Mobile Learning Model Research Articles

Mobile single-lead ECG atrial fibrillation prediction enhancement integrated by standard ECG algorithm with Deep learning model

Abstract Background Artificial intelligence (AI) using electrocardiogram (ECG) enabled to predict atrial fibrillation (AF) in patients without documented AF. Mobile single-lead ECG is more convenient to surveil cardiac rhythm with simple measurement. However, AI-enabled arrhythmia predictability by mobile ECG is limited due to single channel utilization and longer duration for arrhythmia diagnosis. We aimed to enhance mobile single-lead ECG AF prediction AI algorithm integrated with 12-lead ECG using deep learning model. Method Based on 552,372 12-lead ECG data of 318,321 patients, a statistical AF prediction model employing a deep-learning approach was constituted. Out of single-lead 13,509 ECGs from a total of 6,719 patients, we utilized a total of 10,287 normal sinus rhythm ECGs from 5,170 patients. Resnet structure was utilized to distinguish subtle changes of the vicinity of P-wave. Single-lead mobile ECGs were adjusted with noise filtering and segmented every 10 seconds. A random under-sampling was applied to reduce bias from data imbalance. Both 12-lead ECG and single-lead ECG were allocated to training, validation, testing datasets in a 6:2:2 ratio. Then, we conducted transfer learning using the standard 12-lead ECG’s deep learning model to improve performance of single-lead mobile ECG deep learning model. Results AF was annotated in 26,541 (4.8%) with 12-lead ECG whereas 1,443 (21.2%) with single-lead mobile ECG. The area under the curve (AUC) value for predicting AF was 0.910 with 12-lead ECG, and 0.742 with mobile ECG. The predictive performance of mobile ECG was 67.5% in accuracy, 64.2% in sensitivity and 66.8% in F1-score. The AUC value of mobile ECG after applying transfer learning based on 12-lead ECG for AF prediction was increased to 0.790 with accuracy of 73.8%, sensitivity of 65.5% and F1-score of 71.0%. Conclusion Integration with deep learning algorithm of standard 12-lead ECG significantly improved the model performance of single-lead ECG AF prediction model compared to single-lead mobile ECG only based model. Easy application of mobile ECG with enhanced AF predictability might serve a more convenient method as a pre-emptive assistive tool to provide probabilistic prediction for PAF screening rather than 12-lead ECG.

Exploring a Mobile Technology-Driven Model for Intercultural Communication Education

In the context of accelerating globalization, intercultural communication competence has become a crucial element for the success of individuals and organizations. The frequent international interactions have underscored the importance of enhancing methods for improving intercultural communication skills within the educational sector. The rapid advancement of mobile technology offers unprecedented opportunities for educational innovation. Its convenience and widespread use have made mobile device-based learning models highly attractive. Particularly, advancements in real-time speech processing technologies have provided new tools and methods for intercultural communication education. By leveraging mobile real-time speech detection and synthesis technologies, a more interactive and personalized learning experience can be achieved, thereby enhancing the efficiency and effectiveness of language learning. This study aims to explore a mobile technology-based model for intercultural communication education and is divided into three main parts: firstly, the investigation of mobile real-time speech detection technologies aimed at intercultural communication education to provide instant feedback and improvement suggestions; secondly, the exploration of mobile real-time speech synthesis technologies to generate high-quality speech samples for learners to practice against; and thirdly, the integration of the aforementioned technologies to develop a flexible, efficient, and highly interactive learning system based on mobile technology. This study is expected to not only improve the effectiveness of intercultural communication education but also provide significant references for the innovative application of educational technologies.

Mobile Learning and Resource Sharing Mode of College English Translation Based on 5G Mobile Communication Technology

The application of 5G and other mobile communication technologies to education and teaching, and the construction of College English Translation mobile learning and resource sharing model and related models have important scientific value and significance for the effective organization and management of learning resources and the improvement of the speed and efficiency of college english translation resource retrieval. This research mainly combines the characteristics of complementarity and expansion of mobile learning technology for traditional online learning technology. Based on Moodle online teaching platform, this research develops a mobile learning and resource sharing mode for college english translation based on 5G mobile communication technology, designs and implements a mobile learning model based on Moodle platform, and designs the system architecture, functional modules, learning mode and learning process, Finally, the main functional modules of the system model are implemented and tested. To promote mobile learning in the teaching field to play a helping role in a wide range. The experimental results show that the AKAZE algorithm is used to optimize the model, and the overall accuracy rate reaches 74.7%, and the test probability value P (siq)=0.00, which is significantly less than the significant level of 0.05. Therefore, we believe that the application of mobile learning instructional design mode will help students improve their academic performance.

An enhanced image based mobile deep learning model for identification of Newcastle poultry disease

An enhanced mobile deep learning model based on images is presented in this paper to identify Newcastle poultry disease. A dataset of manually annotated and labeled images of the disease was utilized to pre-train an image-based Convolutional Neural Network (CNN). An Android smartphone app was developed to communicate with the model. A local server was integrated with the generated model to do image classification. A mobile application was developed and made available, enabling users to upload a fecal photograph to a website housed on the streamlet server and obtain the model's processed findings. The user regains control over their health status. The model achieved an accuracy of 95% on the test set and was able to correctly identify specific instances of Newcastle poultry disease. The paper discusses the advantages of a mobile-based approach in comparison to traditional methods of identification and proposes the model as an effective low-cost solution for farmers and researchers.

Practical Model of Mobile Learning Applications

The mobile device has been supported by the operating system that makes the phone has the potential to be a learning tool. The Android operating system is the mobile platform of high potential in education, plus it is open source, it can support independent learning. Availability of facilities and applications that can be installed offline, allowing an independent learning process starts from the students themselves. The purpose of this research is to find a practical model that can be applied in learning. Make mobile learning a practical model that can be used teachers, students and schools take advantage of the resources that already exist. The research method uses research and development, to mobile learning models that later developed into a model factual. Results The study concluded that: Android mobile application that is filled with interesting teaching materials can continue to invite progress within teacher as creator and students as users. Availability of Internet applications, making new teaching participants, can catch up with learning by opening apps without worry about the royalty claims. Android mobile application that supports online messaging greatly help communication between members. Results of the study are: a practical mobile learning model consists of four aspects, namely: Four Key Roles, Learning Tasks Accommodation, Interactive Content and the Modular Learning Outcomes.

Exploring Students' Perceptions of Mobile Game-Based Social Studies Learning Model

This research explores students' perceptions of mobile game-based learning models in social studies instruction. A descriptive quantitative design was used, with a questionnaire survey given to 31 students with experience with mobile game-based learning models. The research results show that students generally have a positive perception of mobile game use in Social Studies learning, with the majority agreeing that mobile games increase their engagement and motivation. The study also identified several factors influencing student engagement and motivation, such as the relevance and quality of game content, the level of interactivity and feedback provided, and integration with the curriculum. The findings show that the mobile game-based learning model can potentially increase students' enthusiasm for learning social studies, especially for Generation Z students accustomed to using digital devices in their everyday lives.

Retracted: Construction of the EFL Mobile Learning Model in the Hybrid Distributed Terminal

Retracted: Construction of the EFL Mobile Learning Model in the Hybrid Distributed Terminal

Abstract 14207: Mobile ECG Atrial Fibrillation Prediction Enhancement Powered by Standard ECG Algorithm With Artificial Intelligence

Introduction: Artificial intelligence (AI) using electrocardiogram (ECG) enabled to predict atrial fibrillation (AF) in patients without documented AF. It needs 12-lead ECG with tremendous data learning. Mobile 1-lead ECG is more convenient to surveil cardiac rhythm with simple measurement. However, AI-enabled arrhythmia predictability by mobile ECG is limited due to single channel utilization and longer duration for arrhythmia diagnosis. Hypothesis: We aimed to enhance mobile ECG AF prediction AI algorithm integrated with 12-lead ECG deep learning model. Methods: Based on 552,372 12-lead ECG data of 318,321 patients, a statistical AF prediction model employing a deep-learning approach was constituted. The raw data of 6,792 1-lead mobile ECGs were acquired from 6,792 patients for about 1 minute at 250Hz. A statistical AF prediction model with mobile ECG employing a deep-learning approach was constituted. Resnet structure was utilized to distinguish subtle changes of the vicinity of P-wave. Both 12-lead ECG and mobile ECG were allocated to training, validation, testing datasets in a 6:2:2 ratio. Then, we conducted transfer learning using the standard 12-lead ECG’s deep learning model to improve performance of 1-lead mobile ECG deep learning model. Results: AF was annotated in 26,541 (4.8%) with 12-lead ECG whereas 1,443 (21.2%) with 1-lead mobile ECG. The area under the curve (AUC) value for predicting AF was 0.910 with 12-lead ECG, and 0.721 with mobile ECG . The predictive performance of mobile ECG was 79.7% in accuracy, 47.5% in sensitivity and 50.3% in F1-score. The AUC value of mobile ECG after applying transfer learning based on 12-lead ECG for AF prediction was increased to 0.905 with accuracy of 89.1%, sensitivity of 71.3% and F1-score of 57.7%. Conclusions: Integration with deep learning algorithm of standard 12-lead ECG significantly improved the model performance of mobile ECG AF prediction model compared to 1-lead mobile ECG only based model. Easy application of mobile ECG with enhanced AF predictability might serve a more convenient method as a pre-emptive assistive tool to provide probabilistic prediction for PAF screening rather than 12-lead ECG.

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.

Effect Size Mobile Learning on Student’s 21 st Century Skills

This study aims to determine the effect size of mobile learning on students' 21st century skills. This type of research is a meta-analysis research. The research sample came from 13 national and international journals. Data sources came from google scholar, ScienceDirect, Eric, Wiley and ProQuest databases. Data collection techniques in this study were direct observation and documentation. The process of selecting data sources was carried out carefully and systematically. Inclusion criteria are national and international journals indexed by SINTA and Scopus, research must use experimental or quasi-experimental methods, research must have experimental classes with mobile learning models and conventional controls and research has complete data on the value of n, f and t. The data analysis technique is quantitative statistical analysis with the help of the JSAP application. The results of this study concluded that the average effect size value (ES = 0.853) and the N-gain value of 0.60 These findings indicate that the mobile learning model has a high influence on students' 21st century thinking skills. This finding shows that the mobile learning model has a high impact on students' 21st century thinking skills.

Effectiveness of the Inquiry Based Learning Model Based on Mobile Learning on Students' Creative Thinking Skills: A Meta-Analysis

This study aims to determine the effect of the inquiry-based learning model based on Mobile Learning on Students' Creative Thinking Skills. This type of research is quantitative research with meta-analysis methods. The data source comes from analysis of national and international journals. The process of searching for data sources through the Google Scholar, ERIC, DOAJ, Wiley, Taylor of Francis and Plos ONE databases. Inclusion criteria are data sources from national and international journals indexed by SINTA; Scopus, WOS, DOAJ and EBSCO, Research must be related to inquiry models, mobile learning and critical thinking skills, Journals must be published in 2017-2023 and have a value (r), (t), or F N ≥ 25. Research results of 18 studies explains that the inquiry-based learning model based on mobile learning has a significant effect on students' creative thinking skills (z = 6.713; p < 0.001; CI 95% (0.784; 1.206). The effect size in this study is high criteria (rRE = 0.885). These findings show the application of mobile learning-based inquiry learning models has a major effect on students' creative thinking skills.

Retracted: Mobile Learning Model of Tour Guide Business in Universities from the Perspective of Distributed Cognition

Retracted: Mobile Learning Model of Tour Guide Business in Universities from the Perspective of Distributed Cognition

Retracted: Construction of College English Mobile Learning Model Based on Distributed Terminal

Retracted: Construction of College English Mobile Learning Model Based on Distributed Terminal

The Effectiveness of LKPD Mobile Learning on the PBL Model of Environmental Change Material to Increase Interest in Learning and Critical Thinking of Grade X High School Students

This study aims to find out: (1) the effectiveness of LKPD mobile learning model problem-based learning on environmental change material used to increase the learning interest of grade X high school students; (2) the effectiveness of LKPD mobile learning model problem-based learning on environmental change material used to improve critical thinking of grade X high school students. This type of research is quasi-experimental with a pre-test design and post-test control group design. The limited trial subjects in this study were 34 grade X students of Senior Hight School 1 Ngabang. The field test subjects consisted of 36 experimental class students and 36 students for the control class in grade X of Senior Hight School 1 Ngabang. Data collection used several instruments, namely interviews, needs analysis questionnaires, assessment questionnaires for material experts, media expert assessment questionnaires, RPP expert assessment questionnaires, syllabus expert assessment questionnaires, biology teacher assessment questionnaires, student response questionnaires, learning interest questionnaires, critical thinking test sheets. Data analysis techniques use the normality test, homogeneity test, T-test, Mann-Whitney test, and n-gain score. The results showed that: (1) LKPD mobile learning model problem-based learning is effective in increasing students' interest in learning. The increase in interest in learning according to the n-gain score that occurred in the experimental class achieved a score of 0.7 in the high category, and (2) LKPD mobile learning problem-based learning model is effective in improving students' critical thinking. The increase in critical thinking according to the n-gain score that occurred in the experimental class achieved a score of 0.8 in the high category.

Model of intention and actual use mobile learning in higher education institutions in Iraq

Mobile learning (ML) is now involved in the creation of teaching and learning methods for higher education. However, this involvement is varied among countries. The objective of this research was to examine the factors that impact students' intentions to use ML in Iraqi higher education institutions (HEIs). Building on unified theory of acceptance and use of technology (UTAUT) and information system success model (ISSM), a conceptual model was developed. The population of this study are users in Iraqi univeristies. Using a stratified random sampling, a total of 323 responses were collected to examine the proposed hypotheses. The findings showed that variables of UTAUT such as effort expectancy (EE), social influence (SI), performance expectancy (PE), facilitating conditions (FC) as well as variables of ISSM such as satisfaction along with perceived enjoyment and self efficacy affected positively the intention to use ML (ITUML) which in turn affected actual use (AU). Gender and experience moderated the effect of PE, EE, and SI on ITUML. A model of ITUML among users in Iraqi HEI was developed. Decision makers are advised to focus on certain variables to enhance the usage of ML in HEI.

A Sustainable Quality Model for Mobile Learning in Post-Pandemic Higher Education: A Structural Equation Modeling-Based Investigation

As an effect of the digital transformation encountered by higher education institutions in the post-pandemic phase, the current study aims to inspect the factors affecting the actual use of mobile learning among higher education students. A novel hybrid model based on the information system success and technology acceptance models was proposed and tested. The study included 400 undergraduate and postgraduate students from four Saudi universities who responded to a questionnaire consisting of two parts and seven dimensions, with a total of 26 items. For the analysis, a quantitative approach was applied using structural equation modeling. The results displayed that information quality had no impact on the actual use of mobile learning among higher education students. In contrast, other quality factors (system quality, service quality, and satisfaction) and perceived factors (perceived usefulness and perceived ease of use) had a positive effect. Accordingly, this study proposed an integrated framework to assist decision makers at higher education institutions in scaffolding students to develop their educational performance by depending on mobile applications comprising high-quality factors that address their real needs. This would also enable higher education institutions to enhance their digital transformation experience, thus contributing to achieving positive learning sustainability after the pandemic.

The Effect of RPG Maker Mobile Learning Model in Learning English

The aimed of this research was to measure the effect of application (RPG) MakerMV. This research belongs to experimental research and quasi experimental design. It described about the effect of using of the application. The research methodology was a quantitative method. The population of this study was one Class XI students from MA Siti Khadijah, and the sample was twenty students or two classes throughpurposive sampling. The research result showed that this application were very effective and very easy to use.The researcher concluded that the experimental group through this application in the right way. The average percentage of student activity is 75.6%, which is included in both categories.The experimental group had higher score (429) after learning English vocabulary using direct method than control group (274). The conclusions of this study was RPG Maker mobile effective in the teaching learning English. This study recommended to use this aplication in teaching english. The application help students to follow the lesson

An M-Learning Model in the Context of the Blended Synchronous Learning Environment

Mobile learning provides more flexibility and holds considerable promise for improving the learning process and promoting lifelong learning. In order to reduce the sense of isolation felt by the learners, this research integrates mobile learning in the blended synchronous learning environment (BSLE). This study proposed a mobile learning model in BSLE at Shanghai Open University, and 51 students' satisfaction and engagement surveys were examined. The results showed that student satisfaction with instruction and with interaction can significantly predict behavioral engagement, while satisfaction with instruction and with technology can significantly predict psychological engagement. The findings prove that the mobile learning mode in the blended synchronous learning environments is effective and contributes to the predictors of student engagement. Thus, it can provide some insights to construct a more flexible and effective learning space.

The study on designed gamified mobile learning model to assess students’ learning outcome of accounting education

This study constructs an innovative course for accounting teaching based on a student-centered strategy. The curriculum is designed through the effective teaching module (BOPPPS) to assist students to understand accounting knowledge. A game-based mobile learning environment is created by developing an accounting mobile game and combining it with a mobile learning system (TronClass). The private technology university students had been selected by purposive sampling. A total of 81 accounting majored students, among them, 41 students are in the experimental group, and the rest were in the control group. The quasi-experimental design was to be applied in curriculum development. Meanwhile, quantitative data were collected by questionnaires and the qualitative data were collected through interviews. The result shows that game-based mobile learning can be beneficial to teaching effectiveness. And the regression model supports that information quality and service quality have positive predictive power on use intention. In addition, use intention and user satisfaction impacted learning engagement positively. Further, user satisfaction has a mediating effect. Finally, some suggestions are put forward to provide references for accounting educators and researchers.

A personalised recommendation of mobile learning model based on content awareness

In order to overcome the problems of traditional recommendation methods such as large error in recommendation results and long time-consuming process of recommendation results generation, the paper proposes a personalised recommendation method based on content-aware mobile learning mode. First, the recommendation process architecture is designed, which mainly includes a user demand analysis module, a user preference analysis module, and a mobile learning model resource library decision module. Then, the energy function is used, and the dataset is inserted to design the content perception process. Finally, according to the perceptual results, a user emotional topic model with a supervision mechanism is used to complete personalised recommendation. The experimental results show that the average absolute error value of the recommendation results obtained by the method in this paper is between 0.06-0.15, the maximum recommendation result generation process takes only 4.5 s, and the clustering effect of different mobile learning modes is better.