M-learning Applications Research Articles

Analysis of the Evolution and Adoption of Mobile Technology in Educational Institutions

Smartphone acceptance could improve instruction, increase accessibility, and foster student involvement, which is a fundamental element in the development and application of m-learning in the classroom. This process reflects continuing advancements and versions in educational methods. The effect of mobile technology adoption in classrooms is examined in this study, focusing on its effects on student engagement, educational outcomes, and the associated infrastructure and support. The study comprised qualitative and quantitative data collected from 300 students using a mixed-method approach. Five key variables related to mobile technology use were integrated, and SPSS software was employed for analysis. Descriptive statistics were used to compute an adoption index, highlighting positive student feedback and assessing technological integration, despite, to assess variations in student satisfaction and academic achievement; a one-sample t-test was used. Results revealed significant positive impacts on engagement and outcomes, though challenges in infrastructure and cost price. The findings highlight how mobile technology could transform education, but they also highlight how much additional resources and encouragement are required to ensure that it is as sustainable and effective as possible.

Factors Affecting The Adoption Of Mobile Learning In Vocational High Schools And High Schools Using Extended UTAUT

M-Learning is a learning process that uses technology or mobile devices such as smartphones, tablets or wearable devices to support the learning process. This is still being done because there are many different theoretical models proposed. However, there is no model that can be generally accepted as an established theoretical model in the application of M-learning in vocational and high school education environments in Sidoarjo. This research is expected to make a significant contribution to the development of a better theoretical understanding of the determining factors that influence M-learning adoption using the Unified Theory of Acceptance and Use of The Technology (UTAUT). To collect data, researchers distributed questionnaires to respondents using Google Form. The data used were 444 M-learning users. Theoretical model research was carried out using Structural Equation Modeling (SEM) analysis, then SPSS and Amos as analysis support. There are seven factors that determine the results of acceptance of M-Learning adoption in this research, namely Facilitating Condition, Performance Expectancy, Effort Expectancy, Perceived Convenience, Social Influence, School Management Support. The six factors that show a positive and significant relationship are Facilitating Condition, Performance Expectancy, Effort Expectancy, Perceived Convenience, Social Influence, School Management Support. Perceived Convenience has the first strongest positive and significant value, and Performance Expectancy has the second strongest value. Each factor has a moderate influence on Intention to Use. This factor is the most influential in implementing M-Learning in vocational and high schools in the Sidoarjo area.

Learning Management System in Education via Mobile App: Trends and Patterns in Mobile Learning

The mobile revolution has influenced students’ preferences for various educational platforms in the new digital era, especially regarding young learners’ utilization of mobile devices such as smartphones, iPads, and other gadgets for mobile learning (m-learning). Because of this, universities that implement learning management systems (LMS) through standard web-based platforms should explore the potential for integrating mobile devices and technologies into m-learning platforms. Through the utilization of this technology, LMS can facilitate continuous user interaction and enhance user awareness of any revisions made to the material. To reach the majority of LMS users, mobile applications must be developed for all major mobile platforms. By utilizing the Web View API, this research combined native mobile and web technologies to develop the mobile application. This strategy was adopted in anticipation of the requirement to create and maintain the application across multiple mobile platforms. It was anticipated that this approach would reduce the time needed for creation, maintain a consistent interface, and enable the use of platform-specific features. It also makes sense to provide mobile device access to some of the LMS virtual classroom’s functions. The K-means algorithm is used for analyzing course material and learning. Nevertheless, achieving this goal might not be an easy process. To enable this form of connection between the LMS and the m-learning applications, this chapter assesses the challenges involved in achieving that goal and presents various common interchange designs and related research and development efforts.

A Study On The Usage Of M-Learning Applications Within Bulgarian Schools

The transformation of the educational model is directly linked to the key participants in the educational process. Modern pupils cannot imagine life without the usage of various digital technologies such as mobile devices. Moreover, today’s learners spend more time preparing for school using interactive mobile applications. That defines the purpose of this article: to explore the usage of m-learning applications in the educational process within Bulgarian schools. To achieve that it has been created a survey that consists of 11 questions and was distributed to 140 participants (pupils from Bulgarian schools). The results have shown that 54.28% of respondents use m-learning applications within the classroom and 72.86% of them use m-learning applications for self-study. Additionally, 60% of respondents believe that using such applications would enhance the educational process. This provides ample justification for directing efforts from all stakeholders (educational institutions, educators, and learners) toward wider and more comprehensive integration of these applications in the educational process. From the perspective of future research, there could be endeavors to conduct a more extensive and representative study by trying to utilize cluster sampling in order to overcome the limitations posed by the current research methodology.

Empowering young learners: m-learning application with adaptive learning and CCI standards

Empowering young learners: m-learning application with adaptive learning and CCI standards

Validity of instrument to measure primary school mathematics teachers’ acceptance of m-learning applications

<span lang="EN-US">Mobile learning (M-learning) is one of the proposed technology-based methods for teaching mathematics in today’s education 4.0 era. This method enables teachers to conduct instruction and learning without being bounded by walls, wires, and the need for specialized physical infrastructure and facilities. This study was conducted to develop and validate an instrument for evaluating primary school mathematics teachers’ acceptance of M-learning applications in problem-solving teaching involving six variables. This research is quantitative and utilizes a questionnaire to collect data. This study involved a total of four experts and 120 participants. Cronbach’s alpha and Exploratory Factor Analysis were used to perform a descriptive analysis of the data. Cronbach’s alpha was determined to be 0.934, with a factor eigenvalue greater than 1. The Kaiser–Meyer–Olkin value for each construct is 0.50, whereas Bartlett’s test value is statistically significant (<0.5). Each item has a factor loading of 0.50 and a variance of ≥60%. The findings showed that this instrument contains six constructs and nineteen appropriate items. It also indicated that this instrument could be used to investigate perceptions of the primary school mathematics teachers’ acceptance of m-learning applications in problem-solving teaching instruments involving performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, and usage behavior (attitude).</span>

Predictive Model for Factors Influencing Students’ Continuance Usage Intention on a Gamified Formative Assessment Application

Formative assessments help students identify their strengths and weaknesses as well as the target areas that need work. Meanwhile, the success of gamification implementation in mobile learning (m-learning) applications has been proven by many research associated with technology acceptance. However, studies on the technology post-acceptance phase are scarce. Deploying gamification concepts in competency-based education and assessment method, aim to diversify the game dynamics usage in new and practical areas such as technical and vocational education. Therefore, this study focused on the development of a predictive model for a gamified m-learning application that extends the Expectation Confirmation Model (ECM) with perceived enjoyment. This study applied a correlational research design on the students of Technical and Vocational Education and Training (TVET) based diploma programmes in one of Malaysian public university. An online survey was conducted, and garnered 269 responses, which were used in the final analysis based on the Partial Least Square-Structural Equation Modelling (PLS-SEM). The findings showed that all the factors in the model, which are the confirmation of expectation, satisfaction, perceived usefulness and perceived enjoyment, appeared to be significantly influencing the students’ continuance usage intention towards the gamified m-learning application as well as jointly explained 64.6% (R2) of the changes. The addition of variable perceived enjoyment provided an increment of 2.7% compared to the R2 value of the original ECM model. Most importantly, the proposed extended ECM proved to be a reliable prediction model when the Q2 values for continuance use intention and perceived usefulness displayed strong predictive accuracy, having 0.368 (Q2) for both variables, while satisfaction showed moderate strength with the value of 0.305 (Q2). Therefore, the predictive model is reliable to be used to investigate future usage and effective designs of gamified m-learning products among the technical and vocational education students in other Higher Education Institution (HEI).

Factors affecting students’ intention to use m-learning: Extending the technology acceptance model (TAM)

ABSTRACT This study set out to determine what variables led students to choose mobile learning during COVID-19 and to study the direct impact of the perceived fear of COVID-19 (PCF) on undergraduate learners’ intentions to adopt m-learning in developing countries such as Sudan. It utilizes the Technology Acceptance Model (TAM) as a foundation and uses PCF as an undependable variable. The surveys were given out to Sudanese undergraduate students. The SPSS and SmartPLS software were utilised for data analysis. The findings revealed that PCF affects both perceived ease of use (PEOU) and perceived usefulness (PU) positively. PEOU and PU influence intention to use (INT) positively. The research findings will help decision-makers, developers of m-learning applications, and researchers promote the acceptance of m-learning. This study contributes to the field of knowledge by extending TAM and adding a new factor, PCF, in order to understand Sudanese students’ intention to use m-learning.

Development of Mobile Learning Applications for Android Based on Artificial Intelligence

In order to enable students to study autonomously and without being constrained by time and distance, this research was done to design an M-Learning learning media based on learning media on artificial intelligence. constrained by time and space, as well as to boost students' enthusiasm for studying. The following are the problems that need to be solved by this research: (1) How feasible is an android-based M-learning application for the field of artificial intelligence? (2) What are the students' reactions to the M-learning application built for Android in the Artificial Intelligence course?Research and development, sometimes known as R&D, is the research methodology employed. Students in the informatics engineering department at Raharja University served as the study's research targets. Techniques Instrument reliability and validity assessments as well as questionnaires are employed as data gathering methods respondents. Then, the examination of media viability and student reaction is performed as a data analysis approach. Descriptive analysis was done on the student replies. (1) Media validation by professional validators is calculated at 92.5% by percentage, according to the results. On the basis of this, it can be said that the android-based M-learning application falls within the category of "Very Excellent" and is appropriate for usage. (2) The results of the M-learning application for Android-based students were positive, with a 79.5% response rate.

Adaptation to the Context in M-Learning Application Using Mobile Design Patterns

The fast progress of mobile devices and the popularity of mobile applications in the market requires complex software to be run on mobile devices without issues. Mobile devices are known as handhelds and they have different characteristics such as size, resolution, battery life and processing power. These are limitations to consider when developing an M-learning application, which can make it difficult to design and develop mobile applications for different mobile devices. Design Patterns, a set of tried and tested solutions for common problems were originally designed for desktop computers, so application of these patterns in M-learning applications might not meet its requirements such as adaptation of the content to the context of the device mobile used by learners. However, mobile design patterns that have been adapted or designed for such an environment can be used to overcome the challenges and limitations of M-learning application development. In this paper we try to propose an approach to engineering design patterns allowing the development of M-learning applications to provide an adapted content taking into account the context of device mobile used by learners. We named these patterns: “Mobile design patterns”.

Development of Android-Based Learning Media in Computer and Basic Network Lessons Class X TKJ Cokroaminoto Vocational High School, Kotamobagu

Cokroaminoto Kotamobagu Vocational School is an educational institution that aims to develop the potential of the nation's children so they can compete at the world level. To fulfill these objectives, educational institutions need to be equipped with adequate computer networks. This study aims to develop learning media based on Android (m-learning) on the subject of Applying IP Addressing to Computer Networks for class X SMK Cokroaminoto Kotamobagu. The research method used in this research is research and development (R&D). The steps taken are Potential and Problems, Gathering Information, Product Design, Design Validation, Design Improvement, Product Trials, Revision of potentials and problems, data collection, product design, design validation, design revisions, product trials, and product revisions. After the product trials were carried out with two stages, namely small group trials carried out by twelve students, and wide trials carried out twice, the first stage was carried out by one teacher, and the second stage by thirty-four participants educated. From the product trials, the results regarding the android-based m-learning application in the material Applying IP Addressing on Computer Networks obtained an assessment with very feasible criteria based on the teacher's assessment and very interesting based on the student's assessment.

Mobile Learning Among University Students: Adoption and Application of M-Learning

The successful implementation of mobile learning is mainly impacted by learners’ attitudes to accepting and applying mobile learning systems. In the current study, an m-learning application was developed and implemented for university students, and their perspective toward the adoption and application of m-learning was investigated. This cross-sectional study was carried out in 2020. The participants were 114 university students at Abadan University of Medical Sciences. Data were collected by means of a valid questionnaire containing 42 questions in 13 subscales in addition to an open-ended question about the positive and negative aspects of the m-learning application. There was a significant relationship between acceptance and major (P=0.001), mobile usage (P=0.035), and familiarity with m-learning (P<0.001). The total mean of m-learning application acceptance was 3.95±0.32, which shows a good level. Self-efficacy, perceived ease of use, and user interface were, respectively, the most influencing factors; mobile device limitation and governmental support were the least influencing factors. “Direct communication with the teacher/instructor” and “Internet disconnection during video teaching” were among the positive and negative aspects of applying the m-learning application, respectively. The m-learning application was well adopted among the students. Mobile device limitations are a reason for the student's concerns. “Usability features” can be a point for covering mobile device limitations.

Investigating Factors That Affect the Continuance Use Intention Among the Higher Education Institutions’ Learners Towards a Gamified M-Learning Application

Investigating Factors That Affect the Continuance Use Intention Among the Higher Education Institutions’ Learners Towards a Gamified M-Learning Application

Understanding the inclination of South Indian nursing graduates in using mobile learning applications.

Clinical-based mobile learning courses have great demand among the nursing graduates as learners look for possibilities to update skills. The present study explores the feasibility, familiarity, utility, and attitude of nursing graduates of South India toward mobile learning applications (m-apps). An online-based cross-sectional descriptive survey was conducted in May 2021 among the South Indian nursing graduates of Tamil Nadu and Kerala, using a questionnaire consisting 49 items categorized under six sections with items related to socio-demographic information; m-app usage; online learning experience; preference in using m-apps for learning purposes before COVID-19 and during COVID-19; students' engagement through e-learning; and anxiety related to online assessment. Descriptive and inferential (ANOVA, Chi-square, and t test) statistical data analysis were done using SPSS version 23. A total of 447 student nurses responded. The result shows that most of them, 96% (432) used android phones and 94% (422) owned a mobile. Age was highly influencing mobile learning application (m-learning apps) usage; students of less than 20 years of age used applications more frequently and possessed more educational applications. Majority of them, 84% (377) of them had started using m-learning apps only after COVID. 57.7% (249) commonly used m-learning apps for acquiring nursing knowledge resources, nursing exam preparatory, and drug resources. Students rated high for the interactive nature of these m-learning apps, while abundant learning materials and usage with ease were rated as other attractive features. Sixty-six percent (305) had mostly downloaded these apps from Google Play Store. The findings would help the m-learning application developers to offer customize solutions to address learning gaps that prevail among the South Indian nursing graduates and help with sustainable growth.

Development of AGRONUTRI-X M-Learning Application using MIT App Inventor 2 Platform

Education today is changing, in line with technology development that has been embedded in our lives. Thus, to meet the needs of today’s generation, there is a need to revolutionize conventional instructional methods by integrating technological elements in line with today’s advancements. The thriving smartphone technology with high ownership rates needs to be leveraged as a learning medium known as M-learning. Compared to E-learning, M-learning has been deemed a more forward-looking trend in learning as it allows learning to be accessed anytime and anywhere without needing specialized facilities or laboratories. However, the optimal use of M-learning as a learning medium in agricultural TVET has yet to be explored. This gap is demonstrated by the small amount of M-learning materials developed for learning agricultural subjects. This study focuses on developing specific M-learning applications for Crop Agro-industry students in Vocational College, specifically to improve their competency in preparing formulations for fertigation fertilizer. The design and development of the AGRONUTRI-X M-learning application are based on the ADDIE model. The model has five phases in its learning material development process: analysis, design, development, implementation, and testing. Balsamiq Wireframes software was used in designing the storyboard and application interface, and the MIT App Inventor 2 platform was used for the application development using the storyboard and interface provided during the design phase. The application was evaluated by five experts, and the results of the expert content evaluation showed that the application has a high content validity (89.2%). The application was tested on 30 vocational college students during a six-week intervention process which was conducted concurrently with conventional learning. The AGRONUTRI-X M-learning application usability questionnaire was then distributed to these students, and the results showed a high level of application usability with a mean score of 4.41. Overall, the development of AGRONUTRI-X M-learning applications has successfully achieved the objectives and contributed to developing M-learning applications for agriculture students.

The Adoption of Mobile Technology for Learning in Public Universities in Ogun State, Nigeria

This study examines the characteristics that influence undergraduate students in public universities in Ogun State to use mobile learning technologies. Based on primary data collected using self-designed questionnaire, the study explored the effect of undergraduate students' perceived satisfaction, perceived ease of use, and perceived accessibility on the use of mobile devices in the classroom, as well as whether there is any difference in undergraduate students' perceptions of the usefulness of mobile technology for learning based on sex and age. Data from a random sample of 400 students from two public colleges in Ogun State was analyzed using the regression Analysis of Variance (ANOVA) technique. Undergraduate students' evaluations of satisfaction, ease of use, and accessibility have been found to have a direct impact on the usage of mobile technology for learning in public universities. Furthermore, it was discovered that, while age and gender have no significant joint and independent influence on the use of mobile technology for learning in public universities, undergraduate students' perceptions of accessibility, satisfaction, and ease of use have a positive significant joint and independent influence. The study discovered that undergraduate students' evaluations of satisfaction, ease of use, and accessibility encourage them to adopt mobile technology for studying in public universities in Ogun State. Undergraduates should be encouraged by obtaining, providing, or funding mobile learning technologies, according to the study. A section for M-learning applications should also be created to provide students with a visual and auditory tutorial.

Developing M-Learning Applications to Support Digital Literacy of Vocational High School Students

Abstrak: Literasi digital sangat penting untuk diterapkan saat ini karena pembelajaran telah mengarah pada pembelajaran yang mendukung teknologi. Literasi digital di Indonesia masih tergolong sangat rendah sehingga perlu upaya dalam meningkatkannya. Penelitian ini bertujuan untuk mengembangkan aplikasi m-learning berbasis Android yang tepat untuk mendukung literasi digital siswa SMK. Jenis metode yang digunakan yakni penelitian dan pengembangan (R&D) dengan model ADDIE. Hasil penelitian ini menunjukkan bahwa pengembangan media sudah sesuai dengan kriteria baik materi maupun media yang menunjukkan hasil 3,67 yang kategori “baik”, sementara hasil pada uji media sebesar 4,05 yang kategori “sangat baik” dan pada uji coba kelas 3,92 yang kategorinya “baik”. Aplikasi ini juga, terbukti efektif untuk digunakan dalam mendukung literasi digital siswa yang ditandai dengan meningkatnya hasil belajar siswa dari rata-rata pretest 66 menjadi 78 pada postestnya. Sehingga dapat disimpulkan dari hasil penelitian ini memberikan kontribusi dalam mendukung literasi digital peserta didik vokasi. Abstract: Digital literacy is very important to apply at this time because learning has led to learning that supports technology. Digital literacy in Indonesia is still very low, so efforts are needed to increase it. This study aims to develop appropriate Android-based m-learning applications to support digital literacy in vocational high school students. The type of method used is research and development (R&D) with the ADDIE model. The results of this study indicate that media development is in accordance with the criteria for both material and media which shows a result of 3.67 which is in the "good" category, while the results in the media test are 4.05 which is in the "very good" category and in class trials 3.92 in the "good" category. This application has also proven to be effective for use in supporting students' digital literacy which is marked by increased student learning outcomes from an average pretest of 66 to 78 in the posttest. So it can be concluded from the results of this study that it contributes to supporting the digital literacy of vocational students.Â

Adoption of Mobile Learning at Universities Using the Extended Technology Acceptance Model

This study aims to contribute to the proof of factors likely to determine the success of M-learning adoption based on previous research. This is done because there are many different theoretical models proposed. However, there is not yet a model that can be generally accepted as an established theoretical model for the adoption of M-learning in universities. This research is expected to make a significant contribution to the development of a better theoretical understanding of the determinants that influence the adoption of M-learning using the Technology Acceptance Model (TAM). To collect the data, researchers distributed questionnaires to respondents using google forms. Forms are distributed via WhatsApp and Telegram. The data used was 515 M-learning users. Theoretical model research was carried out with Structural Equation Model (SEM) analysis, then SPSS and Amos as support for analysis. There are six factors that determine the results of acceptance of M-leaning adoption in this study, namely Social Influence, Perceived Enjoyment, Facilitating Condition, Self-Efficacy, Perceived Usefulness, and Perceived Ease of Use. The five factors that show positive and significant relationships are Social Influence, Perceived Enjoyment, Self-Efficacy, Perceived Usefulness, and Perceived Ease of Use. Perceived Usefulness has the first strongest positive and significant value, and then Social Influence has the second strongest value. Each factor has a medium influence value on Behavioral Intention. That factor is the most influential in the application of M-learning in universities.

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.

Development and Evaluation of a Software Product Line for M-Learning Applications

The popularity of mobile devices in all social classes has motivated the development of mobile learning (m-learning) applications. The existing applications, even having many benefits and facilities in relation to the teaching-learning process, still presents problems and challenges, es- pecially regarding the development, reuse and architectural standardization. Particularly, there is a growing adoption of the Software Product Line (SPL) concept, in view of research that investigates these gaps. This paradigm enables organizations to explore the similarities and variabilities of their products, increasing the reuse of artifacts and, consequently, reducing costs and development time. In this context, we discuss how systematic reuse can improve the development of solutions in the m-learning domain. Therefore, this work presents the design, development and experimental evaluation of M-SPLearning, an SPL created to enable the systematic production of m-learning applications. Specifically, the conception of M-SPLearning covers from the initial study for an effective domain analysis to the implementation and evaluation of its functional version. In this regard, the products have been experimentally evaluated by industry software developers, pro- viding statistical evidence that the use of our SPL can speed up the time-to-market of m-learning applications, in addition to reducing their respective number of faults.