Mobile Learning System Research Articles

A systematic literature review and plans for further study on smart mobile learning system acceptance

This paper’s aim is to conduct a systematic review of the state of scholarly research on smart mobile learning acceptance (SMLS) and suggest areas for future research. Fifty-five papers from 2019 to 2022 were used for the systematic literature review. The theories of the SMLS acceptance, publication patterns by region, industry, sample type, as well as the study methodology obtained from content analysis are presented. The results revealed about 18 different theories were identified with TAM being the most frequently employed model (n = 23). Three different methods (quantitative, qualitative and mixed-method) were used by the scholars, with questionnaire survey being the main method used to collect data (n = 50). Most of the researchers used structural equation modelling (SEM) to analyse their data. The Asian region has the most studies on SMLS (n = 33), while the education industry happens to be the main focus of the researchers (n = 45). This study's findings may be utilized to enrich theoretical and methodological advancements, as well as to provide important insights for policy creation by developing strategies and frameworks for the acceptance of SMLS. By putting all the relevant knowledge of SMLS together from diverse industries, this study enhances the academic landscape.

Learning efficiency maximization in UAV-and-RIS-aided mobile edge learning system

With the ever-increasing number of Internet of Things devices (IoTDs) and the rapid development of artificial intelligence (AI) technologies, mobile edge learning (MEL) has emerged as a new communication network paradigm that can deploy machine learning on mobile edge computing (MEC) platforms with abundant computational resources. Then how to fully exploit the potential of MEL and enhance its performance is an important issue. To address this issue, the paper proposes an unmanned aerial vehicle (UAV)-and-reconfigurable intelligent surface (RIS)-aided MEL system. In the system, the UAV equipped with a MEL server can fly close to the IoTDs to collect their data for training a deep learning model. And the RIS mounted on the building can improve the wireless channel environment between the UAV and IoTDs to assist the UAV in collecting data. In order to maximize the MEL learning performance while minimizing the system energy consumption, this paper also proposes a new optimization metric called learning efficiency. Then, a learning efficiency maximization problem based on the proposed system is formulated by jointly optimizing the minority class sample size, the transmit resource of the IoTDs, the phase shift of the RIS, and the trajectory of the UAV. Considering the intractability of the problem, we solve it using the alternating optimization (AO) algorithms based on the two types of UAV trajectory design, i.e., a time-division-multiple-access (TDMA) design with higher performance and a Flight-Hover design with lower complexity. The simulation results demonstrate that the proposed optimization metric and algorithms are effective and perform excellently compared to other baselines.

The Structure of Open and Distance English Language Teaching Models: A Follow Up Study 2018 – 2023

In recent years, the widespread adoption of Open and Distance Learning (ODL) has revolutionized education, especially English language teaching. ODL has democratized access to educational resources and enables interaction between students and instructors across geographical boundaries. Moreover, technology integration has emerged as a dominant theme, with a focus on web-based, computeraided, and mobile-learning systems. However, the study advocates for innovative approaches, such as mobile and game-based learning, aligning with contemporary trends. The adoption of various evaluation methods shows the recognition of individual differences and underscores the need for a comprehensive view of learner progress. This research employs a systematic approach to review literature and investigate the common structures of ODL models for teaching the English language, focusing on articles published between 2018 and 2023. This study categorizes studies based on five macro dimensions of ODL —management, communication, learning, technology, and evaluation—in order to identify common themes and patterns within the literature. In contrast to a previous study spanning 1990 to 2017, this investigation uncovers the evolving landscape of distance education, underscoring the need for continued research and improvement. This research emphasizes the lack of a comprehensive management model for EFL education delivered through ODL, and suggests the need for further investigation in this domain. Moreover, the findings emphasize the significance of leveraging diverse communication models to enhance learner engagement. A shift towards autonomous learning models is observed, demanding further investigation into strategies that support effective self-directed learning experiences. In conclusion, this comprehensive analysis provides a roadmap for advancing ODL models in English-language education. To enhance the quality and accessibility of English language instruction in ODL, educators and researchers can concentrate on addressing management gaps, optimizing communication strategies, promoting autonomous learning, exploring advanced technologies, and refining evaluation approaches. This research offers a rich repository of knowledge for educators, researchers, and academic institutions that wish to deepen their comprehension and practical application of ODL approaches in the domain of English language instruction

Implementation of a Personalized Adaptive Mobile Learning System

A notable obstacle in the field of education is the restricted flexibility of traditional teaching approaches. These approaches frequently take a consistent stance, disregarding the wide range of learning preferences that pupils possess. This leads to a decrease in student motivation and engagement, which in turn produces below-average learning outcomes. This research focuses on creating an adaptive learning system that classifies learners using the Felder-Silverman model in order to overcome these problems. After then, this system creates customized recommendations based on user choices in an effort to improve learning results. In order to keep enhancing the system's efficacy, the study have also included a feedback mechanism and performance evaluation.

Automatic Mobile Learning System for the Constant Preparation of the Student Community

Introduction: the events that occurred with the pandemic caused a drastic change in all activities with direct contact due to the high risk of contagion, with educational centers being affected by the closure measures and the imposition of virtual classes to continue with student preparation, leading many students to see the need to have a computer to take their classes, eventually showing boredom due to the lack of desire to be in front of a computer, This to a certain extent weakens their interest in learning and affects their learning because mobile devices have become more important due to the various applications that provide students with information. For this reason, we propose mobile learning that allows students to have more information, as well as interaction with different students so that they have the opportunity to learn on a constant basis.Objective: the objective is to create an automatic mobile learning system for the constant preparation of the student community.Method: a methodology based on a client-server model to take advantage of the various educational resources accompanied by the good support it provides the subjects for students with the interaction of a mobile application.Results: through the operation of the system, it was visualized that the tests carried out with the students were presented with an efficiency of 96,70 %,Conclusions: this system presents a high efficiency that allows to reinforce the subjects that need more prominence in the student's learning and progress of level through the teacher's evaluations

Understanding mobile learning continuance after the COVID-19 pandemic: Deep learning-based dual stage partial least squares-structural equation modeling and artificial neural network analysis

The influence of COVID-19 on educational processes has halted physical forms of teaching and learning and initiated online and mobile learning systems in most countries. The provision and usage of online and e-learning systems are becoming the main challenge for many universities during the COVID-19 pandemic. Due to the novelty of this situation, a substantial amount of research has been carried out to investigate the issue of m-learning adoption or acceptance. Nevertheless, little is known about studying to examine the continued use of m-learning, which is still in short supply and calls for further research. Five different theoretical models are integrated into this study to develop an integrated model that overcomes this limitation, including the technology acceptance model, the theory of planned behavior, the expectation-confirmation model, the Delone and McLean Information System Success Model, and the Unified Theory of Acceptance and Utilization of Technology 2. This conceptual framework shows novel relationships between variables by integrating trust, personal innovation, learning value, instructor quality, and course quality. Unlike extant literature, this study utilized a hybrid analysis methodology combining two-stage analysis using partial least squares structural equation modeling (PLS-SEM) and evolving artificial intelligence named deep learning (Artificial Neural Network [ANN]) on 250 usable responses. The sensitivity analysis results revealed that attitude has the most considerable effect on the continued use of m-learning, with 100% normalized importance, followed by perceived usefulness (88%), satisfaction (77%), and habit (61%). This research reveals that a “deep ANN architecture” may determine the non-linear relationships between variables in the theoretical model. Further theoretical and practical implications are also discussed.

A Visual Prompt-Based Mobile Learning System for Improved Algebraic Understanding in Students With Learning Disabilities

A Visual Prompt-Based Mobile Learning System for Improved Algebraic Understanding in Students With Learning Disabilities

China-ASEAN Qualifications Framework: Pathways to mutual recognition

Developing a mutual recognition mechanism for China-ASEAN qualification framework of vocational education is essential for creating a fair, freely mobile lifelong learning system that will enable learner to carry and accumulate their learning outcomes across the region. By formulating and implementing unified standards, mutual recognition of qualifications across domains, industries and regions will be promoted, fostering greater collaboration and mobility between educational institutions and workplaces. To achieve this objective, China and the Association of Southeast Asian Nations (ASEAN) have undertaken extensive preparations and explorations at the policy, organizational and functional levels. The development commitments outlined in the “Vision 2030 of China-ASEAN Strategic Partnership” provide a framework for the establishment of a mutual recognition mechanism, while the existing promotion measures of all parties demonstrate their commitment to realizing this vision. This paper proposes the concept of five development stages of mutual recognition of national qualification frameworks (NQFs) in vocational education, which will gradually establish the mutual recognition mechanism of regional NQFs. This mechanism will not only promote greater collaboration and mobility between learners, educational institutions, and workplaces but also achieve the ultimate goal of comprehensive development of people and society in the region.

Exploring students’ acceptance of an artificial intelligence speech evaluation program for EFL speaking practice: an application of the Integrated Model of Technology Acceptance

Adapted from the Technology Acceptance Model (TAM), the Integrated Model of Technology Acceptance (IMTA) has been used to examine the perceptions and acceptance of computer-assisted language learning (CALL), such as online learning, mobile learning, and learning management systems. However, whether IMTA can be applied to empirical research on AI-assisted language learning remains unexplored. Therefore, this article intends to analyze an AI speech evaluation system for English speaking practice, in the context of higher education through the IMTA. Research instruments encompassed questionnaires (n = 218) and semi-structured interviews (n = 21). The participants were English as a foreign language (EFL) learners who had used an AI speech evaluation program to practice their speaking skills. The results suggested that (1) most participants found the AI program useful, pleasant, and easy to use. They also had a strong intention to continue using it; (2) perceived usefulness (PU) and perceived enjoyment (PE) are significant predictors of behavioural intention to use (BI). Meanwhile, problems related to user interface design, the accuracy of automatic feedback and especially the lack of face-to-face interaction were reported. This study demonstrates how IMTA could be applied to examine users’ acceptance of AI programs for EFL speaking practice. The findings also offer insights into further research and development in AI tools for EFL speaking practice.

EVALUATIОN OF CHILDREN’S BEHAVIOUR IN THE CONTEXT OF AN EDUCATIONAL MOBILE GAME

The paper presents a study aiming to propose a model and system for mobile gamebased learning in which the behaviour of the learner during the game is taken into account, considering characteristics such as noise level, movements of the mobile device, switching between applications and the availability of Wi-Fi network and Internet connection. Behavioural profiles and behavioural templates are suggested implementation techniques to assist the teacher in evaluating learner’s progress and curriculum efficiency. The learning model also applies adaptive, micro lesson and game-based approaches. An implementation of a software system prototype has been tested with real learners. The results of a survey of students' and teachers' opinions on the proposed approach are presented.

Measuring and classifying students' cognitive load in pen‐based mobile learning using handwriting, touch gestural and eye‐tracking data

AbstractAlthough the utilization of mobile technologies has recently emerged in various educational settings, limited research has focused on cognitive load detection in the pen‐based learning process. This research conducted two experimental studies to investigate what and how multimodal data can be used to measure and classify learners' real‐time cognitive load. The results found that it was a promising method to predict learners' cognitive load by analysing their handwriting, touch gestural and eye‐tracking data individually and conjunctively. The machine learning approach used in this research achieved a prediction accuracy of 0.86 area under the receiver operating characteristic curve (AUC) and 0.85/0.86 sensitivity/specificity by only using handwriting data, 0.93 AUC and 0.93/0.94 sensitivity/specificity by only using touch gestural data, and 0.94 AUC and 0.94/0.95 sensitivity/specificity by using both the touch gestural and eye‐tracking data. The results can contribute to the optimization of cognitive load and the development of adaptive learning systems for pen‐based mobile learning. Practitioner notesWhat is already known about this topic Pen‐based mobile learning systems allow natural ways of handwriting and gestural touching, which can facilitate learners' cognitive processes in mobile learning. Behavioural and physiological multimodal data are helpful in detecting learners' real‐time cognitive load in mobile learning. The effectiveness of behavioural and physiological multimodal data for measuring cognitive load in pen‐based mobile learning is limited investigated. What this paper adds This paper confirms the effectiveness of handwriting and touch gestural multimodal data for measuring pen‐based learning cognitive load, in terms of their stroke‐, path‐ and time‐based features. This paper explores the potential of eye‐tracking data in measuring pen‐based learning cognitive load. A combination of behavioural and physiological multimodal data is reported to increase the prediction accuracy for cognitive load measurement. Implications for practice and/or policy Practitioners are suggested to use behavioural and physiological multimodal data individually or conjunctively for measuring cognitive load in pen‐based learning. The results provide guides for developing adaptive pen‐based learning systems by optimizing the real‐time cognitive load.

The extent of awareness of the importance of using mobile learning systems among students specializing in educational technology at Taiz University in Yemen

The aim of the research is to investigate the extent of awareness regarding the skills of utilizing the Mobile Learning (M-Learning) system among students specializing in Educational Technology at Taiz University. In order to achieve the research objective and address its inquiries, a descriptive-analytical methodology was depending on this methodology is used in the analysis and investigation of research phenomena. The research sample comprised graduate students specializing in Educational Technology enrolled in the master’s program at Taiz University for the academic year 2023/2024, totalling 63 male and female students. A questionnaire was used as the data collection tool. Upon gathering and statistically analysing the research data, the results indicated that the research sample students possess awareness of the importance of mobile learning, with their awareness primarily cantered around the fundamental skills of utilizing mobile learning. The results also demonstrated a moderate level of awareness among students regarding their use of mobile learning skills. Their mobile and smart device use is largely restricted to communication and basic applications. However, there is a significant deficiency in their awareness of using mobile devices to access information online, engage in online courses, attend virtual conferences, and other skills available for students' educational and learning benefit. Several suggestions were put forward by the students to enhance effective utilization of mobile learning skills in education and learning. Among these suggestions were raising awareness about mobile learning skills and organizing training courses to equip students with the ability to employ mobile devices in the educational process. Consequently, a number of recommendations were made, including the necessity of incorporating mobile learning as a curriculum component for students, arranging training and awareness sessions on mobile learning skills, fostering students' interest, and encouraging them to acquire mobile learning skills. Additionally, the importance of being cognizant of the diverse uses of mobile learning to keep up with contemporary developments was emphasized.

Which qualities should built environment possess to ensure satisfaction of higher-education students with remote education during pandemics?

The built environment may be considered a complex system comprising several parts (including people, organizations, and physical structures). Since the emergence of the COVID-19 virus in recent years, there has been an explosion in the usage of mobile learning applications for classroom instruction. Research on the diffusion of mobile learning is lacking. This study examines the perceptions and experiences of many aspects of online education from the viewpoint of Chinese HEIs and university students who shifted to online modes of instruction during the COVID-19 outbreak. This research aimed to determine whether or not college students were happy with the resources available to them, how their teachers communicated with them, how they were evaluated, and the conditions in which they studied at home. It also looked at the pros and cons of online learning from the students' points of view. For this study, researchers opted for a cross-sectional survey using an online questionnaire. Information was gathered from 450 students from different Chinese tertiary institutions. To this end, we reflect on the experiences and perspectives of postgraduate students in urban design and discuss the results of an online survey conducted during the 2021-2022 academic year's subject delivery period, which included activities such as assessment, feedback, field study visits, workshops, and the use of digital platforms. PLS-SEM, which stands for partial least square structural equation modeling, is used in order to evaluate the proposed model. Therefore, the findings show that the research model offered for this investigation could explain the diversity in the actual use of mobile learning systems. This would provide helpful insight into the effect of educational, environmental, and quality factors on the actual use of remote education systems. It was shown that quality concerns acted as a mediator between institutional policy, change management, and the implementation of mobile learning systems in actual classrooms. The findings also indicate that the level of pleasure students feel acts as a moderating factor between the degree to which the mobile learning system is used and its overall level of quality. According to the findings of these studies, academic institutions may enhance their use of mobile learning applications and get more benefits from these systems if they adhere to the suggestions stated in these studies.

Joint Data Transmission and Trajectory Optimization in UAV-Enabled Wireless Powered Mobile Edge Learning Systems

The mobile edge learning (MEL) system is an emerging intelligent system, which can collect the data generated by the Internet of things devices (IoTDs) and perform machine learning algorithms on the edge computing platform. However, due to the limited transmit power of IoTDs and the severe propagation loss, maximizing F-measure in MEL systems is a challenging task, which is a meaningful performance metric in machine learning algorithms. In order to address the dilemma, an unmanned aerial vehicle (UAV)-enabled wireless powered MEL system is proposed in this paper. In this system, the UAV provides energy for all IoTDs and collects the data from busy IoTDs for machine learning. The busy IoTDs transmit their respective data to the UAV through a direct and an indirect transmission process. In the latter one, the busy IoTDs utilize their neighboring free IoTDs that have no data to forward data. An F-measure maximization problem based on this system is investigated by jointly optimizing the minimum sample size among all classes, the transmit power of IoTDs, and the UAV's trajectory and velocity. Since the formulated problem is non-convex and intractable to solve, we propose two effective algorithms, one is based on successive convex approximation (SCA) and the other is based on alternating optimization (AO). And the computational complexity of the latter is lower than the former. Simulation results show that the SCA-based algorithm is superior to the AO-based algorithm and other baselines. The results also reveal that the proposed algorithms can converge within several iterations.

A Framework for Farmers’ Acceptance of Smart Mobile Learning System for Pesticide Management Training: A Conceptual Review

A Framework for Farmers’ Acceptance of Smart Mobile Learning System for Pesticide Management Training: A Conceptual Review

Designing an Algorithm for Automated Analysis of Completed Tasks in a Mobile Learning System

Purpose: this abstract discusses an urgent problem in the modern education system associated with the need to adapt to rapid changes in social spheres and the economy. The main purpose of the study is to develop an adaptive mobile learning environment for higher education institutions, with an emphasis on the use of electronic (mobile) learning and information and analytical support for the educational process.
 Methods: to achieve this goal, an analysis of existing research and publications in the field of educational technologies and the effectiveness of e-learning was carried out. The objectives of adaptive mobile learning technologies are formulated, including the creation of a virtual learning environment and the development of effective methods of text processing in distance learning.
 Results: in the course of the study, an algorithm for an adaptive mobile learning environment system based on an autonomous analysis of completed tasks using neural networks was proposed. To ensure the reliability of the system, it is proposed to implement a task analysis service as a background process. A scheme for transferring information from mobile applications to a background analytical service has been developed, taking into account the use of the RabbitMQ queue for organizing communication between services. An algorithm for detailed analysis of the text of completed assignments of students has also been developed.
 Conclusions: the study highlights the need to adapt educational systems to modern challenges and the possibility of using adaptive mobile learning technologies. The developed algorithm and system provide an effective analysis of assignments and text responses of students, which makes them relevant in the context of modern information technologies and education.

Investigating the Validity and Reliability of the Mobile Application Rating Scale

The purpose of this study is to perform the validity and reliability study of the Mobile Application Rating Scale (MARS) for a mobile learning system that contains e-contents in different forms to improve digital parenting competencies for adults. This scale was prepared by Stoyanov et al. (2015) and adapted into Turkish for the e-pulse application by Korkmaz and Arıkan (2021). In this study, the scale items were rearranged in the context of the study to evaluate “digital parenting”. This scale aims to evaluate the application with four sub-dimensions of “participation, functionality, aesthetics, and knowledge”, and it has 21 items. The study was carried out with 181 participants. Considering the validity results of the scale, the model with the highest validity among the single-factor model, four-factor model, and second-level confirmatory factor analysis models was the four-factor model, as in the original scale. Factor loads vary between 0.55 and 0.91. Considering the reliability results, the average variance extracted for the participation dimension was 0.69, for functionality 0.78, for aesthetics 0.75, for knowledge dimension 0.63, and the average variance extracted for the whole scale was 0.70. In addition, alpha reliability (stratified alpha reliability) was 0.92 for participation, 0.94 for functionality, 0.92 for aesthetics, 0.92 for knowledge, and 0.97 for the whole scale. In the context of these findings, the validity and reliability values of the scale were high.

Applying a wearable MR-based mobile learning system on museum learning activities for university students

ABSTRACT This study developed a wearable mixed reality (MR)-based mobile learning system and applied it to museum learning activities to explore whether the proposed system can effectively improve university students’ learning outcomes, situational interest, and cognitive load. Four museums were selected as the informal learning sites, and 64 university students were randomly divided into the experimental group (using the wearable MR-based mobile learning system) and the control group (using the conventional mobile learning system). The results show that in the retention and comprehension dimensions of the learning outcome, the experimental group has significantly better learning performance than the control group. The experimental group showed improvements in almost all dimensions of situational interest, except for the challenge dimension, while their extraneous cognitive load was reduced. Furthermore, this study found that the proposed system has a positive influence on museum learning in higher education, and has good potential for improving the conventional mobile learning system and enhancing students’ learning performance. The findings of this study can provide a reference for future research on the possibilities of using wearable devices for museum education.

Management of Teaching and Learning Resources Application

Abstract: This paper focuses on the effective management of teaching and learning resources in classrooms using mobile learning and biometric attendance systems. With the creation of mobile applications, learning has become more flexible and accessible. Mobile learning is a strong tool for supporting and increasing learning. Classroom management is also crucial for providing an environment that is favorable to learning, and the integration of proper note-taking strategies can aid in effective learning. Additionally, using biometric attendance can produce precise and trustworthy attendance records, guaranteeing the attendance of all students. Overall, developing a successful learning environment that encourages student engagement and achievement depends on effective management of teaching and learning resources

A Mandarin Tone Recognition Algorithm Based on Random Forest and Feature Fusion †

In human–computer interaction (HCI) systems for Mandarin learning, tone recognition is of great importance. A brand-new tone recognition method based on random forest (RF) and feature fusion is proposed in this study. Firstly, three fusion feature sets (FFSs) were created by using different fusion methods on sound source features linked to Mandarin syllable tone. Following the construction of the CART decision trees using the three FFSs, modeling and optimization of the corresponding RF tone classifiers were performed. The method was tested and evaluated on the Syllable Corpus of Standard Chinese (SCSC), which is a speaker-independent Mandarin monosyllable corpus. Additionally, the effects were also assessed on small sample sets. The results show that the tone recognition algorithm can achieve high tone recognition accuracy and has good generalization capability and classification ability with unbalanced data. This indicates that the proposed approach is highly efficient and robust and is appropriate for mobile HCI learning systems.