M-learning System Research Articles

Adoption of mobile application for enhancing learning in higher education: Students’ views from the State University of Zanzibar, Tanzania

Mobile (m)-learning is a technology-enhanced learning approach utilizing mobile technologies to facilitate learning. Despite significant attention from previous researchers, there is a lack of adequate studies about student’s usage and perceptions of m-learning in Zanzibar’s’ higher learning institutions (HLIs). In this study, the researchers explored the specific activities undertaken by students through the m-learning system, the students’ perceptions on the usefulness and ease of use of m-learning system, and the challenges they faced at the State University of Zanzibar (SUZA). The study used the survey method with descriptive statistical analysis and examined 240 randomly selected participating in the m-learning initiative. The findings indicate that 96.7% of participants easily used the system to navigate learning resources, 92.9% viewed assignments, and 94.6% took quizzes. Additionally, the majority of the respondents showed a positive attitude regarding the usefulness and ease of use of the system. However, 57% of participants experienced challenges, including unreliable internet connectivity and system incompatibility with some of their devices. The findings of this study will help HLIs like SUZA to find better strategies to improve m-learning, especially pedagogical features and awareness among students. Moreover, the study underscores the importance of more research to discover factors that influence m-learning adoption in Africa HLIs.

A Universal Design for an Adaptive Context-Aware Mobile Cloud Learning Framework Using Machine Learning

Mobile learning is becoming more and more popular today. It gained popularity recently due to the COVID-19 pandemic restrictions in 2020. However, to provide learners with appropriate educational materials in such a mobile environment, the characteristics and context of the learners must be considered. Therefore, in this paper, we propose a framework for providing an adaptive context-aware learning process considering a combination of student learning models and principles of Universal Design for Learning (UDL). The proposed system consists of components capable of detecting changes in context and adapting the way the application responds and behaves. The framework uses a machine-learning algorithm to predict learners’ characteristics and follow UDL principles to deliver enriched user experience and location-aware content and activities. An online survey was conducted with 20 undergraduate students. We analyzed their levels of satisfaction with the proposed m-learning system. From the analyzed data, we noticed that the average rating values are close to 4.5, which indicates that the proposed m-learning system complies with UDL principles and provides an adaptive and localized learning environment, thus enhancing the efficiency of the learning process and experiences. The study also investigated the impact of factors (i.e., noise level, physical activity, and location) on learners’ concentration towards the learning process. The results show that these factors have a significant impact on the learner’s concentration level.

The integrated social cognitive theory with the TAM model: The impact of M-learning in King Saud University art education.

Technology in higher education now includes a substantial amount of mobile learning (M-learning). M-learning also enables students to use the internet and technology for research, teamwork, and idea sharing. Additionally, in order to use M-learning systems, both students and teachers must accept M-learning. However, not enough research has been done in Saudi Arabia to determine how satisfied students are with their real use of mobile learning for educational purposes. As a result, the current study intends to investigate students' behavioral intentions to utilize mobile learning, their happiness with the technology, as well as their impressions of how they actually use mobile learning systems. Therefore, this study aimed to develop a new model by integrating social cognition theory and the technology acceptance model to better understand the elements that influence the adoption of mobile learning in higher education (TAM). The majority of the information was gathered through a survey, with 412 university students' randomly assigned questionnaires. The data analysis tools utilized were SPSS and Smart-PLS3.3.3. The studies proposed research model could, according to the study's findings, account for 52.5% of the variation in how mobile learning systems were actually used. This information is crucial for understanding how social and educational technology factors affect the actual use of mobile learning systems. With only two hypotheses being rejected, this study created a new model that supported 16 of them. The findings indicated a beneficial relationship between 10 social and educational technology elements. The findings also indicated a favorable impact on students' behavioral intentions to use and student happiness, which favorably impacts the actual use of M-learning in higher education. In order to improve students' academic performance via mobile learning, social cognitive theory and the TAM model are combined as a consequence of the study's empirical results. Therefore, we encourage students to collaborate with their colleagues at higher education institutions and use M-learning in their classrooms.

Learning with smartphones: the acceptance of m-learning in higher education

PurposeIn parallel with technological developments, mobile devices have become an important part of our daily lives. Nowadays, people, particularly generation Z, actively engage with the Internet and mobile technologies, including smartphones. The new technologies have also made electronic learning (e-learning) and mobile learning (m-learning) tempting for learners in higher education. Despite the growing use of m-learning, particularly in developed countries, its utilization in higher education is still at its early stage and far from its potential. This study investigates the university students' acceptance of using m-learning with smartphones in a developing country context.Design/methodology/approachQuantitative data collected through conducting questionnaires with 405 higher education students in Turkey were analyzed with structural equation modelling.FindingsThe findings show that the acceptance of using m-learning is affected by several factors, including perceived enjoyment, complexity and facilitating conditions. The findings are particularly relevant in the current context, where the COVID-19 pandemic has pushed many higher education institutions to adapt m-learning systems to enhance student learning experiences.Originality/valueThis study's findings offer fresh and important insights that can be used by m-learning developers and educators for designing m-learning systems and using m-learning applications in enhancing students' experience and performance with m-learning.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-10-2021-0516.

Investigating lecturers’ reasons for adoption of mobile learning in higher education: A case study


 
 
 The usage of Mobile Learning (M-learning) has grown among Institutes of Higher Learning worldwide as mobile technologies benefit teaching and learning. Despite this interest, there is a lack of M-learning research in formal educational contexts and the reasons for lecturers to adopt M-learning in a Singapore private education institute have been limited. Furthermore, the Teaching and Learning Division (TLD) of the institution which looks after the professional development of lecturers and sponsors innovative technology-mediated learning initiatives like M-learning is keen to find out how lecturers can be supported to encourage a greater usage of M-learning. After interviewing 10 lecturers, the research found that the following five reasons influence lecturers’ adoption of M-learning from most important to the least: Usefulness in Teaching and Learning, Barriers to using M-Learning, Ease of use of mobile technology, Students’ Motivation and Service quality of the M-learning systems. From this, TLD can identify and develop strategies to support the successful integration of M-learning. The five support aspects considered when implementing M-learning are found to be the following in descending order of importance: Professional Development, Social, Organisational, Technical and Advanced features to support teaching and learning. This research has clarified the existing literature in M-learning especially in formal learning contexts where learning is intentional and recognises the role that lecturers play in the future adoption of M-learning.
 
 

Mobile Learning New Trends in Emerging Computing Paradigms: An Analytical Approach Seeking Performance Efficiency

Mobile learning (m-learning) adoption has increased and shall be demonstrated superior performance by implementing related computing paradigms, such as IoT, edge, mobile edge, fog, AI, and 5G. Mobile cloud architectures (MCAs) enable m-learning with several benefits and face limitations while executing real-time applications. This study investigates the state-of-the-art m-learning architectures, determines a layered m-learning-MCA obtaining numerous benefits of related computing paradigms, and expands m-learning functional structure. It evaluates m-learning performance across the four physical layer’s MCAs—distance cloud, cloudlet, operator-centric cloud, ad hoc cloud, and emerging computing architectures. Surprisingly, only distance-cloud MCA is adopted for developing m-learning systems by ignoring the other three. Performance evaluation shows m-learning gets terrific benefits and users QoE in related computing paradigms. Mobile edge computing offers ultralow latency, whereas the current architecture improves task execution time (1.87, 2.01, 2.63, and 3.97) for the resource-intensive application (i.e., 4.2 MB). Fog using AI algorithms is exceptional for more complex learning objects, IoT is superior for intelligent learning tools, and 5G ultrawideband services are more significant for intelligent video analytics. These findings help learners, educators, and institutions adopt an appropriate model for achieving their academic objectives across educational disciplines. The presented approach enables future research to design innovative architectures considering resource-intensive m-learning application execution requirements, such as video content analytics and virtual reality learning models.

The Role of Quality Measurements in Enhancing the Usability of Mobile Learning Applications during COVID-19

Despite numerous studies offering some evidence about the significance of quality measurements in enhancing the success of m-learning applications, there are still limited studies about the role of quality measurements in promoting the usability of mobile learning systems. Therefore, our study explores the role of quality measurements in promoting the usability of m-learning systems during COVID-19. The results revealed that the service quality, information quality and system quality are the most important factors affecting mobile learning usability among learners during COVID-19. Moreover, these findings are valuable for classifying the significance of these quality elements, which provide guidance on assigning quality aspects to improve this mobile learning usage during COVID-19 in higher education institutions.

Employing the TAM Model to Investigate the Readiness of M-Learning System Usage Using SEM Technique

During COVID-19, universities started to use mobile learning applications as one of the solutions to support distance learning. The readiness of universities to apply new systems, such as mobile learning applications, is considered one of the critical issues to ensure the system’s success. Determining the most important aspects of readiness to use mobile learning is a key step to adopt mobile learning in an effective way. To address this issue, this research aims to determine the most important determinants influencing mobile learning readiness by employing the Technology Acceptance Model (TAM). The Structural Equation Modelling (SEM) method was used to test the hypotheses in the proposed model. The results showed that the relationship between mobile learning readiness and awareness, IT infrastructure and top management support was positively significant. In conclusion, the findings will be of value to decision makers and mobile learning developers in universities to enhance the development of mobile learning applications. In addition, it may help facilitate and promote the usage of mobile learning applications among users.

Students’ Perceptions of the Actual Use of Mobile Learning during COVID-19 Pandemic in Higher Education

Mobile learning (M-learning) has become a significant component of higher education technology. Moreover, M-learning allows students to study, collaborate, and exchange ideas while using the internet and technology. Furthermore, an acceptance of M-learning is necessary for students and educators when it comes to using M-learning systems. However, in Saudi Arabia, not enough studies have been conducted to address students’ perceptions of their actual use of mobile learning for the purpose of education during the COVID-19 pandemic. Therefore, the current research aims to examine students’ satisfaction with their behavioral intention to use mobile learning, as well as their perceptions of their actual use of mobile learning during the COVID-19 pandemic in higher education. The research is based on the Technology Acceptance Model (TAM). The satisfaction and behavioral intentions of students to utilize mobile learning to make real use of mobile education was measured using four separate variables. A TAM survey was used to collect the majority of the data, with questionnaires being randomly distributed to 300 students from King Saud University. SPSS and Smart-PLS3.3.3 were used to analyze the data. The results in terms of the students’ satisfaction and behavior in using M-learning show that M-learning has a good and constructive influence on the actual usage of M-learning during the COVID-19 pandemic in higher education. As a result, we encourage students to use M-learning in their classrooms and to collaborate with their peers at higher education institutions. The study’s empirical findings aid in the integration of the TAM model in order to increase students’ M-learning performance.

Implementing M-Learning System for Learning Mathematics Through Computer Games and Applying Neural Networks for Content Similarity Analysis of an Integrated Social Network

In order to make e-learning systems more readily available for use, the majority of new systems are being developed in a form suitable for mobile learning, i.e. m-learning. The paper puts focus on the parts of the implementation of an e-learning system which is not restricted to desktop platforms, but works equally well on smartphones and tablets in the form of m-learning. The implemented system uses educational computer games for learning Mathematics in primary schools and has an integrated social network, which is used for communication and publishing of the content related to the game. Besides analysing the platforms used for accessing the system (desktop/mobile), since students are given a choice, the paper also questions how to interpret messages when they contain concepts in student jargon or generally unknown to teachers, and shows that these messages can be interpreted by applying neural networks.

Detecting Students Gifted in Mathematics with Stream Mining and Concept Drift Based M-Learning Models Integrating Educational Computer Games

One of the problems of individualized classes which adapt contents and methods of teaching to students of different cognitive capabilities is early and widely available detection of students gifted in certain educational fields. The paper proposes models which are based on stream mining and which can detect students gifted in Mathematics solely on the basis of their interaction with the m-learning system using educational computer games and with no access to any other feature except for student age. Classification accuracy and time-efficiency of different feature selection methods are examined in order to make the models more interpretable, hence less complex. Stream mining classification accuracy in the utilized models is evaluated on new (yet unseen) records, while the concept drift detection analyses at which point of time should new models be built.

Developing an educational framework for using mobile learning during the era of COVID-19

This paper focuses on the impact of fear emotion upon technology adoption by educators and students during Covid-19 pandemic. Mobile learning (m-learning) has been applied as the educational social platform within higher education institutes, public as well as private. The research hypotheses were associated with the Covid-19 influence on m-learning adoption with the rise of the coronavirus increasing types of fear. Such fears include fear caused by the education failure, family lockdown, and loss of social relationships. Teachers and students are mostly fearful of these aspects of the situation. An integrated model was established within the research, using theoretical models; the Planned Behavior theory, the Technology Acceptance Model, and the Expectation-Confirmation Model. The proposed integrated model (using PLS-SEM software) was analyzed using an online survey data, with 420 respondents from Zayed University, UAE. The findings indicated that attitude was the best predictor for using the m-learning system, followed by continuous intention, expectation confirmation, perceived usefulness, ease-of-use, perceived fear, behavioral control, and satisfaction. According to the research, during the coronavirus pandemic, if the m-learning system is adopted for educational reasons, the learning and teaching outcome proves quite promising. Yet there is a fear of the family being stressed, or of loss of friends, and also a fear of the results of future schooling. It is therefore necessary to assess the students efficiently during this pandemic so that the situation can be managed emotionally.

Factors Affecting Mobile Learning Acceptance in Higher Education: An Empirical Study

The use of mobile tools to support learning and teaching activities has become a significant part of the informal learning process. Mobile learning (M-learning) is used to considerably develop the forms of learning activities made by learners, and support the learning process. The effective application of M-learning in higher educational institutions, however, is based on the learners’ adoption. It is therefore essential to define and investigate the factors affecting the desire of learners to use and adopt M-learning. Thus, this research investigates the factors affecting students’ intention to adopt M-learning in institutions of higher education. To achieve the objectives of this research, a model is proposed based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model and the Technology Acceptance Model (TAM). The instrument is developed using validated items from previous studies and shreds of literature. Data for this quantitative study are collected from undergraduate and postgraduate students. A Structural Equation Model (SEM) is used to analyze the data collected from 218 participants using a survey questionnaire. The findings show that students’ intention to adopt M-learning is shaped by various variables consisting of personnel innovativeness, self-management, facilitating conditions, social influence, relative advantage, and effort expectancy. The research results also present several practical contributions and implications for M-learning adoption in terms of research and practice. Investigation of the required determinants may contribute to gain learners’ adoption and is important to enhance the learning experience of students and help them improve their knowledge and academic achievement. The contribution of this paper lies in defining the factors influencing the acceptance and use of M-learning systems by students of higher education in Palestine. Hopefully, the results of the study are valuable for policy-makers in designing comprehensive M-learning systems.

Artificial nero fuzzy inference model for location and time aware m-learning system - an empirical investigation

Use of smart devices in education has facilitating anywhere and anytime learning, providing access to the contents through mobile devices, is called as 'mobile learning (m-learning)'. Learning styles have been evolved with advancements in mobile technology and mobile networks. Personalisation and context awareness of a learner play a vital role in delivering suitable content, based on the context of a learner. There is a need of a content delivery system that takes care of dynamically changing learner needs. Artificial nero fuzzy inference system (ANFIS) can be used to model such an adaptive content delivery system for an m-learning environment. In this paper, two models have been built and compared for their performance. The first model uses grid partitioning algorithm and second, subtractive clustering. Firstly, in both models the behaviour of the two optimisation methods-hybrid and back propagation; against various membership functions are compared for their exclusive performance against various datasets. Secondly, behaviours of both models are compared to find out which model performs better against various datasets.

Decision tree learning through a Predictive Model for Student Academic Performance in Intelligent M-Learning environments

Abstract In the area of machine learning and data science, decision tree learning is considered as one of the most popular classification techniques. Therefore, a decision tree algorithm generates a classification and predictive model, which is simple to understand and interpret, easy to display graphically, and capable to handle both numerical and categorical data. The intelligent m-learning systems, enjoy recently an explosive growth of interest, for more effective education and adaptive learning tailored to each student's learning abilities. The goal of this paper is to further improve personalization in student academic performance, that includes dynamic tests with a predictive model. One major objective of this research is to create adaptive dynamic tests for assessing student academic performance, while constantly comparing the results of the assessment which exhibits the individual student profile, with the results of the decision tree's algorithm which formulates a predictive model for students' knowledge level, according to the weights of the decision tree.

A Comparative analysis of learning, retention, learning and study strategies in the traditional and M-learning systems

IntroductionLearning and study strategies are important issues that have attracted the attention of many researchers in recent decades. The importance of strategies is well known as a way of improving the level of learning. ObjectiveThis research aimed to compare the effect of learning via mobile devices on learning, retention, learning, and study strategies, with traditional instructional methods. MethodThe present study is a quasi-experimental design with a static group comparison design. The study comprised all primary education students of Taybad Branch, Islamic Azad University (N=54) in the second semester of the academic year 2018–2019 who chose the language course. These students were considered as a research sample and divided them non-randomly into two groups (27 people as experimental and 27 people as the control group). Before the beginning of the research, a researcher-made learning pre-test questionnaire was developed for both groups in similar conditions. Then, we used the traditional method for the control group and implemented the M-Learning method for the experimental group. At the end of sixteen weeks of training, a researcher-made learning post-test and also the standard learning strategies test were performed in both groups, and after two months of learning tests; the retention test was performed for both test and control groups. Independent t-test analyzed data in Statistical Product and Service Solutions (SPSS) software. ResultsFindings showed that the rate of learning and retention among trainees by the M-learning method is more than the traditional method. However, in the traditional method, the motivation for progress was more than M-Learning. ConclusionSince mobile learning has a high degree of interoperability, personalization, and participation, compared to traditional learning, it allows students to learn at any time and place they wish. Language teachers and education planners with regard to the importance of learning various skills, apply e-learning in training increase personal motivation and interest in learning.

DQAMLearn: Device and QoE-Aware Adaptive Multimedia Mobile Learning Framework

With the proliferation of mobile devices and online video services, mobile learning has grown both in popularity and complexity. New challenges include the multitude of mobile devices with different characteristics and limitations, as well as the exponential growth in educational multimedia content. Streaming multimedia content to mobile devices is a resource intensive task that requires significant resources such as network bandwidth, with demand expected to increase beyond future networks capacity as users adopt new technologies such as UHD, AR, VR, 3D and 360-degree video. While a number of adaptive m-learning systems have been previously proposed, none of these have thoroughly addressed the adaptation of educational multimedia content. This article proposes the novel DQAMLearn framework that aims to support mobile learner’s seamless access to educational multimedia content from a variety of mobile devices with different characteristics. Moreover, as mobile users are increasingly becoming quality-aware, the framework integrates novel mechanisms for decreasing the video quality in a controlled way, with the aim to support a good learner quality of experience (QoE) even in resource constrained situations. A comprehensive subjective study was conducted to evaluate the proposed framework. The results showed that the framework enables both high learning achievement from educational multimedia clips, with 12% and 83% correct response rates for pre and post-test questionnaires, respectively, and high learner QoE with a mean video quality rating of 79.19 on a 0–100 acceptability scale.

Predicting the actual use of m-learning systems: a comparative approach using PLS-SEM and machine learning algorithms

ABSTRACT Despite the plethora of m-learning acceptance studies, few have tackled the importance of examining the actual use of m-learning systems from the lenses of social influence, expectation-confirmation, and satisfaction. Additionally, most of the prior technology adoption literature tends to use the structural equation modeling (SEM) technique in analyzing the structural models. To address these limitations, this study extends the technology acceptance model (TAM) with the expectation-confirmation model (ECM) and social influence to predict the actual use of m-learning systems. A comparative approach using the partial least squares-structural equation modeling (PLS-SEM) and machine learning algorithms was employed to test the proposed model with data collected from 448 students. The results revealed that both techniques have successfully provided support to all the hypothesized relationships of the research model. More interesting, the J48 classifier has performed better than the other classifiers in predicting the dependent variable in most cases. The employment of a comparative analytical approach is believed to add a significant contribution to the information systems (IS) literature in general, and the m-learning domain in specific.

Influencing factors in M-learning adoption in higher education

The tremendous and rapid developments in the information and communications technology sector as well as mobile devices have resulted in modern technologies, one of which is Mobile Learning (M-learning). M-learning is a new technique for learning that helps students to do their educational activities and access the learning materials easily without temporal or spatial restrictions, with the help of mobile devices. It is a robust component to make learning easy and flexible. Recently, many applications and services related to it have been developed. Despite the large number of researchers who have dealt with the topic of M-learning, the issue of factors affecting the adoption of M-learning has not been dealt with adequately, especially in Palestine. Therefore, it becomes necessary to explore the factors influencing the intentions of the students of higher education institutions to adopt M-learning. Hence, the goal of this study is to inspect the factors that influence higher education students’ intentions in Palestine to adopt M-learning system in the learning process and use its applications based on Technology Acceptance Model (TAM) and some external factors. Wherefore, built on the Technology Acceptance Model (TAM) integrated with some external factors (mobility, self-efficacy and enjoyment), this paper proposes a hypothesized model of M-learning in Higher education institutes in Palestine. Relevant data were gathered from a sample of 388 students. Participants, using a self-report questionnaire, reported data. Pearson correlation, multiple linear regression and structural equation modeling (SEM) were employed to analyze the collected data. Results indicate that perceived usefulness and attitude have significant influence on M-learning adoption intention, while perceived usefulness, perceived ease of use and perceived self-efficacy significantly affect the attitude to use M-learning. Perceived enjoyment and perceived self-efficacy are predictors of perceived ease of use. While mobility and perceived ease of use have significant effect on perceived usefulness. These results validate the capacity of TAM constructs and the external variables used in this research for predicting acceptance of M-learning. Limitations and future work are highlighted.

Evaluation of MoblrN m-learning system: Participants' attitudes and opinions

The advance of pervasive technologies such as mobile technologies and various mobile devices has brought opportunities for educators to design and perform interactive learning activities and has also enabled educationalists to access the virtual learning environment anytime and anywhere without restrictions of time or place and without too much additional effort. The m-learning system, MobLrN, has been developed by the researcher. Different from the m-learning systems developed by other researchers, the MobLrN m-learning system contains all teaching/learning components for m-learning, such as access control, user profile, learning materials, assignments, self-tests, quizzes, performance measurement, announcements, and report generation. Thus, instead of using different systems for different activities, the instructors can use the developed mobile system for all of their teaching activities. The main porpuse of the study was to identify whether the developed m-learning system is acceptable or not as a mobile learning environment. Data were collected through a questionnaire. Descriptive statistical analysis technique and the percentage method were used. The obtained results confirm that the MobLrN m-learning system was deemed to be useful for instructors and students for the future. The system is oriented to anyone who may have an interest in mobile technologies, and all higher education institutes.
 Keywords: m-learning system; m-learning environment; m-learning; evaluation; distance learning