study the factors that affect university students’ intentions to accept m-learning. Based on the unified theory of acceptance and use of technology (UTAUT) (Venkatesh et al., 2003), this study proposes a model to identify the factors that influence the acceptance of m-learning in higher education and to investigate if prior experience of mobile devices affects the acceptance of m-learning. A structural equation model was used to analyse the data collected from 174 participants. The results indicate that performance expectancy, effort expectancy, influence of lecturers, quality of service, and personal innovativeness were all significant factors that affect behavioural intention to use m-learning. Prior experience of mobile devices was also found to moderate the effect of these constructs on behavioural intention. The results of this research extend the UTAUT in the context of m-learning acceptance by adding quality of service and personal innovativeness to the structure of UTAUT and provide practitioners and educators with useful guidelines for designing a successful m-learning system.</p>

Intelligent Delivery of Multimedia Content in Mobile Environment

Process of delivering multimedia learning objects such as text, video, images, audio, and animation requires real-time streaming of multimedia content from the M-learning systems to the mobile users. With enhancing the integration methods of the mobile devices into the learning systems, we have possibility to allow easy storin g, recording and delivering of multimedia contents to the students in real-time. In this paper, we will present an intelligent mobile lea rning (M-learning) system for delivery of multimedia content depending of the context-aware conditions. This paper considers the significance of high QoS requirements for applications that are essential to achieve higher continuity of real-time multimedia contents . In this research, we have considered the best queuing packet delivery algorithm to provide appropriate multimedia contents to improve required bandwidth for user's communications.

Mobile Internet Oriented M-Learning System

In recent years, the mobile internet is deployed rapidly in large-scale. Meanwhile the smart mobile devices are penetrated universally. The combination of them provides the sufficient precondition for the Mobile Learning, i.e. M-Learning. A revolution on future learning foreseen because of the M-learning, characterized with mobility, convenience, timeliness and other characteristics, enabling anywhere, anytime learning for anybody via smart devices in order to easier access information, flexible attend classes, and freely join discussion, etc. This paper analyzes the key elements and characteristics of mobile Internet-oriented mobile learning system, provides the framework of M-Learning system functionality architecture for core applications, and furthermore focuses on some technical key issues including knowledge aggregation/mashup and information pushing for mobile learning.

Qoe Model for Multimedia Content Delivery from Mcloud to Mobile Devices

Integration of mobile devices in the m-learning systems provides the learner with scaffolding outside the classroom, it allows them to easily store, record and deliver multimedia content in real-time. The process of delivering multimedia learning objects (rich text, video, images, audio, animation, etc.) from the mlearning systems to the users requires more computational resources than mobile devices can provide. Considering the existence of different kinds of mobile browsers, which have limited support for HTML plugins (Flash, Java and Silverlight), we face the challenge to deliver the adapted multimedia contents in the interactive m-learning system. Installation of add-ons to the mobile browsers is just a provisional solution that is not always possible on each mobile device. Generally, the m-learning systems presented on the mobile device that not provide rich multimedia information leads to a degraded learning experience. In order to provide users with multimedia content that is suitable for their mobile devices and according to their needs we introduce the mobile cloud computing environment as paradigm that is ideal to overcome these problems. The proposed interactive mCloud system should provide high scale collaboration and interaction between the professor and students, in direction of increasing the quality of learning. The main focus is the delivery of multimedia learning objects to the users depending on the student’s cognitive style and adapting the content in accordance with the context-aware network conditions. The main task within this paper is to design a QoE model for estimating the multidimensional metric based on multimedia content adaptive features.

Design and Development of m-Learning Service Based on 3G Cellular Phones

Abstract —As the knowledge society matures, not only distant, but also off-line universities are trying to provide learners with on-line educational contents. Particularly, high effectiveness of mobile devices for e-Learning has been demonstrated by the university sector, which uses distant learning that is based on blended learning. In this paper, we analyzed previous m-Learning scenarios and future technology prospects. Based on the proposed m-Learning scenario, we designed cellular phone-based educational contents and service structure, implemented m-Learning system, and analyzed m-Learning service satisfaction. The design principles of the m-Learning service are 1) to provide learners with m-Learning environment with both cellular phones and desktop computers; 2) to serve announcements, discussion boards, QA and 3) to serve learning activities like the reviewing of full lectures, discussions, and writing term papers using desktop computers and cellular phones. The m-Learning service was developed on a cellular phone that supports H.264 codex in 3G communication technology. Some of the functions of the m-Learning design principles are implemented in a 3G cellular phone. The contents of lectures are provided in the forms of video, text, audio, and video with text. One-way educational contents are complemented by exercises (quizzes)

Opinion Mining of M Learning Reviews using Soft Computing Techniques

Internet has increasingly become the place for online learning, and exchange of ideas. The rapid development in wireless technology offering fast data transfer has lead to mobile device revolution. With the ease of access of mobile devices like mobile phones, PDAs, tablet PCs and high bandwidth through wireless, there is an upsurge of mobile learning or Mlearning. It is important to know the opinion of users using mlearning platforms for developing and fine tuning of Mlearning systems. The sheer volume of reviews found in the internet blog spot, bulletin board makes it difficult to track and understand customer opinions. Opinion mining also known as sentiment mining is an area of research which attempts at determining the opinion underlying a text written in natural language which summarizes the customer reviews and express whether the opinions are positive or negative. In this paper, we investigate the classification accuracy of machine learning algorithms for opinion mining of Mlearning system review.

Distance Learners’ Needs on Interactivity in SMS-based Learning System

The literature points to the rise of mobile learning (m-learning) adoption in some higher education institutions in Malaysia. However, the designs of m-learning system in each institution are diverse and may not be carefully designed up to learners’ expectation. Needs on interactivity is one aspect to be considered. This study examined the extent to which interactivity is viewed as a key element in designing an SMS-based m-learning system from the perspectives of distance learners. A purposive sample of 61 responses from distance learners from Universiti Sains Malaysia (USM) was analyzed. The results attested that in general, interactivity was viewed as important in their learning process. Specifically, interaction between students and lecturer was mostly preferred by the students, not only for learning communication, but also as a support to the SMS (Short Message Service)-based learning system. Findings in this study are of interest to distance educators and course designers interested in exploring the interactive elements of SMS-based learning applications.

Pilot Project of M-Learning

Abstract The article offers an insight into specific features of mobile e-learning (m-learning) in terms of technical implementation, available platforms and their related problems, and evaluates the practical experience. It also summarizes reasons for introduction of mobile learning and analyzes available options for its technical implementation. Specific example is used to demonstrate practical use of m-learning courses - learning of English language for a defined group of recipients within an international Leonardo da Vinci pilot project “M-Learning for the Young People (Students) at Risk Groups (MLARG)”. Substantial part of the text is dedicated to the analysis of typical functions available in an m-learning system and to the discussion about its advantages, disadvantages and limitations, mainly in relation to the properties and features of conventional e-learning environments. The expected development of m-learning technologies is outlined in the context of modern learning method.

The Integration of m-Learning and Social Nework for Suporting Knowledge Sharing

Mobile learning (m-Learning) is a type of a learning system that combines mobile, computer, and modern educational technologies to facilitate teaching and learning. Social network use Web 2.0 technology to communicate, share and searching information for the students. This paper presents the integration of m-Learning and social network design for supporting the sharing of knowledge. The m-Learning system was developed from IPTV (Suan Dusit Internet Broadcasting: SDIB) to mobile IPTV and extended to m-Learning systems. The prototype was designed by integrating m-Learning system (contents and communication tool) and social networks that support knowledge sharing of the learners. The prototype has been used for teaching and learning and students evaluated the system. The result found that the m-Learning system was useful in helping learners to share knowledge and create social interaction.