M-learning Model Research Articles

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 Flipped Classroom Approach via the M-Learning Model: Impact on Student Learning and Motivation

Over the past decade, there has been a growing emphasis on integrating modern technology into educational systems, particularly in the form of new information and communication technologies (NICT), including mobile technologies. Mobile learning (M-Learning) is an advanced form of e-learning that allows learners to access educational materials and knowledge sources through intelligent digital electronic devices in a flexible mobile environment. This study explores the impact of applying the flipped classroom pedagogical approach via the M-Learning model on learners' motivation. To investigate this, an experiment was conducted on a group of 86 learners in the Operating System and Local Area Network course, where M-Learning was adopted as the teaching model. The experimental group was compared with a control group, and the obtained results showed that the experimental group had a significant improvement in learning outcomes and remarkable motivation according to the ARCS model. The findings indicate that the experimental group demonstrated improved learning and remarkable motivation according to the ARCS model. This suggests that incorporating M-Learning into the flipped classroom approach can be an effective way to enhance learners' motivation and engagement in the learning process. Keywords : flipped classroom, m-learning, ARCS model, motivation. https://doi.org/10.55463/issn.1674-2974.50.4.18

Digital competences model for elderly m-learning

Digital competences model for elderly m-learning

M-Learning and Learning Autonomy - Needs Analysis and Suggested Model

In this 4.0 era, where technology has been a part of our daily lives, the integration of smart devices into education is an indispensable movement that we are striving for. Hence, the application of mobile devices into learning, M-learning is becoming a trendy approach and a quality assurance’s measurement for almost every academic institution. Technology makes education available for students almost anywhere, anytime, and offers unlimited access to learning resources. Furthermore, M-learning is also reported to have a positive impact on students’ learning autonomy. Hence, this paper aims at validating possibilities to amplify this aspect of M-learning and suggesting an appropriate M-learning model at Nguyen Tat Thanh university (NTTU). The paper investigates students’ needs, requirements, ability to adapt to the new trend of M-learning plus the external elements affecting their learning autonomy to come up with the learning model. Surveys were collected from students in the Faculty of Foreign Languages and analyzed using a qualitative method to provide educators and teachers at NTTU with insightful information and careful preparation before launching new approaches to M-learning.

Leveraging the Power of Deep Learning Technique for Creating an Intelligent, Context-Aware, and Adaptive M-Learning Model

Machine learning (ML) and deep learning (DL) algorithms work well where future estimations and predictions are required. Particularly, in educational institutions, ML and DL algorithms can help instructors in predicting the learning performance of learners. Furthermore, the prediction of the learning performance of learners can assist instructors and intelligent learning systems (ILSs) in taking preemptive measures (i.e., early engagement or early intervention measures) so that the learning performance of weak learners could be increased thus reducing learners’ failures and dropout rates. In this study, we propose an intelligent learning system (ILS) powered by the mobile learning (M-learning) model that predicts learners’ performance and classify them into various performance groups. Subsequently, adaptive feedback and support are provided to those learners who struggle in their studies. Four M-learning models were created for different learners considering their learning features (study behavior) and their weight values. The M-learning model was based on the artificial neural network (ANN) algorithm with the aim to predict learners’ performance and classify them into five performance groups, whereas the random forest (RF) algorithm was used to determine each feature’s importance in the creation of the M-learning model. In the last stage of this study, we performed an early intervention/engagement experiment on those learners who showed weak performance in their study. End-user computing satisfaction (EUCS) model questionnaire was adopted to measure the attitude of learners towards using an ILS. As compared to traditional machine learning algorithms, ANN achieved the highest prediction accuracy for all four learning models, i.e., model 1 = 90.77%, model 2 = 87.69%, model 3 = 83.85%, and model 4 = 80.00%. Moreover, the five most important features that significantly affect the students’ final performance were MP3 = 0.34, MP1 = 0.26, MP2 = 0.24, NTAQ = 0.05, and AST = 0.018.

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.

Mobile learning acceptance and use in higher education during social distancing circumstances: an expansion and customization of UTAUT2

PurposeThis paper investigates if the existing degree of students' acceptance and use of mobile or m-learning may face the online shift determined by SARS-CoV-2. Based on the extended unified theory of acceptance and use of technology (UTAUT2), a new comprehensive model, SD-UTAUT (social distancing-UTAUT), is developed to better understand relationships between the original constructs, plus personal innovativeness (PI) and information quality (IQ). It identifies the key factors affecting behavioral intention (BI) and use by examining the influence of revaluated hedonic motivation (HM) and learning value (LV) importance as mediators.Design/methodology/approachThe paper opted for an exploratory study involving 311 learners, using partial least squares structural equation modeling (PLS-SEM).FindingsSD-UTAUT can be a new m-learning model in higher education. It has high predictive power and confirmed 15 out of 16 hypotheses. The most powerful relationship is between performance expectancy (PE) and HM. IQ affected LV the most, since HM the behavioral use (BU). HM impacts the use behavior (UB) more than LV, but habit (HT) affects it the most.Research limitations/implicationsBecause of the pandemic context, output may lack generalizability and reproducibility.Practical implicationsTo improve usage, staff must provide better support, course creators emphasize the objectives and competencies and developers integrate innovation. The joy and pleasure of m-learning use may stimulate the LV through interesting and interactive content, like incorporating gamification.Originality/valueThe model set-up and circumstances are previously unseen. SD-UTAUT confirms ten new hypotheses and introduces the student's grade point average (GPA) as a moderator.Peer reviewThe peer review history for this article is available at https://publons.com/publon/10.1108/OIR-01-2021-0017

Innovative Smart Phone Learning System for Graphical Systems within COVID-19 Pandemic

With restrictions on face-to-face clinical consultations within the COVID-19 pandemic and the challenges faced by healthcare systems in delivering patient care, alternative information technologies like telemedicine and smartphones play a crucial role. A wide variety of smartphone applications employs high-tech mathematical and programming approaches to enhance computer-assisted communication and learning processes’ pedagogical efficiency. Accordingly, this study’s main objective is to develop a model system that can function as smartphone computer graphics. This paper used the Technology Acceptance Model (TAM) as an m-learning model, and Bresenham’s line algorithm is a calculation system implemented by applications. The study method applies technology to validate the accuracy of the contents’ acceptance method of use. The results reveal significant positive effects of the proposed model on generating reasonable, fast, and accurate solutions for the presented problems and developing a more interactive platform of m-learning.

M-Learning Systems Usage: A Perspective from Students of Higher Educational Institutions in Sri Lanka

Mobile devices have become attractive learning devices for education. The digitalization of the higher education system in Sri Lanka by 2020 is part of the government's effort to modernize and enhance the country's overall education system particularly in view of the COVID-19 pandemic. Theoretically, this study contributes to the M-Learning model in higher education institutions via the integration of literature on technology adoption (TAM and UTAUT) with the variables of Perceived Usefulness, Perceived Ease of Use, Attitude, Effort Expectancy, Social Influence, and Facilitating Condition. The attitude towards M-Learning amongst higher education students was gauged via an online questionnaire survey. The convenience sample comprised 344 students from the Advanced Technological Institutes (ATI) in Batticaloa District, Sri Lanka. Descriptive statistics, a measurement, and structural model, and hypotheses testing were used to analyze the derived data. The findings indicate that mobile learning is significantly affected by perceived ease of use, social influence, effort expectancy, and facilitating condition, but negatively affected by attitude and perceived usefulness. The exhaustive literature review revealed that there are very few M-Learning studies related to digital learning in the context of higher education in the Batticaloa district.

Learn from your environment: A visual literacy learning model

Based on the presupposition that visual literacy skills are not usually learned unaided by osmosis, but require targeted learning support, this article explores how everyday encounters with visuals can be leveraged as contingent learning opportunities. The author proposes that a learner’s environment can become a visual learning space if appropriate learning support is provided. This learning support may be delivered via the anytime and anywhere capabilities of mobile learning (m-learning), which facilitates peer learning in informal settings. The study propositioned a rhizomatic m-learning model of visual skills that describes how the visuals one encounters in their physical everyday environment can be leveraged as visual literacy learning opportunities. The model was arrived at by following an approach based on heuristic inquiry and user-centred design, including testing prototypes with representative learners. The model describes one means visual literacy could be achieved by novice learners from contingent learning encounters in informal learning environments, through collaboration and by providing context-aware learning support. Such a model shifts the onus of visual literacy learning away from academic programmes and, in this way, opens an alternative pathway for the learning of visual skills.
 
 Implications for practice or policy:
 
 This research proposes a means for learners to leverage visuals they encounter in their physical everyday environment as visual literacy learning opportunities.
 M-learning software developers may find the pedagogical model useful in informing their own software.
 Educators teaching visual skills may find application of the learning model’s pedagogical assumptions in isolation in their own formal learning settings.

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.

Deep neural network based m-learning model for predicting mobile learners’ performance

The use of deep learning (DL) techniques for mobile learning is an emerging field aimed at developing methods for finding mobile learners' learning behavior and exploring important learning features. The learning features (learning time, learning location, repetition rate, content types, learning performance, learning time duration, and so on) act as fuel to DL algorithms based on which DL algorithms can classify mobile learners into different learning groups. In this study, a powerful and efficient m-learning model is proposed based on DL techniques to model the learning process of m-learners. The proposed m-learning model determines the impact of independent learning features on the dependent feature i.e. learners? performance. The m-learning model dynamically and intuitively explores the weights of optimum learning features on learning performance for different learners in their learning environment. Then it split learners into different groups based on features differences, weights, and interrelationships. Because of the high accuracy of the DL technique, it was used to classify learners into five different groups whereas random forest (RF) ensemble method was used in determining each feature importance in making adaptive m-learning model. Our experimental study also revealed that the m-learning model was successful in helping m-learners in increasing their performance and taking the right decision during the learning flow.

Improving M-Learners’ Performance Through Deep Learning Techniques by Leveraging Features Weights

Mobile learning (M-learning) has gained tremendous attention in the educational environment in the past decade. For effective M-learning, it is important to create an efficient M-learning model that can identify the exact requirements of mobile learners (M-learners). M-learning model is composed of features that are generated during M-learners' interaction with mobile devices. For an adaptive M-learning model, not only learning features are required, but it is also important to determine how they differ for various M-learners, their weights, and interrelationship. This study proposes a robust and adaptive M-learning model that is based on machine learning and deep learning (ML/DL) techniques. The proposed M-learning model dynamically explores learning features, their corresponding weights, and association for M-learners. Based on learning features, the M-learning model categorizes M-learners into different performance groups. The M-learning model then provides adaptive content, suggestions, and recommendations to M-learners in order to make learning adaptive and stimulating. For comparative analysis, the prediction accuracy of five baseline ML models was compared with the deep Artificial Neural Network (deep ANN). The results demonstrated that deep ANN and Random Forest (RF) models exhibited better prediction accuracy. Subsequently, both models were selected for developing the M-learning model which included the performance categorization of M-learners under a five-level classification scheme and assigning weights to various features for providing adaptive help and support to M-learners. Our explanatory analysis has shown that behavioral features besides contextual features also influence the learning performance of M-learners. As a direct outcome of this research, more efficient, interactive, and useful mobile learning applications can be developed that accurately predict learning objectives and requirements of diverse M-learners thus helping M-learners in enhancing their study behavior.

Effect of information quality and system quality in information system success model as an antecedent of mobile learning in education institutions: case study in Jordan

Educators and students in the higher institutions have reaped the benefits of wired technology in the past several years in the form of mobile learning. The main objective of the research is to develop and propose a service quality model for m-learning in university environment. The study proposed a service quality model based on the Information System (IS) success model for universities m-learning in a developing country, Jordan. This study was carried out through three stages; first, a questionnaire was used to obtain the perception of m-learning services among 360 students studying in different colleges of the Jordan University of Science and Technology (JUST), and second information quality was measured through usefulness and adequacy, system quality (ease of use, accessibility and interactivity), and their causal relationship with learner's perceived service quality. Finally, the third stage involved the development of m-learning system prototype (MLSP) by using Rapid Application Development (RAD) technique.

Technology to Improve the Assessment of Learning

The methodologies of assessment are one of the indicators of quality in teaching, to the point that the idea me how you evaluate and I’ll tell you how you teach is very accepted. To that we can add, and with what technology? When it is proposed that learning experiences have served to think and reflect on what we learn, learn about ourselves, self-regulate our learning ... Technologies have many functions and possibilities to improve these processes of diagnostic assessment, summative and formative, customize teaching, communicating and reflecting on what has been learned, making feedback more interactive and instantaneous, more motivating activities, easier and faster to manage evaluation data, essential in e-learning, b-learning and m-learning models. At the same time, these digital resources may have problems that we must also attend to. In summary, technologies are essential resources for the evaluation of learning. These are questions from this work: What technologies can we develop a formative assessment with? What emerging technologies are there to assessment?

Predicting Actual Use of Mobile Technology in Learning: Towards E-Learning Culture Framework

With the advent of communication technology evolution, learning becomes flexible and accessible at any time and anywhere. Educational technology researchers have extensively integrated the Theory of Acceptance Model (TAM) and Theory of Planned Behavior to link the beliefs and actions in mobile learning. This study attempted to predict a hypothesized model of mobile learning culture via smart phone. In this present study, the m-learning culture is conceptualized from the factors of attitude, ethical use, technology competent; technology reliance and social well being. Social well being from the perspectives of The Adaptive Structuration has been integrated to underpin the study. 185 samples were drawn from the population of Korea University. Using self constructed questionnaire for the survey, the analyses involved descriptive and simultaneous Multiple Regression Analysis (MRA). The study was supported by qualitative design via interviews. The findings indicate all predictors are significant except for technology reliance and ethical use. Social well being is the highest predictor for the m-learning via smartphone. This study has been explored from both quantitative and qualitative research which provide important empirical information to support m-learning culture and its predictors. The findings have contributed to a model of m-learning which extends the literature and existing models of TAM and Theory of Planned Behavior.

Designing Math Trails for Enhanced by Mobile Learning Realistic Mathematics Education in Primary Education

Seeking a systematic combination of the pedagogical model of m-learning with the Realistic Mathematics Education (RME) approach, this study concerns the use of math trail as a learning activity model that can take the advantages of mobile computing devices for the design of effective learning experiences in an authentic context. The paper presents the design and the case study of the first pilot implementation of a math trail, using mobile devices for primary school students. In this math trail, the students are guided, through a digital map, to a sequence of preselected sites of a park where they solve specially designed math problems using data from the environmental context. The students measure real objects’ dimensions either with conventional instruments or by measurement applications of their tablet. According to the findings of the study, students solved the puzzles by applying mathematical knowledge, discussion and collaboration. The students applied and reinforced their knowledge through an effective and engaging learning activity. Moreover, the students were puzzled about the differences of the measurements by conventional and digital instruments and this confusion triggered social negotiation. Further research is needed for a grounded theory development about m-learning design for RME.

Cross-cultural Usability Issues in E/M-Learning

This paper gives an overview of electronic learning (E-Learning) and mobile learning (M-Learning) adoption and diffusion trends, as well as their particular traits, characteristics and issues, especially in terms of cross-cultural and universal usability. E-Learning and M-Learning models using web services and cloud computing, as well as associated security concerns are all addressed. The benefits and enhancements that accrue from using mobile and other internet devices for the purposes of learning in academia are discussed. The differences between traditional classroom-based learning, distance learning, E-Learning and M-Learning models are compared and some conclusions are drawn.

Location-based clustering and collaborative filtering for mobile learning

This paper presents a new m-learning model described as location-based collaborative M-learning (LCM). This new model exploits location information of mobile users and implements two main operations; k-means for clustering mobile users, and location-based collaborative filtering (CF) to provide learning items recommendations to clustered users. A comprehensive evaluation methodology was utilised to validate the proposed model in terms of complexity, performance and learning items' recommendations quality. Results have confirmed successful implementation of the proposed LCM model during different mobile users' settings. It was shown that LCM structure efficiently reduces processing overload and time required for users' clustering and learning content authoring, and improves learning items recommendations' accuracy.

Factors influencing mobile learning: a literature review of selected journal papers

The technological developments, reduction in prices of mobile gadgets and services, and the need for mobility has led to a major change in the learning environment. The situation has given rise to a novel method of learning, namely, mobile learning (m-learning). M-Learning helps in imparting educations taking into consideration the learner's needs irrespective of time and place limitations. M-learning has created a new RD facilitating conditions, effort-expectancy and social influence were found to be trivial. Unified Theory of Acceptance and Use of Technology (UTAUT) model was found to be the most optimal model for m-learning.