A Design Requirements Framework for Mobile Learning Environment

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.

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

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.

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.

This research study aimed to synthesize theoretical framework and designing of constructivist mobile application learning environment to foster creative thinking on basic photography skill for high school students. The target group consisted of 3 expert reviewers for content, mobile application and learning environment designing. Research methodology is developmental research; developmental research consisted of 3 processes: (1) designing process, (2) developing process, (3) evaluate process. The procedures were as follows: (1) to examine and analyze the principles and theories, (2) to synthesize theoretical framework, and (3) to synthesize designing framework. The result revealed that: 1) to synthesize theoretical framework comprise of 6 components as following that. Contextual base, Psychological base, Pedagogies base, Creative Thinking base and Technologies and media base. 2) To synthesize design framework of mobile application learning environment to foster creative thinking of 6 components i.e., (1) Problem base, (2) Resource, (3) Creative Thinking Photo Lab, (4) Collaboration, (5) Coaching, (6) Scaffolding. The efficiency of this learning environment was evaluated by expert review. It was found that the learning environment is appropriate on 3 aspects: content, mobile application learning design, and learning environment design..

Mobile devices are becoming increasingly pervasive within the higher education student community and correspondingly mobile learning applications are increasingly popular as part of the learning process. The purpose of this study was to investigate engagement with, and effectiveness of, different avatar interface types (text, image, audio, video and cartoon) when used within a mobile learning context, and specifically, when used to teach the English language courses mandatory within Saudi degree programmes. One hundred fifty six students from Albaha University participated in this study which involved students undertaking mini lectures delivered onto their mobile devices via each of the five avatar types. The students completed pre/post questionnaires about their perceptions and experiences of mobile learning and undertook a short test to assess their performance and retention of information after the mini-lectures. Results revealed that the majority of students rated the convenience of using m-learning as being very important and showed that they possessed the required resources and underlying technical experience required to adopt m-learning. Results also showed that whilst female students preferred to learn from video or cartoon interfaces they performed best with cartoon and audio interfaces, whilst male students preferred the video interface yet performed better with the cartoon interface.

Learners consume video-based learning content on various mobile devices due to their mobility and accessibility. However, most video-based learning content is originally designed for desktop without consideration of constraints in mobile learning environments. We focus on readability and visibility problems caused by visual design elements such as text and images on varying screen sizes. To reveal design issues of current content, we examined mobile learning adequacy of content with 681 video frames from 108 video lectures. The content analysis revealed a distribution and guideline compliance rate of visual design elements. We also conducted semi-structured interviews with six video production engineers to investigate current practices and challenges in content design for mobile devices. Based on the interview results, we present a prototype that supports a guideline-based design of video learning content. Our findings can inform engineers and design tool makers on the challenges of editing mobile video-based learning content for accessible and adaptive design across devices.

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.

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 the rise of mobile learning platforms, it has become increasingly evident that individuals require personalized experiences that are tailored to the strengths and limitations of mobile devices. The present study explores the significant impact that personalized mobile learning environments, powered by artificial intelligence (AI), could have. This study specifically evaluates the impact of an AI-driven personalized educational platform, designed for mobile devices, on the academic achievement and educational progress of students at Mohammed VI Polytechnic University. The platform, designed for mobile devices, allows instructors to easily upload information. Learners can interact with an AI mentor through a chat interface that is seamlessly integrated into their mobile course materials. The system, constructed using cutting-edge technologies such as Langchain, Pinecone, and the LLM Model, excels at providing personalized, real-time feedback and support for learners who are frequently mobile. This study compared two groups of students. One group had access to a mobile personalized learning platform powered by AI, whereas the control group did not have access to it. We conducted a comparative analysis of mobile educational experiences, levels of engagement, and academic outcomes across these groups. In addition, qualitative feedback was gathered from educators and students to evaluate the mobile usability and effectiveness of the system. The results of our study demonstrate that the AI-driven mobile-tailored learning system significantly improves the experience of mobile learners. The increased levels of engagement, improved understanding, and superior academic achievements support our claim. This study not only supports the potential of AI-driven personalized mobile learning in higher education but also emphasizes the importance of continuous innovation to improve its usefulness and effectiveness.

With the rapid development of the Internet, mobile learning environment is rapidly and profoundly affecting the learning behavior of college students in the new era. This paper investigates the current situation and problems of college students' learning behaviors under the mobile learning environment by using a combination of questionnaire survey and interview with undergraduates in a certain university in a certain region as the research object. The survey results show that from the learning behavior of mobile learning environment, it can be seen that college students have a great demand for online learning, but most of them have not found a reasonable way of learning, resulting in low learning efficiency. In view of the existing problems, the author proposes corresponding online learning strategies, so that college students can exert greater learning potential in the mobile learning environment.

With the popularity of mobile devices, learning through mobile device proliferated in the education sector. Now a day's mobile devices and mobile technologies are advancing rapidly. Therefore, mobile learning surpassing all other educational servicing methods. On the other hand, human life style averts stagnated education and demanding synchronous mode education such as learning while working. In this study we set out to look-over influencing factors on mobile devices for applicable and sustainable mobile learning environment. In this study we propose an impact model consists six influencing factors i.e. screen size, supportive software, screen zooming, video playback control, touch screen keyboard, and language predictive tools. 120 university teachers and learners with equal portion in each category participated on the model evaluation through pre and post survey questionnaires. Initially they faced pre survey questionnaire with their prior mobile learning experience and finally took the post survey questionnaire after successfully working on the Moodle mobile app modified by first author. The results revealed that the most significant impact factor in the proposed model was `screen zooming'. Hence, when designing applicable and sustainable mobile learning environment it should be considered that, the screen zooming as the most significant mobile device feature for learner and teacher to adopt in mobile learning.

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

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.