Guideline-Based Evaluation and Design Opportunities for Mobile Video-based Learning

In successful mobile learning (ML) integration, factors associated with live-ware, software, and infrastructure are important. The main objective of this study is to investigate and model the influencing factors for learners and teachers at once to adopt ML in higher education. The proposed model consists of five impact factors: teacher, learner, mobile devices, ML tools, ML contents, communication technologies, and higher education institutes. Then the proposed model was implemented using a modified Moodle mobile application and evaluated using 60 teachers and 60 learners attached to the University of Kelaniya, Sri Lanka in 2021. According to the experimental research design approach, the proposed impact model was assessed as pre-test and post-test surveys using seven questionnaires. According to the Pearson correlation coefficient test, the most significant factor for learners and teachers to adopt ML is the mobile device. Learning content and communication technology were elected as the second most significant adoption factors for teachers and learners consecutively. However, higher correlation values were obtained for all factors denoting that they are greatly influenced the participant to adopt ML. The significant influencing factor of each impact factor was also investigated. In conclusion, it was recommended that featured smart devices, quality learning content, user-satisfied communication technology, academic enriched ML tools and higher education institutes with sound educational facilities are crucial for the university community to adopt ML in higher education. These findings help design academic community acceptable ML environments for higher education context.

A formative assessment-based mobile learning approach to improving the learning attitudes and achievements of students

Mobile learning is gaining popularity as it modifies the conventional definition of teaching and learning by using portable devices. In order to cope with identified limitations of mobile learning, while maintaining its transformational potential of using mobile technology in higher education, we proposed a blended mobile learning environment. In this study, we described the mobile blended learning environment and examined its applicability in theatre arts classrooms by presenting and discussing how mobile devices were used in three blended theatre arts classes at a comprehensive university in Macau. Survey and interview data showed that though challenges exist, the fusion of the multichannel teaching approach (m-learning, blended learning, in class/online lectures) has greatly enhanced teaching and learning experiences.

Mobile learning is currently receiving a lot of attention in the education arena, particularly within electronic learning. This is attributed to the increasing mobile penetration rates and the subsequent increases in university student enrolments. Mobile Learning environments are supported by a number of crucial services such as content creation which require an authoring tool. The last decade or so has witnessed increased attention on tools for authoring mobile learning content for education. This can be seen from the vast number of conference and journal publications devoted to the topic. Therefore, the goal of this paper is to review works that were published, suggest a new classification framework and explore each of the classification features. This paper is based on a systematic review of mobile learning content authoring tools (MLCATs) from 2000 to 2009. The framework is developed based on three broad dimensions i.e. Technology, Pedagogy and Usability and a number of features such as system type, development context, Tools and Technologies used, tool availability, ICTD relation, Multimedia support, tool purpose, support for standards, learning style support, intuitive Graphical User Interface and accessibility. This paper provides a means for researchers to extract assertions and several important lessons for the choice and implementation of MLCATs.

Though teacher conceptions of mobile language learning might affect their own language learning and their teaching practices, this is an area seldom examined. To address this paucity of research, fifty-nine students in an MA ELT (English Language Teaching) program, the majority of whom were in-service teachers, were investigated, via an open-ended survey, as to their understanding of mobile language learning and how they undertook mobile language learning and teaching. The results revealed that over eighty percent of the students understood mobile language learning as learning at any time and in any place or as the use of mobile technologies for learning. The participants mainly used mobile devices to practice their listening, reading and vocabulary. A cross-sectional analysis demonstrated that conceptualizing mobile learning as learning at any time and in any place is linked to the practices of listening, reading and online classes, while the conception of mobile learning as using mobile technologies is primarily associated with learning vocabulary, which indicates that conceptions of mobile learning are connected to learning practices. In terms of teaching, the participants reported mobile devices offered the most assistance via enhancing teaching effects, offering richer resources, helping with assigning homework, and communication with parents and students. The findings contribute to our understanding of teacher conceptions of mobile language learning and their teaching practices, which bear significant implications for mobile pedagogy and teacher education.

The purpose of the present study was to design a mobile learning environment that enables the use of a teleconference application used in simultaneous e-learning with mobile devices and to evaluate this mobile learning environment based on students‘ views. With the mobile learning environment developed in the study, the students are able to follow a teleconference application realized by using appropriate mobile devices. The study was carried out with 8 post-graduate students enrolled in Karadeniz Technical University (KTU), Department of Computer Education and Instructional Technologies (CEIT), Graduate School of Natural and Applied Science. The students utilized this teleconference application using mobile devices supporting internet access and Adobe Flash technology. Of the 8 students, 4 accessed the system using EDGE technology and 4 used wireless internet technology. At the end of the application, the audio and display were delayed by 4-5 seconds with EDGE technology, and were delayed by 7-8 seconds with wireless internet technology. Based on the students‘ views, it was concluded that the environment had some deficiencies in terms of quality, especially in terms of the screen resolution. Despite this, the students reported that this environment could provide more flexibility in terms of space and time when compared to other simultaneous distance education applications. Although the environment enables interaction, in particular, the problem of resolution caused by screen size is a disadvantage for the system. When this mobile learning application is compared to conventional education environments, it was found that mobile learning does have a role in helping the students overcome the problems of participating in learning activities caused by time and space constraints.

Whether online or in a classroom, learners have long suffered from a lack of interactivity, several mobile learning system provide recorded lectures, which only enhance passive learning. Thus and in order to improve interactivity and motivation into the learning process, we propose in this paper a conceptual framework to be efficiently integrated into the learning process. This framework describes the design requirements of a mobile learning environment that can deliver live broadcast of real-time classroom teaching to online student with mobile devices. It suggests five perspectives: generic mobile environment, mobile learning context, learning experience, social learning strategy and learning objectives. It also clarifies how to apply these last perspectives and build a mobile learning application efficiently.

The article describes the design of a mobile workplace learning tool for trainers of the International Training Centre of the International Labour Organization. The motivation behind is to provide trainers with a practical tool that will enable them to recall information at the moment of need and continue to learn in context. On this purpose a set of visual augmented reality cards was created, taking into consideration the fundamental mobile learning and usability principles. The nature of the article is empirical as it reports an experiment carried out with trainers which aimed at testing cards usability and learnability. Results show that the integration of both online and offline strategies was perceived as an added value as trainers could choose to retrieve information as they mostly like; finally, it also resulted in high usability scores, an aspect that contributes to their effectiveness at the workplace.

In order to manage the demands of university studies, research suggests that university students should possess efficient self-regulated learning (SRL) skills. The use of mobile learning is believed to enhance SRL skills. Yet, despite the potential benefits of mobile learning, many first-year students encounter challenges in using mobile devices to foster self-regulated learning skills. It is against this backdrop that this study aimed to examine how first-year medical students utilisee mobile learning to foster self-regulated learning. This study employed a Explanatory Sequential Study Design , combining both quantitative and qualitative data. It used the interpretativist and pragmatic paradigms to understand how students perceive and use mobile devices for self-regulated learning. The interpretivist paradigm focused on understanding students’ perspectives, while the pragmatic paradigm aimed to identify effective ways of using mobile devices to promote self-regulated learning. Participants to this study were first-year medical students. The mixed-methods approach aimed to provide a comprehensive understanding of the research problem by collecting data through various methods. Questionnaires gathered quantitative data, while participatory observation, reflective workshops, and focus group discussions provided qualitative data. The theoretical frameworks that guide the study include the Unified Theory of Acceptance and Use of Technology model (UTAUT) (Venkatesh et al., 2003) and Vygotsky's (1978) socio-cultural theory (SCT). The study findings revealed that mobile devices were used by first year medical students to manage and control their learning, both inside and outside the classroom. Notably, significant improvements were observed in cognitive learning strategies, metacognitive self-regulation strategies and resource management learning strategies, including time management. Additionally, the study found that first-year medical students had a positive perception of mobile learning. Furthermore, mobile devices were generally perceived as enablers of self-regulated learning (SRL), providing flexibility, convenience, and access to diverse learning resources. However, distractions and interruptions posed challenges to focus and attention. To mitigate these challenges, strategies such as device management and setting boundaries were suggested. Moreover, first-year medical students expressed the need for support in developing SRL through mobile learning. Guidance in selecting effective mobile applications, opportunities for collaborative learning, and technological skill training were identified as essential forms of support. The study thus recommends integrating mobile learning into curricula to enhance self-regulated learning skills in higher education students, especially among first-year students. Higher education institutions should establish deliberate policies and initiatives for mobile learning programs, especially for first-year students. Educators should offer guidance and training on using mobile learning tools effectively, including information searching skill. However, students should be cautious about distractions from the use of mobile devices.

In this paper, the authors present their proposal for adaptation of educational contents of learning objects to a particular mobile device and a specific learner. Content adaptation in mobile learning objects implies user adaptation and device adaptation, and requires additional metadata categories in comparison with SCORM 2004. This learning object content model, ALMA (A Learning content Model Adaptation), inherits from the SCORM standard a subset of metadata categories, and extends it with three top level metadata categories for content adaptation, i.e., Knowledge, Use, and Mobile Device Requirements (Castillo & Ayala, 2008). For user adaptation, the authors developed NORIKO (NOn-monotonic Reasoning for Intelligent Knowledge awareness and recommendations On the move), a belief system based on DLV, a programming system based on Answer Set Programming paradigm. For device adaptation the authors designed CARIME (Content Adapter of Resources In Mobile learning Environments), which uses transcoding and transrating to adapt media content to suit the device characteristics.

Supporting efficient data access in the mobile learning environment is becoming a hot research problem in recent years, and the problem becomes tougher when the clients are light-weight mobile devices such as cell phones whose limited storage space prevents the clients from holding a large cache. A practical solution is to store the cache data at some proxies nearby, so that mobile devices can access the data from these proxies instead of data servers in order to reduce the latency time. However, when mobile devices move freely, the cache data may not enhance the overall performance because it may become too far away for the clients to access. In this paper, we propose a statistical caching mechanism which makes use of prior knowledge (statistical data) to predict the pattern of user movement and then replicates/migrates the cache objects among different proxies. We propose a statistical inference based heuristic search algorithm to accommodate dynamic mobile data access in the mobile learning environment. Experimental studies show that, with an acceptable complexity, our algorithm can obtain good performance on caching mobile data.

With increasing permeation of mobile devices into people’s everyday life, m-learning can be regarded as the next generation of e-learning. Learning via mobile devices, especially mobile phones, is practiced in educational areas all over the world. This paper describes a mobile language learning project which creates English learning materials and delivers the learning materials to Japanese university students. The data collected from this ongoing mobile language learning project allows us to investigate Japanese students’ mobile learning styles and learning material preferences as well as their concerns about using mobile learning. The data was collected from various sources: online surveys, server logs, user registered personal information, interviews and actual online quiz results. The findings obtained from this research provide useful information for future mobile learning project/system design, implementation and content delivery.KeywordsMobile learning styleslearning preferenceslearning expectationsfindingsfuture mobile learning designJapan

The topic includes four main themes: (1) The Collaborative Work in Cloud Storage Services: The collaborative work is seen as a force for the individual and community. It, in the field of education, expresses the interaction among students of individual differences who work within collaborative aims and skills to achieve a specific aim. In addition, cloud storage predicts a tremendous change in the way information is stored and applications are run. That is, instead of storing information and running programs on PCs, everything will be hosted in a cloud that can be accessed anywhere and processed by addition or deletion collaboratively. (2) Computer- supported collaborative learning environment (CSCL): Collaborative learning is an umbrella term for a variety of educational approaches involving joint intellectual effort by students, or students and teachers together. It is based on the idea that learning is naturally a social act in which the participants talk among themselves. A group of students engaged in collaborative learning works together to achieve shared goals. (3) Mobile learning: Mobile learning is a term that has been used widely in different places all over the world. it has been encouraged to be used in higher education institutions because of a set of factors such as the availability of mobile phones, their ability to motivate students, and the freedom and privacy they provide to share information. Mobile learning is defined as E-learning that uses mobile devices or learning connected to a mobile device, Laouris & Eteokleous. (4) Open-Source Learning Management Systems: The integration of many Educational technologies in education have been widely promoted for their potential to enrich, enhance and extend student-learning experiences. Hence, pioneer educational establishments all over the world try to benefit of these technologies as much as possible to convey knowledge resources to both of the learner and teacher in least time, effort and cost. One of these educational technology tools which has been prominent in the field of education and technology integration is Learning Management Systems known as LMS.

As the mobile learning becomes a viable learning method, it is a sub-category of E-learning. Mobile learning focuses on the use of wireless Internet access in the classroom and other learning spaces. So students can learn anytime, anywhere in mobile learning environments. And language learning is a cumulative process, it needs repeated practice to enhance memory. There for, mobile learning is very suitable for learning foreign language. The paper analyzes the features of mobile learning and foreign language learning, then discusses the application of mobile learning in foreign language learning.

Mapping primary students’ mobile collaborative inquiry-based learning behaviours in science collaborative problem solving via learning analytics